Package: a4 Version: 1.46.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats License: GPL-3 MD5sum: 4fe2823df78513c79777d009196856fd NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. biocViews: Microarray Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4 git_branch: RELEASE_3_16 git_last_commit: 1b8f130 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/a4_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/a4_1.46.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 84 Package: a4Base Version: 1.46.0 Depends: a4Preproc, a4Core Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter, limma, multtest, glmnet, gplots Suggests: Cairo, ALL, hgu95av2.db, nlcv Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 86bfcbf8ca4d02774d1057a304193571 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: RELEASE_3_16 git_last_commit: be70ae7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/a4Base_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Base_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Base_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/a4Base_1.46.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 suggestsMe: epimutacions dependencyCount: 75 Package: a4Classif Version: 1.46.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 4caa10169797e6ee42ca1a05671274a6 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, GeneExpression, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: RELEASE_3_16 git_last_commit: 9679418 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/a4Classif_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Classif_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Classif_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/a4Classif_1.46.0.tgz vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html vignetteTitles: a4Classif package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R dependsOnMe: a4 dependencyCount: 32 Package: a4Core Version: 1.46.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: aee4c7414982df8ade728081971c0923 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Utility functions for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: RELEASE_3_16 git_last_commit: 8999fe1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/a4Core_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Core_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Core_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/a4Core_1.46.0.tgz vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html vignetteTitles: a4Core package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R dependsOnMe: a4, a4Base, a4Classif, nlcv dependencyCount: 19 Package: a4Preproc Version: 1.46.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 31a1f22866a1faad83cbf64561520b11 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Preprocessing Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: RELEASE_3_16 git_last_commit: 8463958 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/a4Preproc_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Preproc_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Preproc_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/a4Preproc_1.46.0.tgz vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html vignetteTitles: a4Preproc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 6 Package: a4Reporting Version: 1.46.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 386cb5d0edb3fa983f262a9043ad760f NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Utility functions to facilitate the reporting of the Automated Affymetrix Array Analysis Reporting set of packages. biocViews: Microarray Author: Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: RELEASE_3_16 git_last_commit: 00b82d2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/a4Reporting_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/a4Reporting_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/a4Reporting_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/a4Reporting_1.46.0.tgz vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html vignetteTitles: a4Reporting package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R dependsOnMe: a4 dependencyCount: 4 Package: ABarray Version: 1.66.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: f841c4c885c64e9776bf0a6ea15cbb2a NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: RELEASE_3_16 git_last_commit: 9625fe2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ABarray_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ABarray_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ABarray_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ABarray_1.66.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: abseqR Version: 1.16.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: d464cec5cfa502841cf5080b3018e048 NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: RELEASE_3_16 git_last_commit: e9f5252 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/abseqR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/abseqR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/abseqR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/abseqR_1.16.0.tgz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 111 Package: ABSSeq Version: 1.52.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: aa240fabff9f675d35cc00883db80e12 NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: RELEASE_3_16 git_last_commit: 07038c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ABSSeq_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ABSSeq_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ABSSeq_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ABSSeq_1.52.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R importsMe: metaseqR2 dependencyCount: 9 Package: acde Version: 1.28.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: e56cb58b44f42acc784e183126551c5c NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: RELEASE_3_16 git_last_commit: 0edccca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/acde_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/acde_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/acde_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/acde_1.28.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R importsMe: coexnet dependencyCount: 3 Package: ACE Version: 1.16.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: c44aad495e4ae569b085f511a104b27b NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: RELEASE_3_16 git_last_commit: 44fa7a7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ACE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ACE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ACE_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ACE_1.16.0.tgz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 80 Package: aCGH Version: 1.76.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: x64 MD5sum: 4ed40e84cdfa7084127e10a09349ee6e NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: RELEASE_3_16 git_last_commit: c606852 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/aCGH_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/aCGH_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/aCGH_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/aCGH_1.76.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP dependencyCount: 16 Package: ACME Version: 2.54.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: x64 MD5sum: 349b793b503b19cfbfea1d60def06700 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: RELEASE_3_16 git_last_commit: 8d39d9d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ACME_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ACME_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ACME_2.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ACME_2.54.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 6 Package: ADaCGH2 Version: 2.38.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: x64 MD5sum: 48c49370d2dd3edb67ed26f7a4f8a5dd NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Code from ffbase by Edwin de Jonge , Jan Wijffels, Jan van der Laan. Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_16 git_last_commit: 7f98abd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ADaCGH2_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADaCGH2_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADaCGH2_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ADaCGH2_2.38.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 97 Package: ADAM Version: 1.14.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: d6aad59a5d8720a7190d99bdb0a7ee44 NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG, GeneExpression, Microarray Author: André Luiz Molan [aut], Giordano Bruno Sanches Seco [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: RELEASE_3_16 git_last_commit: dab2630 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ADAM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADAM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADAM_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ADAM_1.14.0.tgz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 92 Package: ADAMgui Version: 1.14.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: markdown, BiocStyle License: GPL (>= 2) MD5sum: 48cab0de50076fca376c39fe72c7ff4e NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco [aut], André Luiz Molan [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: RELEASE_3_16 git_last_commit: 0dfc260 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ADAMgui_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADAMgui_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADAMgui_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ADAMgui_1.14.0.tgz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 158 Package: adductomicsR Version: 1.14.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 MD5sum: 5321e7ca95c807f02221ea10b1958767 NeedsCompilation: no Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: RELEASE_3_16 git_last_commit: 90b4fbd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/adductomicsR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/adductomicsR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/adductomicsR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/adductomicsR_1.14.0.tgz vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html vignetteTitles: Adductomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R dependencyCount: 145 Package: ADImpute Version: 1.8.0 Depends: R (>= 4.0) Imports: checkmate, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 419c835ce2fc0487c405f8969c72493b NeedsCompilation: no Title: Adaptive Dropout Imputer (ADImpute) Description: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble. biocViews: GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Transcriptomics Author: Ana Carolina Leote [cre, aut] () Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: RELEASE_3_16 git_last_commit: a3696a0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ADImpute_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ADImpute_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ADImpute_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ADImpute_1.8.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 54 Package: adSplit Version: 1.68.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGGREST (>= 1.30.1), multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: x64 MD5sum: b43ed90820ddc28dbc1d10873d3040ba NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: RELEASE_3_16 git_last_commit: 705977b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/adSplit_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/adSplit_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/adSplit_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/adSplit_1.68.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 55 Package: AffiXcan Version: 1.16.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: d65b4c5c4c8af5cb1e07cad498996bb5 NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: RELEASE_3_16 git_last_commit: 13cd956 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AffiXcan_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AffiXcan_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AffiXcan_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AffiXcan_1.16.0.tgz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 58 Package: affxparser Version: 1.70.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: x64 MD5sum: 9898bbc5e7122f87d009435c84c4ba74 NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: RELEASE_3_16 git_last_commit: d2779bf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affxparser_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affxparser_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affxparser_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affxparser_1.70.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, crossmeta, EventPointer, GCSscore, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.76.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: x64 MD5sum: 53cbffc6730bbac4cd93bbd70fb87414 NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affy git_branch: RELEASE_3_16 git_last_commit: 3bb3093 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affy_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affy_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affy_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affy_1.76.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, affyPLM, AffyRNADegradation, altcdfenvs, arrayMvout, bgx, Cormotif, DrugVsDisease, ExiMiR, farms, frmaTools, gcrma, logitT, maskBAD, panp, prebs, qpcrNorm, RefPlus, Risa, RPA, SCAN.UPC, sscore, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, ccTutorial, CLL, curatedBladderData, curatedOvarianData, ecoliLeucine, Hiiragi2013, MAQCsubset, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, arrayQualityMetrics, bnem, CAFE, ChIPXpress, coexnet, Cormotif, crossmeta, Doscheda, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred, IsoGene, seeker suggestsMe: AnnotationForge, ArrayExpress, autonomics, beadarray, beadarraySNP, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, isatabr, maGUI dependencyCount: 11 Package: affycomp Version: 1.74.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 6d69d91c904478573b35eaeb6b536db7 NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affycomp git_branch: RELEASE_3_16 git_last_commit: 1160d63 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affycomp_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affycomp_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affycomp_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affycomp_1.74.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependsOnMe: affycompData dependencyCount: 6 Package: AffyCompatible Version: 1.58.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: ba2f2f8dd8d5a678dc41d5fa86b322f8 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/AffyCompatible git_branch: RELEASE_3_16 git_last_commit: 6508e72 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AffyCompatible_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AffyCompatible_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AffyCompatible_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AffyCompatible_1.58.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R dependencyCount: 19 Package: affyContam Version: 1.56.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: 6ac646aaf6051f868159a44820bdb5a9 NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: RELEASE_3_16 git_last_commit: e2b8a4f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affyContam_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyContam_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyContam_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affyContam_1.56.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R importsMe: arrayMvout dependencyCount: 14 Package: affycoretools Version: 1.70.0 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: 1e54847013ece74e0191aecd71e3a85e NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: RELEASE_3_16 git_last_commit: f09a788 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affycoretools_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affycoretools_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affycoretools_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affycoretools_1.70.0.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R dependencyCount: 192 Package: affyILM Version: 1.50.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: 1509b9ef9078f852665a022115b91018 NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: RELEASE_3_16 git_last_commit: 185cd8e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affyILM_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyILM_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyILM_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affyILM_1.50.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 26 Package: affyio Version: 1.68.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: x64 MD5sum: 822b846100c156382ac0ae7ebd594878 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: RELEASE_3_16 git_last_commit: 33080c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affyio_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyio_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyio_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affyio_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 2 Package: affylmGUI Version: 1.72.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: ecca89124f55e0c161945ad32032df73 NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: RELEASE_3_16 git_last_commit: eb98eb8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/affylmGUI_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/affylmGUI_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affylmGUI_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affylmGUI_1.72.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 58 Package: affyPLM Version: 1.74.2 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: x64 MD5sum: 3bd06614e7ef1830218daf39c3bdc276 NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: RELEASE_3_16 git_last_commit: 429ed7b git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/affyPLM_1.74.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/affyPLM_1.73.0.zip mac.binary.ver: bin/macosx/contrib/4.2/affyPLM_1.74.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/affyPLM_1.74.2.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: RefPlus, bapred importsMe: affylmGUI, arrayQualityMetrics, mimager suggestsMe: arrayMvout, BiocGenerics, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 25 Package: AffyRNADegradation Version: 1.44.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: 0a1d2444de49c7558c377344a93490a3 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: RELEASE_3_16 git_last_commit: 63881f4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AffyRNADegradation_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AffyRNADegradation_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AffyRNADegradation_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AffyRNADegradation_1.44.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 12 Package: AGDEX Version: 1.46.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 394c9e162d25b5506048cc9eb071a49e NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: RELEASE_3_16 git_last_commit: d7c38e8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AGDEX_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AGDEX_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AGDEX_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AGDEX_1.46.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 51 Package: aggregateBioVar Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, methods, S4Vectors, SummarizedExperiment, SingleCellExperiment, Matrix, tibble, rlang Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics, DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext, RColorBrewer, pheatmap, viridis License: GPL-3 MD5sum: 42b7169cc0f2bd5dc0d8e25bfde7ae6c NeedsCompilation: no Title: Differential Gene Expression Analysis for Multi-subject scRNA-seq Description: For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools. biocViews: Software, SingleCell, RNASeq, Transcriptomics, Transcription, GeneExpression, DifferentialExpression Author: Jason Ratcliff [aut, cre] (), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff URL: https://github.com/jasonratcliff/aggregateBioVar VignetteBuilder: knitr BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues git_url: https://git.bioconductor.org/packages/aggregateBioVar git_branch: RELEASE_3_16 git_last_commit: 14e66de git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/aggregateBioVar_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/aggregateBioVar_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/aggregateBioVar_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/aggregateBioVar_1.8.0.tgz vignettes: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html vignetteTitles: Multi-subject scRNA-seq Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R dependencyCount: 37 Package: agilp Version: 3.30.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: b1cbcf2d05504173f46361abad2d9c2b NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: RELEASE_3_16 git_last_commit: a2c898d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/agilp_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/agilp_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/agilp_3.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/agilp_3.30.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AgiMicroRna Version: 2.48.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: 2e41a1337e2cab4d4e7b07bed6eef408 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: RELEASE_3_16 git_last_commit: 4c163b1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AgiMicroRna_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AgiMicroRna_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AgiMicroRna_2.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AgiMicroRna_2.48.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R dependencyCount: 193 Package: AIMS Version: 1.30.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 1d11f0658be529942cf9ecb580295308 NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: RELEASE_3_16 git_last_commit: 2ab6115 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AIMS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AIMS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AIMS_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AIMS_1.30.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 11 Package: airpart Version: 1.6.0 Depends: R (>= 4.1) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, scater, stats, smurf, apeglm (>= 1.13.3), emdbook, mclust, clue, dynamicTreeCut, matrixStats, dplyr, plyr, ggplot2, ComplexHeatmap, forestplot, RColorBrewer, rlang, lpSolve, grid, grDevices, graphics, utils, pbapply Suggests: knitr, rmarkdown, roxygen2 (>= 6.0.0), testthat (>= 3.0.0), gplots, tidyr License: GPL-2 MD5sum: 47bc2fead61ca20f0852ef3c27b9d0fe NeedsCompilation: no Title: Differential cell-type-specific allelic imbalance Description: Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets. biocViews: SingleCell, RNASeq, ATACSeq, ChIPSeq, Sequencing, GeneRegulation, GeneExpression, Transcription, TranscriptomeVariant, CellBiology, FunctionalGenomics, DifferentialExpression, GraphAndNetwork, Regression, Clustering, QualityControl Author: Wancen Mu [aut, cre] (), Michael Love [aut, ctb] () Maintainer: Wancen Mu URL: https://github.com/Wancen/airpart VignetteBuilder: knitr BugReports: https://github.com/Wancen/airpart/issues git_url: https://git.bioconductor.org/packages/airpart git_branch: RELEASE_3_16 git_last_commit: 99d4cc8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/airpart_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/airpart_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/airpart_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/airpart_1.6.0.tgz vignettes: vignettes/airpart/inst/doc/airpart.html vignetteTitles: Differential allelic imbalance with airpart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/airpart/inst/doc/airpart.R dependencyCount: 135 Package: ALDEx2 Version: 1.30.0 Depends: methods, stats, zCompositions, Imports: Rfast, BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: 176a430e5a6cf5da2530c59ee4f69fbf NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report p-values and Benjamini-Hochberg corrected p-values. ALDEx2 also calculates expected standardized effect sizes for paired or unpaired study designs. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: RELEASE_3_16 git_last_commit: cb66705 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ALDEx2_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ALDEx2_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ALDEx2_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ALDEx2_1.30.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.html vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R dependsOnMe: omicplotR importsMe: benchdamic, microbiomeMarker, aIc, ggpicrust2 dependencyCount: 47 Package: alevinQC Version: 1.14.0 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2, GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang, Rcpp LinkingTo: Rcpp Suggests: knitr, BiocStyle, testthat (>= 3.0.0), BiocManager License: MIT + file LICENSE Archs: x64 MD5sum: 729a1c1fe011e289cfc10b91f1ecf27b NeedsCompilation: yes Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (), Avi Srivastava [aut], Rob Patro [aut], Dongze He [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_16 git_last_commit: 5e726f6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/alevinQC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/alevinQC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/alevinQC_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/alevinQC_1.14.0.tgz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 90 Package: AllelicImbalance Version: 1.36.0 Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: ad27098a91fbd7d356d46cd43d7a2af0 NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: RELEASE_3_16 git_last_commit: cb4910c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AllelicImbalance_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AllelicImbalance_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AllelicImbalance_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AllelicImbalance_1.36.0.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 159 Package: AlphaBeta Version: 1.12.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>= 1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>= 3.6), plotly (>= 4.9) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 2822aaf9b95e6ef87dbf7f28931009d1 NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: RELEASE_3_16 git_last_commit: 87dba7e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AlphaBeta_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AlphaBeta_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AlphaBeta_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AlphaBeta_1.12.0.tgz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 93 Package: alpine Version: 1.24.0 Depends: R (>= 3.5.0) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, markdown, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 5e808fea76aa64d1b4188fadbd2055e0 NeedsCompilation: no Title: alpine Description: Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, GeneExpression, Transcription, Coverage, BatchEffect, Normalization, Visualization, QualityControl Author: Michael Love, Rafael Irizarry Maintainer: Michael Love VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alpine git_branch: RELEASE_3_16 git_last_commit: 7e734d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/alpine_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/alpine_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/alpine_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/alpine_1.24.0.tgz vignettes: vignettes/alpine/inst/doc/alpine.html vignetteTitles: alpine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R dependencyCount: 100 Package: AlpsNMR Version: 4.0.4 Depends: R (>= 4.2), future (>= 1.10.0) Imports: utils, generics, graphics, stats, grDevices, cli, magrittr (>= 1.5), dplyr (>= 1.1.0), signal (>= 0.7-6), rlang (>= 0.3.0.1), scales (>= 1.2.0), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), tidyselect, readxl (>= 1.1.0), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), mixOmics (>= 6.3.2), matrixStats (>= 0.54.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), pcaPP (>= 1.9-73), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), vctrs (>= 0.3.0), BiocParallel Suggests: BiocStyle, ChemoSpec, cowplot, curl, DT (>= 0.5), GGally (>= 1.4.0), ggrepel (>= 0.8.0), gridExtra, knitr, plotly (>= 4.7.1), progressr, SummarizedExperiment, S4Vectors, testthat (>= 2.0.0), writexl (>= 1.0), zip (>= 2.0.4) License: MIT + file LICENSE MD5sum: 5afcd910f5242bde6a3e2f92bcb60600 NeedsCompilation: no Title: Automated spectraL Processing System for NMR Description: Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available. biocViews: Software, Preprocessing, Visualization, Classification, Cheminformatics, Metabolomics, DataImport Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut, cre] (), Francisco Madrid Gambin [aut] (), Luis Fernandez [aut] (), Laura López Sánchez [ctb], Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph] Maintainer: Sergio Oller Moreno URL: https://sipss.github.io/AlpsNMR/, https://github.com/sipss/AlpsNMR VignetteBuilder: knitr BugReports: https://github.com/sipss/AlpsNMR/issues git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: RELEASE_3_16 git_last_commit: 4766d78 git_last_commit_date: 2023-02-16 Date/Publication: 2023-02-16 source.ver: src/contrib/AlpsNMR_4.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/AlpsNMR_4.0.4.zip mac.binary.ver: bin/macosx/contrib/4.2/AlpsNMR_4.0.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AlpsNMR_4.0.2.tgz vignettes: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.pdf, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.pdf, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.pdf vignetteTitles: Vignette 01: Introduction to AlpsNMR (start here), Older Introduction to AlpsNMR (soft-deprecated API), Vignette 02: Handling metadata and annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/Vig01-introduction-to-alpsnmr.R, vignettes/AlpsNMR/inst/doc/Vig01b-introduction-to-alpsnmr-old-api.R, vignettes/AlpsNMR/inst/doc/Vig02-handling-metadata-and-annotations.R dependencyCount: 132 Package: altcdfenvs Version: 2.60.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 314a03730c8948d9da6c351f356135fc NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: RELEASE_3_16 git_last_commit: 0bc0b44 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/altcdfenvs_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/altcdfenvs_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/altcdfenvs_2.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/altcdfenvs_2.60.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight dependencyCount: 26 Package: AMARETTO Version: 1.14.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr License: Apache License (== 2.0) + file LICENSE MD5sum: aa87962f7524e66e90c8e41da55d28c5 NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: RELEASE_3_16 git_last_commit: 3063cca git_last_commit_date: 2022-11-01 Date/Publication: 2023-03-20 source.ver: src/contrib/AMARETTO_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AMARETTO_1.13.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AMARETTO_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AMARETTO_1.14.0.tgz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 156 Package: AMOUNTAIN Version: 1.24.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: dc945636cf4935aecde8f0a309be8ada NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: RELEASE_3_16 git_last_commit: 250160e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AMOUNTAIN_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AMOUNTAIN_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AMOUNTAIN_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AMOUNTAIN_1.24.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: amplican Version: 1.20.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), data.table (>= 1.10.4-3) Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4), GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 2.2.0), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), clusterCrit (>= 1.2.7) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 Archs: x64 MD5sum: b8bc391abea8de9e3a3659ff29374379 NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: RELEASE_3_16 git_last_commit: eafd080 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/amplican_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/amplican_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/amplican_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/amplican_1.20.0.tgz vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html, vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican FAQ, amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R dependencyCount: 112 Package: Anaquin Version: 2.22.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 3918e7fad4663db676e6df169bed2ba5 NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: RELEASE_3_16 git_last_commit: d848a9b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Anaquin_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Anaquin_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Anaquin_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Anaquin_2.22.0.tgz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 106 Package: ANCOMBC Version: 2.0.2 Depends: R (>= 4.2.0) Imports: mia, stats, CVXR, DescTools, Hmisc, MASS, Rdpack, S4Vectors, SingleCellExperiment, SummarizedExperiment, TreeSummarizedExperiment, doParallel, doRNG, dplyr, emmeans, energy, foreach, lme4, lmerTest, magrittr, nloptr, parallel, rlang, rngtools, tibble, tidyr, utils Suggests: knitr, rmarkdown, testthat, DT, caret, microbiome, tidyverse License: Artistic-2.0 MD5sum: da2398479411b5dfd6ee8ade6908b19d NeedsCompilation: no Title: Microbiome differential abudance and correlation analyses with bias correction Description: ANCOMBC is a package containing differential abundance (DA) and correlation analyses for microbiome data. Specifically, the package includes Analysis of Compositions of Microbiomes with Bias Correction 2 (ANCOM-BC2), Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), and Analysis of Composition of Microbiomes (ANCOM) for DA analysis, and Sparse Estimation of Correlations among Microbiomes (SECOM) for correlation analysis. Microbiome data are typically subject to two sources of biases: unequal sampling fractions (sample-specific biases) and differential sequencing efficiencies (taxon-specific biases). Methodologies included in the ANCOMBC package are designed to correct these biases and construct statistically consistent estimators. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] () Maintainer: Huang Lin URL: https://github.com/FrederickHuangLin/ANCOMBC VignetteBuilder: knitr BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues git_url: https://git.bioconductor.org/packages/ANCOMBC git_branch: RELEASE_3_16 git_last_commit: ebcbaeb git_last_commit_date: 2022-12-17 Date/Publication: 2022-12-18 source.ver: src/contrib/ANCOMBC_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ANCOMBC_2.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/ANCOMBC_2.0.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ANCOMBC_2.0.3.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOM.html, vignettes/ANCOMBC/inst/doc/ANCOMBC.html, vignettes/ANCOMBC/inst/doc/ANCOMBC2.html, vignettes/ANCOMBC/inst/doc/SECOM.html vignetteTitles: ANCOM, ANCOMBC, ANCOMBC2, SECOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOM.R, vignettes/ANCOMBC/inst/doc/ANCOMBC.R, vignettes/ANCOMBC/inst/doc/ANCOMBC2.R, vignettes/ANCOMBC/inst/doc/SECOM.R importsMe: benchdamic, microbiomeMarker dependencyCount: 219 Package: AneuFinder Version: 1.26.0 Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings, GenomicAlignments, reshape2, ggdendro, ggrepel, ReorderCluster, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: x64 MD5sum: ad1d7408f9f3558fadbf40f18fae9bbd NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data. biocViews: ImmunoOncology, Software, Sequencing, SingleCell, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AneuFinder git_branch: RELEASE_3_16 git_last_commit: 7cd59a1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AneuFinder_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AneuFinder_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AneuFinder_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AneuFinder_1.26.0.tgz vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf vignetteTitles: A quick introduction to AneuFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R dependencyCount: 84 Package: ANF Version: 1.20.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: a67284ce5d09605d7752ca1386617428 NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: RELEASE_3_16 git_last_commit: 91157e7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ANF_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ANF_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ANF_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ANF_1.20.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R suggestsMe: HarmonizedTCGAData dependencyCount: 19 Package: animalcules Version: 1.14.0 Depends: R (>= 4.0.0) Imports: assertthat, shiny, shinyjs, DESeq2, caret, plotly, ggplot2, rentrez, reshape2, covr, ape, vegan, dplyr, magrittr, MultiAssayExperiment, SummarizedExperiment, S4Vectors (>= 0.23.19), XML, forcats, scales, lattice, glmnet, tsne, plotROC, DT, utils, limma, methods, stats, tibble, biomformat, umap, Matrix, GUniFrac Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 9350e28f5ac8d04f6109c49c5fe1eee3 NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: Yue Zhao [aut, cre] (), Anthony Federico [aut] (), W. Evan Johnson [aut] () Maintainer: Yue Zhao URL: https://github.com/compbiomed/animalcules VignetteBuilder: knitr BugReports: https://github.com/compbiomed/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: RELEASE_3_16 git_last_commit: bac5a33 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/animalcules_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/animalcules_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/animalcules_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/animalcules_1.14.0.tgz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R dependencyCount: 204 Package: annaffy Version: 1.70.0 Depends: R (>= 2.5.0), methods, Biobase, BiocManager, GO.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 9f5252619c2fb8cf2ca23b705af46aab NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: RELEASE_3_16 git_last_commit: c99e812 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/annaffy_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annaffy_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annaffy_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/annaffy_1.70.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: webbioc importsMe: a4Base suggestsMe: metaMA dependencyCount: 48 Package: annmap Version: 1.40.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: a2475106a6afb06daff638379e97d869 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. The annmap database can be downloaded from: https://figshare.manchester.ac.uk/account/articles/16685071 biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: https://github.com/cruk-mi/annmap git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_16 git_last_commit: 02e02b1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/annmap_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/annmap_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/annmap_1.39.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 69 Package: annotate Version: 1.76.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), httr Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: 8e3f53138dee78c278c5181d14579ef3 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/annotate git_branch: RELEASE_3_16 git_last_commit: 0181d5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/annotate_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annotate_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annotate_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/annotate_1.76.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, phenoTest, PREDA, sampleClassifier, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, genefilter, GlobalAncova, globaltest, GOstats, lumi, methylumi, MGFR, phenoTest, qpgraph, tigre, UMI4Cats, easyDifferentialGeneCoexpression, geneExpressionFromGEO, GOxploreR suggestsMe: BiocGenerics, GenomicRanges, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pageRank, pcxn, PhosR, RnBeads, siggenes, SummarizedExperiment, systemPipeR, adme16cod.db, ag.db, ath1121501.db, bovine.db, 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ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, limorhyde, maGUI, MOSS dependencyCount: 48 Package: AnnotationDbi Version: 1.60.2 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25), stats, KEGGREST Suggests: hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: f4a6607e3a70381e5daa861e60a4c0fe NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationDbi VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik BugReports: https://github.com/Bioconductor/AnnotationDbi/issues git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: RELEASE_3_16 git_last_commit: eebebb2 git_last_commit_date: 2023-03-09 Date/Publication: 2023-03-10 source.ver: src/contrib/AnnotationDbi_1.60.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationDbi_1.60.2.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationDbi_1.60.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnnotationDbi_1.60.2.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. 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hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmprobe, htmg430a.db, htmg430aprobe, htmg430b.db, htmg430bprobe, htmg430pm.db, htmg430pmprobe, htrat230pm.db, htrat230pmprobe, htratfocus.db, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, Orthology.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, soybeanprobe, sugarcaneprobe, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, tinesath1probe, rnaseqGene, convertid importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, beadarray, bioCancer, BiocSet, biomaRt, BioNAR, BioNet, biovizBase, bumphunter, BUSpaRse, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, Cogito, compEpiTools, conclus, consensusDE, crisprDesign, crisprseekplus, CrispRVariants, crossmeta, cTRAP, debrowser, derfinder, DominoEffect, DOSE, EasyCellType, EDASeq, eegc, EnrichmentBrowser, ensembldb, EpiMix, epimutacions, erma, esATAC, FRASER, GA4GHshiny, gage, genefilter, geneplotter, GeneTonic, geneXtendeR, GenomicInteractionNodes, GenVisR, ggbio, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS, interactiveDisplay, IRISFGM, isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust, MeSHDbi, meshes, MesKit, MetaboSignal, methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, MLP, MSnID, multiGSEA, multiMiR, NanoMethViz, NanoStringQCPro, nanotatoR, netOmics, NetSAM, netZooR, ORFik, Organism.dplyr, PADOG, pathview, pcaExplorer, phantasus, phenoTest, proActiv, psichomics, pwOmics, qpgraph, QuasR, ReactomePA, REDseq, regutools, restfulSE, rGREAT, rgsepd, ribosomeProfilingQC, RNAAgeCalc, rrvgo, rTRM, SBGNview, scanMiRApp, scPipe, scruff, scTensor, SGSeq, signatureSearch, signifinder, simplifyEnrichment, SMITE, SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore, TFutils, tigre, trackViewer, trena, TRESS, tricycle, txcutr, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovinecdf, bsubtiliscdf, canine.db, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chickencdf, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, GGHumanMethCancerPanelv1.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, HDO.db, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133plusa.db, hthgu133plusb.db, hthgu133pluspm.db, hthgu133pluspmcdf, htmg430a.db, htmg430acdf, htmg430b.db, htmg430bcdf, htmg430pm.db, htmg430pmcdf, htrat230pm.db, htrat230pmcdf, htratfocus.db, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, msigdb, scRNAseq, ExpHunterSuite, aliases2entrez, BiSEp, CAMML, DIscBIO, jetset, Mega2R, MetaIntegrator, MOCHA, netgsa, pathfindR, prioGene, pulseTD, RobLoxBioC, seeker, WGCNA suggestsMe: APAlyzer, ASURAT, autonomics, bambu, BiocGenerics, BiocOncoTK, CellTrails, cicero, cola, csaw, DAPAR, DEGreport, edgeR, eisaR, enrichplot, esetVis, FELLA, FGNet, fgsea, fishpond, GA4GHclient, gCrisprTools, GeneRegionScan, GenomicRanges, iSEEu, limma, MutationalPatterns, NetActivity, oligo, ontoProc, OUTRIDER, piano, Pigengene, plotgardener, pRoloc, ProteoDisco, quantiseqr, R3CPET, recount, RLSeq, sigPathway, sparrow, SummarizedExperiment, systemPipeR, tidybulk, topconfects, weitrix, wiggleplotr, BioPlex, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, bulkAnalyseR, CALANGO, conos, DGCA, dnapath, easylabel, genekitr, MARVEL, pagoda2, Platypus, rliger, scITD, SCpubr, SourceSet dependencyCount: 45 Package: AnnotationFilter Version: 1.22.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 479cc3a2941b020e86b344ee1fc49597 NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: RELEASE_3_16 git_last_commit: c9fea4a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AnnotationFilter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationFilter_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationFilter_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnnotationFilter_1.22.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, CompoundDb, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, drugTargetInteractions, ggbio, proteasy, QFeatures, RITAN, scanMiRApp, TVTB, GenomicDistributionsData, RNAseqQC, utr.annotation suggestsMe: dasper, GenomicDistributions, GenomicFeatures, TFutils, wiggleplotr dependencyCount: 17 Package: AnnotationForge Version: 1.40.2 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, GO.db, markdown, BiocStyle, knitr, BiocManager, BiocFileCache License: Artistic-2.0 MD5sum: ffe79c46d29549916e65ca5813567d28 NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationForge/issues git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: RELEASE_3_16 git_last_commit: 17653c7 git_last_commit_date: 2023-03-25 Date/Publication: 2023-03-27 source.ver: src/contrib/AnnotationForge_1.40.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationForge_1.40.2.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationForge_1.40.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnnotationForge_1.40.2.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats, ViSEAGO, GGHumanMethCancerPanelv1.db suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 47 Package: AnnotationHub Version: 3.6.0 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr, yaml, dplyr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown, HubPub Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 0cf4c3b511b29d9375eed93381239b76 NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: RELEASE_3_16 git_last_commit: 3315a73 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AnnotationHub_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationHub_3.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationHub_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnnotationHub_3.6.0.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, LRcell, octad, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phyloP35way.UCSC.mm39, rGenomeTracksData, synaptome.data, UCSCRepeatMasker, MetaGxBreast, MetaGxOvarian, NestLink, sesameData, tartare, annotation, sequencing, OSCA.advanced, OSCA.basic, OSCA.workflows, SingleRBook importsMe: annotatr, atena, circRNAprofiler, coMethDMR, cTRAP, customCMPdb, dmrseq, EpiCompare, EpiMix, epimutacions, GenomicScores, GSEABenchmarkeR, gwascat, iSEEhub, MACSr, meshes, MethReg, MSnID, NxtIRFcore, OGRE, ontoProc, psichomics, pwOmics, regutools, REMP, restfulSE, RLSeq, scanMiRApp, scAnnotatR, scmeth, scTensor, singleCellTK, SpliceWiz, tximeta, Ularcirc, AHLRBaseDbs, AHMeSHDbs, AHPathbankDbs, AHPubMedDbs, AHWikipathwaysDbs, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, grasp2db, metaboliteIDmapping, synaptome.db, adductData, alpineData, BioImageDbs, biscuiteerData, celldex, chipseqDBData, crisprScoreData, curatedMetagenomicData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, easierData, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GenomicDistributionsData, HCAData, HiContactsData, HMP16SData, HMP2Data, mcsurvdata, MerfishData, MetaGxPancreas, msigdb, RLHub, scpdata, scRNAseq, SFEData, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, RNAseqQC, utr.annotation suggestsMe: BgeeCall, Chicago, ChIPpeakAnno, CINdex, clusterProfiler, CNVRanger, COCOA, crisprViz, DNAshapeR, dupRadar, ELMER, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, factR, GenomicRanges, Glimma, GOSemSim, maser, MetaboAnnotation, MIRA, MSnbase, multicrispr, nullranges, OrganismDbi, plotgardener, recountmethylation, satuRn, TCGAbiolinks, TCGAutils, VariantAnnotation, xcore, AHEnsDbs, CTCF, ENCODExplorerData, excluderanges, gwascatData, ontoProcData, BioPlex, HarmonizedTCGAData dependencyCount: 94 Package: AnnotationHubData Version: 1.28.0 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, BiocCheck, graph, AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 426aaaf49408beb78eea1f1d09428fbd NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: RELEASE_3_16 git_last_commit: 37f43f7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AnnotationHubData_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnnotationHubData_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnnotationHubData_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnnotationHubData_1.28.0.tgz vignettes: vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData importsMe: AHEnsDbs, EuPathDB suggestsMe: HubPub, GenomicState dependencyCount: 134 Package: annotationTools Version: 1.72.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: cb3cd53a81f8ebc598cb5e95cf41c3a3 NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: RELEASE_3_16 git_last_commit: 62cafbb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/annotationTools_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annotationTools_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annotationTools_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/annotationTools_1.72.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R dependencyCount: 6 Package: annotatr Version: 1.24.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>= 0.23.10), stats, utils Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene License: GPL-3 MD5sum: 7baf3ac4f6a76c7c09caff7fef435d08 NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: RELEASE_3_16 git_last_commit: 84619b5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/annotatr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/annotatr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/annotatr_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/annotatr_1.24.0.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: annotatr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth suggestsMe: borealis, ramr dependencyCount: 148 Package: anota Version: 1.46.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: 3d3e04a8fb10434fb98e3026a5cb4943 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: RELEASE_3_16 git_last_commit: 9a0deb2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/anota_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/anota_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/anota_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/anota_1.46.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 47 Package: anota2seq Version: 1.20.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: f9e143aea5e03df220d16c65e5ecb984 NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Julie Lorent VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: RELEASE_3_16 git_last_commit: ae46d48 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/anota2seq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/anota2seq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/anota2seq_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/anota2seq_1.20.0.tgz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R dependencyCount: 100 Package: antiProfiles Version: 1.38.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: cc00a140351e3f2dfaa0a5ea88f9eb84 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: RELEASE_3_16 git_last_commit: c3d409f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/antiProfiles_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/antiProfiles_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/antiProfiles_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/antiProfiles_1.38.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: AnVIL Version: 1.10.2 Depends: R (>= 3.6), dplyr Imports: stats, utils, methods, futile.logger, jsonlite, httr, rapiclient (>= 0.1.3), tibble, tidyselect, tidyr, rlang, shiny, DT, miniUI, htmltools, BiocManager Suggests: parallel, knitr, rmarkdown, testthat, withr, readr, BiocStyle, devtools License: Artistic-2.0 MD5sum: 50b0905bc683515b13c32996f0ccdede NeedsCompilation: no Title: Bioconductor on the AnVIL compute environment Description: The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides end-user and developer functionality. For the end-user, AnVIL provides fast binary package installation, utitlities for working with Terra / AnVIL table and data resources, and convenient functions for file movement to and from Google cloud storage. For developers, AnVIL provides programatic access to the Terra, Leonardo, Rawls, Dockstore, and Gen3 RESTful programming interface, including helper functions to transform JSON responses to formats more amenable to manipulation in R. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (), Kayla Interdonato [aut], Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Marcel Ramos [ctb], Sehyun Oh [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: RELEASE_3_16 git_last_commit: 38838f0 git_last_commit_date: 2023-03-23 Date/Publication: 2023-03-24 source.ver: src/contrib/AnVIL_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnVIL_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/AnVIL_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnVIL_1.10.2.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html, vignettes/AnVIL/inst/doc/RunningWorkflow.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package, Running an AnVIL workflow within R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R, vignettes/AnVIL/inst/doc/RunningWorkflow.R dependsOnMe: cBioPortalData, terraTCGAdata importsMe: AnVILPublish dependencyCount: 74 Package: AnVILBilling Version: 1.8.0 Depends: R (>= 4.1) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: e7db4f23c6f10a0aa48289d99d4700d0 NeedsCompilation: no Title: Provide functions to retrieve and report on usage expenses in NHGRI AnVIL (anvilproject.org). Description: AnVILBilling helps monitor AnVIL-related costs in R, using queries to a BigQuery table to which costs are exported daily. Functions are defined to help categorize tasks and associated expenditures, and to visualize and explore expense profiles over time. This package will be expanded to help users estimate costs for specific task sets. biocViews: Infrastructure, Software Author: BJ Stubbs [aut], Vince Carey [aut, cre] Maintainer: Vince Carey VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: RELEASE_3_16 git_last_commit: efdc1c8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AnVILBilling_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnVILBilling_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnVILBilling_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnVILBilling_1.8.0.tgz vignettes: vignettes/AnVILBilling/inst/doc/billing.html vignetteTitles: Software for reckoning AnVIL/terra usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBilling/inst/doc/billing.R dependencyCount: 100 Package: AnVILPublish Version: 1.8.0 Imports: AnVIL, httr, jsonlite, rmarkdown, yaml, readr, whisker, tools, utils, stats Suggests: knitr, BiocStyle, BiocManager, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 73c4909afcf29b2ac9d08a22dc932632 NeedsCompilation: no Title: Publish Packages and Other Resources to AnVIL Workspaces Description: Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package DESCRIPTION file and from vignette YAML headings) are used to populate the 'DASHBOARD'. Vignettes are translated to python notebooks ready for evaluation in AnVIL. biocViews: Infrastructure, Software Author: Martin Morgan [aut, cre] (), Kayla Interdonato [aut], Vincent Carey [ctb] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: RELEASE_3_16 git_last_commit: 4f47cca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AnVILPublish_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AnVILPublish_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AnVILPublish_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AnVILPublish_1.8.0.tgz vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html vignetteTitles: Publishing R / Bioconductor packages to AnVIL Workspaces hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R dependencyCount: 85 Package: APAlyzer Version: 1.12.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq2, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, VariantAnnotation, dplyr, tidyr, repmis, Rsamtools, HybridMTest Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, testthat, pasillaBamSubset License: LGPL-3 MD5sum: 92d468e84e770089d6d301929a191bec NeedsCompilation: no Title: A toolkit for APA analysis using RNA-seq data Description: Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, GeneRegulation, Annotation, DataImport, Software Author: Ruijia Wang [cre, aut] (), Bin Tian [aut], Wei-Chun Chen [aut] Maintainer: Ruijia Wang URL: https://github.com/RJWANGbioinfo/APAlyzer/ VignetteBuilder: knitr BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues git_url: https://git.bioconductor.org/packages/APAlyzer git_branch: RELEASE_3_16 git_last_commit: 46cf23c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/APAlyzer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/APAlyzer_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/APAlyzer_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/APAlyzer_1.12.0.tgz vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R dependencyCount: 131 Package: apComplex Version: 2.64.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: d8fcbf77040efe7bc5dee5bd1e303c57 NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: RELEASE_3_16 git_last_commit: d2667d0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/apComplex_2.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/apComplex_2.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/apComplex_2.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/apComplex_2.64.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependencyCount: 52 Package: apeglm Version: 1.20.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: ff2b45cd3e43bfe8f6a4184a1d5ba349 NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: RELEASE_3_16 git_last_commit: 213e75c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/apeglm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/apeglm_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/apeglm_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/apeglm_1.20.0.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R dependsOnMe: rnaseqGene importsMe: airpart, debrowser, DiffBind, TEKRABber suggestsMe: bambu, BRGenomics, DESeq2, fishpond, NanoporeRNASeq, RNAseqQC dependencyCount: 36 Package: APL Version: 1.2.0 Depends: R (>= 4.2) Imports: reticulate, ggrepel, ggplot2, viridisLite, plotly, Seurat, SingleCellExperiment, magrittr, SummarizedExperiment, topGO, methods, stats, utils, org.Hs.eg.db, org.Mm.eg.db, rlang Suggests: BiocStyle, knitr, rmarkdown, scRNAseq, scater, scran, testthat License: GPL (>= 3) MD5sum: 642b14abaf442e3724c459a5eebc2956 NeedsCompilation: no Title: Association Plots Description: APL is a package developed for computation of Association Plots (AP), a method for visualization and analysis of single cell transcriptomics data. The main focus of APL is the identification of genes characteristic for individual clusters of cells from input data. The package performs correspondence analysis (CA) and allows to identify cluster-specific genes using Association Plots. Additionally, APL computes the cluster-specificity scores for all genes which allows to rank the genes by their specificity for a selected cell cluster of interest. biocViews: StatisticalMethod, DimensionReduction, SingleCell, Sequencing, RNASeq, GeneExpression Author: Elzbieta Gralinska [cre, aut], Clemens Kohl [aut], Martin Vingron [aut] Maintainer: Elzbieta Gralinska SystemRequirements: python, pytorch, numpy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/APL git_branch: RELEASE_3_16 git_last_commit: 32354e4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/APL_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/APL_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/APL_1.2.0.tgz vignettes: vignettes/APL/inst/doc/APL.html vignetteTitles: Analyzing data with APL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/APL/inst/doc/APL.R dependencyCount: 180 Package: appreci8R Version: 1.16.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, rsnps, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, utils, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db License: LGPL-3 MD5sum: bf71dc7fbecf1dcd7ed10576d9127458 NeedsCompilation: no Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/appreci8R git_branch: RELEASE_3_16 git_last_commit: e035de3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/appreci8R_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/appreci8R_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/appreci8R_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/appreci8R_1.16.0.tgz vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf vignetteTitles: Using appreci8R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R dependencyCount: 168 Package: aroma.light Version: 3.28.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: 41873cd80a3006a0c1e3d97a5dd41e3f NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: RELEASE_3_16 git_last_commit: a61bf3e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/aroma.light_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/aroma.light_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/aroma.light_3.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/aroma.light_3.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone, PSCBS suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core dependencyCount: 8 Package: ArrayExpress Version: 1.57.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: b6acb78969e4a9e0474dbb96fdf1a46f NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert Maintainer: Suhaib Mohammed git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: master git_last_commit: 321e40d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ArrayExpress_1.57.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ArrayExpress_1.57.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ArrayExpress_1.57.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ArrayExpress_1.57.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease suggestsMe: Hiiragi2013, bapred, seeker dependencyCount: 56 Package: ArrayExpressHTS Version: 1.47.0 Depends: sampling, Rsamtools (>= 1.99.0), snow Imports: Biobase, BiocGenerics, Biostrings, GenomicRanges, Hmisc, IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rhtslib (>= 1.15.3) License: Artistic License 2.0 MD5sum: a737adbd619efab5b847cae073a55848 NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: ImmunoOncology, RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/ArrayExpressHTS git_branch: master git_last_commit: 4f3ba88 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/ArrayExpressHTS_1.47.0.tar.gz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R dependencyCount: 150 Package: arrayMvout Version: 1.56.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy Imports: mdqc, affyContam, lumi Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: 2e824877f0535fafa29106d192931e34 NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: RELEASE_3_16 git_last_commit: 252dbe6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/arrayMvout_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/arrayMvout_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/arrayMvout_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/arrayMvout_1.56.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R dependencyCount: 166 Package: arrayQuality Version: 1.76.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 0feba15db585d8925f0c25e54751d6a7 NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: RELEASE_3_16 git_last_commit: 7836fa3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/arrayQuality_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/arrayQuality_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/arrayQuality_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/arrayQuality_1.76.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: arrayQualityMetrics Version: 3.54.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: fbeea53cd5c65f8ccf62c1473e622f89 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: RELEASE_3_16 git_last_commit: 8dfb5dd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/arrayQualityMetrics_3.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/arrayQualityMetrics_3.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/arrayQualityMetrics_3.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/arrayQualityMetrics_3.54.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R dependencyCount: 134 Package: ARRmNormalization Version: 1.38.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 271de41ca9a42c15860876f8e79836b0 NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: RELEASE_3_16 git_last_commit: 0f4adf3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ARRmNormalization_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ARRmNormalization_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ARRmNormalization_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ARRmNormalization_1.38.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: artMS Version: 1.16.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, bit64, circlize, cluster, corrplot, data.table, dplyr, getopt, ggdendro, ggplot2, gplots, ggrepel, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, ComplexHeatmap, factoextra, FactoMineR, gProfileR, knitr, PerformanceAnalytics, org.Mm.eg.db, rmarkdown, testthat License: GPL (>= 3) + file LICENSE MD5sum: 2ef02b42db86fef7bad3743456c66688 NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales [aut, cre] (), Alexandre Rosa Campos [aut, ctb] (), John Von Dollen [aut], Nevan Krogan [aut] (), Danielle Swaney [aut, ctb] () Maintainer: David Jimenez-Morales URL: http://artms.org VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues git_url: https://git.bioconductor.org/packages/artMS git_branch: RELEASE_3_16 git_last_commit: 0633a95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/artMS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/artMS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/artMS_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/artMS_1.16.0.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 159 Package: ASAFE Version: 1.24.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 46ac15b9109436311f6f5cca89056df4 NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: RELEASE_3_16 git_last_commit: 732a2e9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASAFE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASAFE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASAFE_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASAFE_1.24.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.42.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: x64 MD5sum: ec93c088b0d32771d713c713c3bd7e2a NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: RELEASE_3_16 git_last_commit: dd736b5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASEB_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASEB_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASEB_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASEB_1.42.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.32.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 4b2e5da9da66a109a1e9bb34086044a5 NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: RELEASE_3_16 git_last_commit: 4cbd6c7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASGSCA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASGSCA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASGSCA_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASGSCA_1.32.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R dependencyCount: 9 Package: ASICS Version: 2.14.0 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods, mvtnorm, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: 42618e0ae51c5fb0ad97e5f72bf0b295 NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: RELEASE_3_16 git_last_commit: 57b60cb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASICS_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASICS_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASICS_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASICS_2.14.0.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R dependencyCount: 128 Package: ASpediaFI Version: 1.11.0 Depends: R (>= 3.6.0), SummarizedExperiment, ROCR Imports: BiocParallel, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, IVAS, Rsamtools, biomaRt, limma, S4Vectors, stats, DRaWR, GenomeInfoDb, Gviz, Matrix, dplyr, fgsea, reshape2, igraph, graphics, e1071, methods, rtracklayer, scales, grid, ggplot2, mGSZ, utils Suggests: knitr License: GPL-3 MD5sum: cc09f47446cb09110d4b6889d52c6c14 NeedsCompilation: no Title: ASpedia-FI: Functional Interaction Analysis of Alternative Splicing Events Description: This package provides functionalities for a systematic and integrative analysis of alternative splicing events and their functional interactions. biocViews: AlternativeSplicing, Annotation, Coverage, GeneExpression, GeneSetEnrichment, GraphAndNetwork, KEGG, Network, NetworkInference, Pathways, Reactome, Transcription, Sequencing, Visualization Author: Doyeong Yu, Kyubin Lee, Daejin Hyung, Soo Young Cho, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr BugReports: https://github.com/nachoryu/ASpediaFI git_url: https://git.bioconductor.org/packages/ASpediaFI git_branch: master git_last_commit: 808c177 git_last_commit_date: 2022-04-26 Date/Publication: 2022-05-01 source.ver: src/contrib/ASpediaFI_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASpediaFI_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASpediaFI_1.11.0.tgz vignettes: vignettes/ASpediaFI/inst/doc/ASpediaFI.pdf vignetteTitles: ASpediaFI.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpediaFI/inst/doc/ASpediaFI.R dependencyCount: 191 Package: ASpli Version: 2.8.0 Depends: methods, grDevices, stats, utils, parallel, edgeR, limma, AnnotationDbi Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle, igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid, graphics, pbmcapply License: GPL MD5sum: 0b44adbcd9b1533799a07ca9082eb239 NeedsCompilation: no Title: Analysis of Alternative Splicing Using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Estefania Mancini git_url: https://git.bioconductor.org/packages/ASpli git_branch: RELEASE_3_16 git_last_commit: ff40685 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASpli_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASpli_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASpli_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASpli_2.8.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R dependencyCount: 167 Package: AssessORF Version: 1.16.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: c1f8a5e955f04bf92610e1b75551a330 NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: RELEASE_3_16 git_last_commit: 0a35f1b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AssessORF_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AssessORF_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AssessORF_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AssessORF_1.16.0.tgz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R suggestsMe: AssessORFData dependencyCount: 37 Package: ASSET Version: 2.16.0 Depends: R (>= 3.5.0), stats, graphics Imports: MASS, msm, rmeta Suggests: RUnit, BiocGenerics, knitr License: GPL-2 + file LICENSE MD5sum: f6009892f7d3840dbd00a55cbee23627 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant. biocViews: StatisticalMethod, SNP, GenomeWideAssociation, MultipleComparison Author: Samsiddhi Bhattacharjee [aut, cre], Guanghao Qi [aut], Nilanjan Chatterjee [aut], William Wheeler [aut] Maintainer: Samsiddhi Bhattacharjee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_16 git_last_commit: 10c22c3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASSET_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASSET_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASSET_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASSET_2.16.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 15 Package: ASSIGN Version: 1.34.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 90043100613f938ff0a7bf54c9288285 NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: RELEASE_3_16 git_last_commit: 5b656c6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASSIGN_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASSIGN_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASSIGN_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASSIGN_1.34.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R importsMe: TBSignatureProfiler dependencyCount: 98 Package: ASURAT Version: 1.2.0 Depends: R (>= 4.0.0) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, Rcpp (>= 1.0.7), cluster, utils, plot3D, ComplexHeatmap, circlize, grid, grDevices, graphics LinkingTo: Rcpp Suggests: ggplot2, TENxPBMCData, dplyr, Rtsne, Seurat, AnnotationDbi, BiocGenerics, stringr, org.Hs.eg.db, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 + file LICENSE Archs: x64 MD5sum: e8f11759f0e72d7504f271e8de82388e NeedsCompilation: yes Title: Functional annotation-driven unsupervised clustering for single-cell data Description: ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs). biocViews: GeneExpression, SingleCell, Sequencing, Clustering, GeneSignaling Author: Keita Iida [aut, cre] (), Johannes Nicolaus Wibisana [ctb] Maintainer: Keita Iida VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASURAT git_branch: RELEASE_3_16 git_last_commit: c986006 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ASURAT_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ASURAT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ASURAT_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ASURAT_1.2.0.tgz vignettes: vignettes/ASURAT/inst/doc/ASURAT.html vignetteTitles: ASURAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASURAT/inst/doc/ASURAT.R dependencyCount: 48 Package: ATACCoGAPS Version: 1.0.0 Depends: R (>= 4.2.0), CoGAPS (>= 3.5.13) Imports: gtools, GenomicRanges, projectR, TFBSTools, GeneOverlap, msigdbr, tidyverse, gplots, motifmatchr, chromVAR, GenomicFeatures, IRanges, fgsea, rGREAT, JASPAR2016, Homo.sapiens, Mus.musculus, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, stringr, dplyr Suggests: knitr, viridis License: Artistic-2.0 MD5sum: e1e57847a30aa2a8a5b07cf4513621de NeedsCompilation: no Title: Analysis Tools for scATACseq Data with CoGAPS Description: Provides tools for running the CoGAPS algorithm (Fertig et al, 2010) on single-cell ATAC sequencing data and analysis of the results. Can be used to perform analyses at the level of genes, motifs, TFs, or pathways. Additionally provides tools for transfer learning and data integration with single-cell RNA sequencing data. biocViews: Software, ResearchField, Epigenetics, SingleCell, Transcription, Bayesian, Clustering, DimensionReduction Author: Rossin Erbe [aut, cre] () Maintainer: Rossin Erbe VignetteBuilder: knitr BugReports: https://github.com/FertigLab/ATACCoGAPS/issues git_url: https://git.bioconductor.org/packages/ATACCoGAPS git_branch: RELEASE_3_16 git_last_commit: f320695 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ATACCoGAPS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ATACCoGAPS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ATACCoGAPS_1.0.0.tgz vignettes: vignettes/ATACCoGAPS/inst/doc/ATACCoGAPS.html vignetteTitles: ATACCoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACCoGAPS/inst/doc/ATACCoGAPS.R dependencyCount: 241 Package: ATACseqQC Version: 1.22.0 Depends: R (>= 3.5.0), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat, rmarkdown License: GPL (>= 2) MD5sum: 7c486d6bc3730cc9bb1a12601f6d4733 NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: RELEASE_3_16 git_last_commit: be03c44 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ATACseqQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ATACseqQC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ATACseqQC_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ATACseqQC_1.22.0.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R suggestsMe: ATACseqTFEA dependencyCount: 189 Package: ATACseqTFEA Version: 1.0.1 Depends: R (>= 4.2) Imports: BiocGenerics, S4Vectors, IRanges, Matrix, GenomicRanges, GenomicAlignments, GenomeInfoDb, SummarizedExperiment, Rsamtools, motifmatchr, TFBSTools, stats, pracma, ggplot2, ggrepel, dplyr, limma, methods Suggests: BSgenome.Drerio.UCSC.danRer10, knitr, testthat, ATACseqQC, rmarkdown, BiocStyle License: GPL-3 MD5sum: e5de11a5adccff977b830f09981cca58 NeedsCompilation: no Title: Transcription Factor Enrichment Analysis for ATAC-seq Description: Assay for Transpose-Accessible Chromatin using sequencing (ATAC-seq) is a technique to assess genome-wide chromatin accessibility by probing open chromatin with hyperactive mutant Tn5 Transposase that inserts sequencing adapters into open regions of the genome. ATACseqTFEA is an improvement of the current computational method that detects differential activity of transcription factors (TFs). ATACseqTFEA not only uses the difference of open region information, but also (or emphasizes) the difference of TFs footprints (cutting sites or insertion sites). ATACseqTFEA provides an easy, rigorous way to broadly assess TF activity changes between two conditions. biocViews: Sequencing, DNASeq, ATACSeq, MNaseSeq, GeneRegulation Author: Jianhong Ou [aut, cre] () Maintainer: Jianhong Ou URL: https://github.com/jianhong/ATACseqTFEA VignetteBuilder: knitr BugReports: https://github.com/jianhong/ATACseqTFEA/issues git_url: https://git.bioconductor.org/packages/ATACseqTFEA git_branch: RELEASE_3_16 git_last_commit: f25f34f git_last_commit_date: 2022-11-02 Date/Publication: 2022-11-02 source.ver: src/contrib/ATACseqTFEA_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ATACseqTFEA_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ATACseqTFEA_1.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ATACseqTFEA_1.0.1.tgz vignettes: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.html vignetteTitles: ATACseqTFEA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqTFEA/inst/doc/ATACseqTFEA.R dependencyCount: 127 Package: atena Version: 1.4.1 Depends: R (>= 4.1), SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, BiocParallel, S4Vectors, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, SQUAREM, sparseMatrixStats, AnnotationHub, scales, matrixStats Suggests: covr, BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: ae24e1475f76c875e62d5e9bc1b49f2b NeedsCompilation: no Title: Analysis of Transposable Elements Description: Quantify expression of transposable elements (TEs) from RNA-seq data through different methods, including ERVmap, TEtranscripts and Telescope. A common interface is provided to use each of these methods, which consists of building a parameter object, calling the quantification function with this object and getting a SummarizedExperiment object as output container of the quantified expression profiles. The implementation allows one to quantify TEs and gene transcripts in an integrated manner. biocViews: Transcription, Transcriptomics, RNASeq, Sequencing, Preprocessing, Software, GeneExpression, Coverage, DifferentialExpression, FunctionalGenomics Author: Beatriz Calvo-Serra [aut, cre], Robert Castelo [aut] Maintainer: Beatriz Calvo-Serra URL: https://github.com/functionalgenomics/atena VignetteBuilder: knitr BugReports: https://github.com/functionalgenomics/atena/issues git_url: https://git.bioconductor.org/packages/atena git_branch: RELEASE_3_16 git_last_commit: c41a0fb git_last_commit_date: 2023-02-03 Date/Publication: 2023-02-03 source.ver: src/contrib/atena_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/atena_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/atena_1.4.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/atena_1.4.1.tgz vignettes: vignettes/atena/inst/doc/atena.html vignetteTitles: An introduction to the atena package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atena/inst/doc/atena.R dependencyCount: 124 Package: atSNP Version: 1.14.0 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testthat, utils, lifecycle LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 369b51757f87d6aa9db6525362ff0ff8 NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: RELEASE_3_16 git_last_commit: a71f2c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/atSNP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/atSNP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/atSNP_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/atSNP_1.14.0.tgz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 163 Package: attract Version: 1.50.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: cc6d8ec12476dba5e4299fc998cb7e65 NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/attract git_branch: RELEASE_3_16 git_last_commit: 2df810f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/attract_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/attract_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/attract_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/attract_1.50.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R dependencyCount: 68 Package: AUCell Version: 1.20.2 Imports: DelayedArray, DelayedMatrixStats, data.table, graphics, grDevices, GSEABase, Matrix, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, BiocGenerics, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, rbokeh, Rtsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: 7ca6f285de6793d2b0f538cb57560a99 NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_16 git_last_commit: 1822d37 git_last_commit_date: 2022-12-01 Date/Publication: 2022-12-01 source.ver: src/contrib/AUCell_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/AUCell_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/AUCell_1.20.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AUCell_1.20.2.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R dependsOnMe: OSCA.basic importsMe: RcisTarget suggestsMe: decoupleR, SCpubr dependencyCount: 124 Package: autonomics Version: 1.6.0 Depends: R (>= 4.0) Imports: abind, assertive, BiocFileCache, BiocGenerics, colorspace, data.table, edgeR, ggplot2, ggrepel, graphics, grDevices, grid, gridExtra, limma, magrittr, matrixStats, methods, MultiAssayExperiment, parallel, pcaMethods, rappdirs, rlang, R.utils, readxl, S4Vectors, scales, stats, stringi, SummarizedExperiment, tidyr, tools, utils Suggests: affy, AnnotationDbi, BiocManager, diagram, GenomicRanges, GEOquery, hgu95av2.db, ICSNP, knitr, lme4, lmerTest, MASS, mixOmics, mpm, nlme, org.Hs.eg.db, org.Mm.eg.db, RCurl, remotes, rmarkdown, ropls, Rsubread, rtracklayer, seqinr, statmod, testthat License: GPL-3 MD5sum: 10dfc68a0d084e41156bd314c28fc5b8 NeedsCompilation: no Title: Generifying and intuifying cross-platform omics analysis Description: This package offers a generic and intuitive solution for cross-platform omics data analysis. It has functions for import, preprocessing, exploration, contrast analysis and visualization of omics data. It follows a tidy, functional programming paradigm. biocViews: DataImport, DimensionReduction, GeneExpression, MassSpectrometry, Preprocessing, PrincipalComponent, RNASeq, Software, Transcription Author: Aditya Bhagwat [aut, cre], Shahina Hayat [aut], Anna Halama [ctb], Richard Cotton [ctb], Laure Cougnaud [ctb], Rudolf Engelke [ctb], Hinrich Goehlmann [sad], Karsten Suhre [sad], Johannes Graumann [aut, sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/bhagwataditya/autonomics VignetteBuilder: knitr BugReports: https://bitbucket.org/graumannlabtools/autonomics git_url: https://git.bioconductor.org/packages/autonomics git_branch: RELEASE_3_16 git_last_commit: 6768188 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/autonomics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/autonomics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/autonomics_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/autonomics_1.6.0.tgz vignettes: vignettes/autonomics/inst/doc/using_autonomics.html vignetteTitles: using_autonomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/autonomics/inst/doc/using_autonomics.R dependencyCount: 124 Package: AWFisher Version: 1.12.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: x64 MD5sum: f00e62ecac272132a25beb2b8408b2c9 NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: RELEASE_3_16 git_last_commit: 431750b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/AWFisher_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/AWFisher_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/AWFisher_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/AWFisher_1.12.0.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: awst Version: 1.6.0 Imports: stats, methods, SummarizedExperiment Suggests: airway, ggplot2, testthat, EDASeq, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown License: MIT + file LICENSE MD5sum: 294622a361a7c2462c8c71c2b60d8105 NeedsCompilation: no Title: Asymmetric Within-Sample Transformation Description: We propose an Asymmetric Within-Sample Transformation (AWST) to regularize RNA-seq read counts and reduce the effect of noise on the classification of samples. AWST comprises two main steps: standardization and smoothing. These steps transform gene expression data to reduce the noise of the lowly expressed features, which suffer from background effects and low signal-to-noise ratio, and the influence of the highly expressed features, which may be the result of amplification bias and other experimental artifacts. biocViews: Normalization, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph] (), Stefano Pagnotta [aut, cph] () Maintainer: Davide Risso URL: https://github.com/drisso/awst VignetteBuilder: knitr BugReports: https://github.com/drisso/awst/issues git_url: https://git.bioconductor.org/packages/awst git_branch: RELEASE_3_16 git_last_commit: b0c3a99 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/awst_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/awst_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/awst_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/awst_1.6.0.tgz vignettes: vignettes/awst/inst/doc/awst_intro.html vignetteTitles: Introduction to awst hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/awst/inst/doc/awst_intro.R dependencyCount: 25 Package: BaalChIP Version: 1.24.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 933d1b8c39f86e9b5804d12fde54fe99 NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: RELEASE_3_16 git_last_commit: d4145fe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BaalChIP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BaalChIP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BaalChIP_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BaalChIP_1.24.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 110 Package: BAC Version: 1.58.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: x64 MD5sum: e2e16f6a6fdcea62f2c81fa22b028c1a NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/BAC git_branch: RELEASE_3_16 git_last_commit: e43017b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BAC_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BAC_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BAC_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BAC_1.58.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R dependencyCount: 0 Package: bacon Version: 1.26.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: x64 MD5sum: 6e3160f5907ac61082baa9c6f98002a4 NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: RELEASE_3_16 git_last_commit: 7eec762 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bacon_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bacon_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bacon_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bacon_1.26.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 46 Package: BADER Version: 1.36.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: x64 MD5sum: 712d02420f6824e3e537e5a2af245401 NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: RELEASE_3_16 git_last_commit: bb34300 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BADER_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BADER_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BADER_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BADER_1.36.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.26.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: ff6406ba94601dea34f2ae0d8818213b NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: RELEASE_3_16 git_last_commit: 328cbd2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BadRegionFinder_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BadRegionFinder_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BadRegionFinder_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BadRegionFinder_1.26.0.tgz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 98 Package: BAGS Version: 2.38.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: x64 MD5sum: 2a843ff3165670ae6de5872a349b0f10 NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate git_url: https://git.bioconductor.org/packages/BAGS git_branch: RELEASE_3_16 git_last_commit: c50fe0d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BAGS_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BAGS_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BAGS_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BAGS_2.38.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R dependencyCount: 7 Package: ballgown Version: 2.30.0 Depends: R (>= 3.5.0), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr, markdown License: Artistic-2.0 MD5sum: f852baa110225bc98fb910e57cdd8638 NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: RELEASE_3_16 git_last_commit: 70655cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ballgown_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ballgown_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ballgown_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ballgown_2.30.0.tgz vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R dependsOnMe: VaSP importsMe: RNASeqR suggestsMe: polyester, variancePartition dependencyCount: 85 Package: bambu Version: 3.0.8 Depends: R(>= 4.1), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), BSgenome, IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, tidyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, Rsamtools, methods, Rcpp, xgboost LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, rmarkdown, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, purrr, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE Archs: x64 MD5sum: 69b4a8c169e891cab62fcac39a20c9cf NeedsCompilation: yes Title: Context-Aware Transcript Quantification from Long Read RNA-Seq data Description: bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage. biocViews: Alignment, Coverage, DifferentialExpression, FeatureExtraction, GeneExpression, GenomeAnnotation, GenomeAssembly, ImmunoOncology, MultipleComparison, Normalization, RNASeq, Regression, Sequencing, Software, Transcription, Transcriptomics Author: Ying Chen [cre, aut], Andre Sim [aut], Yuk Kei Wan [aut], Jonathan Goeke [aut] Maintainer: Ying Chen URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: RELEASE_3_16 git_last_commit: a946653 git_last_commit_date: 2023-02-14 Date/Publication: 2023-02-15 source.ver: src/contrib/bambu_3.0.8.tar.gz win.binary.ver: bin/windows/contrib/4.2/bambu_3.0.8.zip mac.binary.ver: bin/macosx/contrib/4.2/bambu_3.0.8.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bambu_3.0.8.tgz vignettes: vignettes/bambu/inst/doc/bambu.html vignetteTitles: bambu hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bambu/inst/doc/bambu.R importsMe: FLAMES suggestsMe: NanoporeRNASeq dependencyCount: 101 Package: bamsignals Version: 1.30.0 Depends: R (>= 3.5.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 15da3cbccec7fa05126994cd29321527 NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Johannes Helmuth URL: https://github.com/lamortenera/bamsignals SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/lamortenera/bamsignals/issues git_url: https://git.bioconductor.org/packages/bamsignals git_branch: RELEASE_3_16 git_last_commit: aac37df git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bamsignals_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bamsignals_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bamsignals_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bamsignals_1.30.0.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: AneuFinder, chromstaR, DNAfusion, epigraHMM, karyoploteR, normr, segmenter, hoardeR dependencyCount: 18 Package: BANDITS Version: 1.14.2 Depends: R (>= 4.0.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) Archs: x64 MD5sum: 8bb204aa5cd45961aecb107b00599a40 NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: RELEASE_3_16 git_last_commit: d998f17 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BANDITS_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BANDITS_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BANDITS_1.14.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BANDITS_1.14.2.tgz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R importsMe: DifferentialRegulation dependencyCount: 77 Package: bandle Version: 1.2.2 Depends: R (>= 4.1), S4Vectors, Biobase, MSnbase, pRoloc Imports: Rcpp (>= 1.0.4.6), pRolocdata, lbfgs, ggplot2, dplyr, plyr, knitr, methods, BiocParallel, robustbase, BiocStyle, ggalluvial, ggrepel, tidyr, circlize, graphics, stats, utils, grDevices, rlang LinkingTo: Rcpp, RcppArmadillo, BH Suggests: coda (>= 0.19-4), testthat, interp, fields, pheatmap, viridis, rmarkdown, spelling License: Artistic-2.0 Archs: x64 MD5sum: bd379edc3c92e9fff1b21c92949ed82e NeedsCompilation: yes Title: An R package for the Bayesian analysis of differential subcellular localisation experiments Description: The Bandle package enables the analysis and visualisation of differential localisation experiments using mass-spectrometry data. Experimental methods supported include dynamic LOPIT-DC, hyperLOPIT, Dynamic Organellar Maps, Dynamic PCP. It provides Bioconductor infrastructure to analyse these data. biocViews: Bayesian, Classification, Clustering, ImmunoOncology, QualityControl,DataImport, Proteomics, MassSpectrometry Author: Oliver M. Crook [aut, cre] (), Lisa Breckels [aut] () Maintainer: Oliver M. Crook URL: http://github.com/ococrook/bandle VignetteBuilder: knitr BugReports: https://github.com/ococrook/bandle/issues git_url: https://git.bioconductor.org/packages/bandle git_branch: RELEASE_3_16 git_last_commit: 8ab251f git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/bandle_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/bandle_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/bandle_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bandle_1.2.2.tgz vignettes: vignettes/bandle/inst/doc/v01-getting-started.html, vignettes/bandle/inst/doc/v02-workflow.html vignetteTitles: Analysing differential localisation experiments with BANDLE: Vignette 1, Analysing differential localisation experiments with BANDLE: Vignette 2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bandle/inst/doc/v01-getting-started.R, vignettes/bandle/inst/doc/v02-workflow.R dependencyCount: 230 Package: banocc Version: 1.22.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 892551c4602b33cb7cc06eee2e2d1265 NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: RELEASE_3_16 git_last_commit: 1021506 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/banocc_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/banocc_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/banocc_1.22.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 62 Package: barcodetrackR Version: 1.6.0 Depends: R (>= 4.1) Imports: cowplot, circlize, dplyr, ggplot2, ggdendro, ggridges, graphics, grDevices, magrittr, plyr, proxy, RColorBrewer, rlang, scales, shiny, stats, SummarizedExperiment, S4Vectors, tibble, tidyr, vegan, viridis, utils Suggests: BiocStyle, knitr, magick, rmarkdown, testthat License: file LICENSE MD5sum: 0928f60a14c5428b02f1ce9e38220799 NeedsCompilation: no Title: Functions for Analyzing Cellular Barcoding Data Description: barcodetrackR is an R package developed for the analysis and visualization of clonal tracking data. Data required is samples and tag abundances in matrix form. Usually from cellular barcoding experiments, integration site retrieval analyses, or similar technologies. biocViews: Software, Visualization, Sequencing Author: Diego Alexander Espinoza [aut, cre], Ryland Mortlock [aut] Maintainer: Diego Alexander Espinoza URL: https://github.com/dunbarlabNIH/barcodetrackR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/barcodetrackR git_branch: RELEASE_3_16 git_last_commit: 73e546d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/barcodetrackR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/barcodetrackR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/barcodetrackR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/barcodetrackR_1.6.0.tgz vignettes: vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.html vignetteTitles: barcodetrackR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/barcodetrackR/inst/doc/Introduction_to_barcodetrackR.R dependencyCount: 97 Package: basecallQC Version: 1.22.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) MD5sum: 8c535ac5b437b427c2faa253f1438034 NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: RELEASE_3_16 git_last_commit: 01b2024 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/basecallQC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/basecallQC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/basecallQC_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/basecallQC_1.22.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R dependencyCount: 117 Package: BaseSpaceR Version: 1.42.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 37384bb87932e9fa40ec827c9630061f NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: RELEASE_3_16 git_last_commit: 17f4ade git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BaseSpaceR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BaseSpaceR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BaseSpaceR_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BaseSpaceR_1.42.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.34.0 Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: f765d73bd452b6d36dc9ad94d9867ef0 NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: RELEASE_3_16 git_last_commit: 53ec861 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Basic4Cseq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Basic4Cseq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Basic4Cseq_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Basic4Cseq_1.34.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 50 Package: BASiCS Version: 2.10.5 Depends: R (>= 4.0), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, scuttle, stats, stats4, SummarizedExperiment, viridis, utils, Matrix (>= 1.5.0), matrixStats, assertthat, reshape2, BiocParallel, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, magick License: GPL (>= 2) Archs: x64 MD5sum: 27fd2ffad9308d17edac943f272641f0 NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut, cre], Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos URL: https://github.com/catavallejos/BASiCS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: RELEASE_3_16 git_last_commit: 6de1264 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BASiCS_2.10.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/BASiCS_2.10.5.zip mac.binary.ver: bin/macosx/contrib/4.2/BASiCS_2.10.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BASiCS_2.10.5.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R dependsOnMe: BASiCStan suggestsMe: splatter dependencyCount: 131 Package: BASiCStan Version: 1.0.0 Depends: R (>= 4.2), BASiCS, rstan (>= 2.18.1) Imports: methods, glmGamPoi, scran, scuttle, stats, utils, SingleCellExperiment, SummarizedExperiment, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1) LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: d7a579460820805e68fc5d9565af5ba1 NeedsCompilation: yes Title: Stan implementation of BASiCS Description: Provides an interface to infer the parameters of BASiCS using the variational inference (ADVI), Markov chain Monte Carlo (NUTS), and maximum a posteriori (BFGS) inference engines in the Stan programming language. BASiCS is a Bayesian hierarchical model that uses an adaptive Metropolis within Gibbs sampling scheme. Alternative inference methods provided by Stan may be preferable in some situations, for example for particularly large data or posterior distributions with difficult geometries. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology Author: Alan O'Callaghan [aut, cre], Catalina Vallejos [aut] Maintainer: Alan O'Callaghan URL: https://github.com/Alanocallaghan/BASiCStan SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/BASiCStan/issues git_url: https://git.bioconductor.org/packages/BASiCStan git_branch: RELEASE_3_16 git_last_commit: 5824462 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BASiCStan_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BASiCStan_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BASiCStan_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BASiCStan_1.0.0.tgz vignettes: vignettes/BASiCStan/inst/doc/BASiCStan.html vignetteTitles: An introduction to BASiCStan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCStan/inst/doc/BASiCStan.R dependencyCount: 153 Package: BasicSTARRseq Version: 1.26.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 9717c2f5eaf1792c629a637e971f2bd3 NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: RELEASE_3_16 git_last_commit: b156a23 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BasicSTARRseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BasicSTARRseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BasicSTARRseq_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BasicSTARRseq_1.26.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 40 Package: basilisk Version: 1.10.2 Imports: utils, methods, parallel, reticulate, dir.expiry, basilisk.utils Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: 5844a4062e45b474042a68f37fca5425 NeedsCompilation: no Title: Freezing Python Dependencies Inside Bioconductor Packages Description: Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr BugReports: https://github.com/LTLA/basilisk/issues git_url: https://git.bioconductor.org/packages/basilisk git_branch: RELEASE_3_16 git_last_commit: 9c9ad75 git_last_commit_date: 2022-11-08 Date/Publication: 2022-11-08 source.ver: src/contrib/basilisk_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/basilisk_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/basilisk_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/basilisk_1.10.2.tgz vignettes: vignettes/basilisk/inst/doc/motivation.html vignetteTitles: Motivation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk/inst/doc/motivation.R importsMe: BiocSklearn, cbpManager, crisprScore, dasper, densvis, FLAMES, MACSr, MOFA2, Rcwl, recountmethylation, scPipe, SimBu, snifter, spatialDE, VDJdive, velociraptor, zellkonverter dependencyCount: 22 Package: basilisk.utils Version: 1.10.0 Imports: utils, methods, tools, dir.expiry Suggests: reticulate, knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: 9e3a4c0ec22a491c0bfb6d7fefbe2587 NeedsCompilation: no Title: Basilisk Installation Utilities Description: Implements utilities for installation of the basilisk package, primarily for creation of the underlying Conda instance. This allows us to avoid re-writing the same R code in both the configure script (for centrally administered R installations) and in the lazy installation mechanism (for distributed package binaries). It is highly unlikely that developers - or, heaven forbid, end-users! - will need to interact with this package directly; they should be using the basilisk package instead. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: RELEASE_3_16 git_last_commit: b4d6087 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/basilisk.utils_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/basilisk.utils_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/basilisk.utils_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/basilisk.utils_1.10.0.tgz vignettes: vignettes/basilisk.utils/inst/doc/purpose.html vignetteTitles: _basilisk_ installation utilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R importsMe: basilisk, crisprScore dependencyCount: 5 Package: batchelor Version: 1.14.1 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix, ScaledMatrix, beachmat LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 Archs: x64 MD5sum: f8230f96c9f7d0046ea0ee4e7926448e NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: RELEASE_3_16 git_last_commit: a1f3e84 git_last_commit_date: 2023-01-02 Date/Publication: 2023-01-03 source.ver: src/contrib/batchelor_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/batchelor_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/batchelor_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/batchelor_1.14.1.tgz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: ChromSCape, mumosa, singleCellTK suggestsMe: TSCAN, RaceID dependencyCount: 52 Package: BatchQC Version: 1.26.0 Depends: R (>= 3.5.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, heatmaply, reshape2, limma, grDevices, graphics, stats, methods, Matrix Suggests: testthat License: GPL (>= 2) MD5sum: bb2d42cb4e5cac641e3f9bbc04b9079a NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Solaiappan Manimaran , W. Evan Johnson , Heather Selby , Claire Ruberman , Kwame Okrah , Hector Corrada Bravo Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/BatchQC SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_16 git_last_commit: c593445 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BatchQC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BatchQC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BatchQC_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BatchQC_1.26.0.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf, vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R dependencyCount: 160 Package: BayesKnockdown Version: 1.24.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 38d94347d31da27b1b3ed63a5833570d NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: RELEASE_3_16 git_last_commit: eb337ab git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BayesKnockdown_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BayesKnockdown_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BayesKnockdown_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BayesKnockdown_1.24.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 6 Package: BayesSpace Version: 1.8.2 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, assertthat, mclust, RCurl, DirichletReg, xgboost, utils, ggplot2, scales, BiocFileCache, BiocSingular LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, dplyr, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE Archs: x64 MD5sum: 5675845abc284d10d8595a2aeed4b88c NeedsCompilation: yes Title: Clustering and Resolution Enhancement of Spatial Transcriptomes Description: Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed. biocViews: Software, Clustering, Transcriptomics, GeneExpression, SingleCell, ImmunoOncology, DataImport Author: Edward Zhao [aut], Matt Stone [aut, cre], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Matt Stone URL: edward130603.github.io/BayesSpace SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: RELEASE_3_16 git_last_commit: e192cc9 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BayesSpace_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BayesSpace_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BayesSpace_1.8.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BayesSpace_1.8.2.tgz vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html vignetteTitles: BayesSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R dependencyCount: 150 Package: bayNorm Version: 1.16.2 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) Archs: x64 MD5sum: 5bb0855e7bbde18de4a283aac8d9d2de NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: RELEASE_3_16 git_last_commit: 7bd8b14 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/bayNorm_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/bayNorm_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.2/bayNorm_1.16.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bayNorm_1.16.2.tgz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 49 Package: baySeq Version: 2.31.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 7190586dd45adb3d81d58b3e22ebd0e1 NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/baySeq git_branch: master git_last_commit: c0f1a83 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/baySeq_2.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/baySeq_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/baySeq_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/baySeq_2.32.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, Rcade, segmentSeq, TCC importsMe: metaseqR2, riboSeqR, srnadiff suggestsMe: compcodeR dependencyCount: 25 Package: BBCAnalyzer Version: 1.28.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 4ab38c1b5062a171b4f522791f6b172f NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: RELEASE_3_16 git_last_commit: 136e385 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BBCAnalyzer_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BBCAnalyzer_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BBCAnalyzer_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BBCAnalyzer_1.28.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 98 Package: BCRANK Version: 1.60.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: x64 MD5sum: c3494f9c7ec4b41596d1b3132005c0c7 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: RELEASE_3_16 git_last_commit: 519d8aa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BCRANK_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BCRANK_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BCRANK_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BCRANK_1.60.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 18 Package: bcSeq Version: 1.20.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: x64 MD5sum: ed0791e7abeeb2b460788ae18b00e33d NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: RELEASE_3_16 git_last_commit: e47dac1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bcSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bcSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bcSeq_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bcSeq_1.20.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 22 Package: BDMMAcorrect Version: 1.16.2 Depends: R (>= 3.5), vegan, ellipse, ggplot2, ape, SummarizedExperiment Imports: Rcpp (>= 0.12.12), RcppArmadillo, RcppEigen, stats LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: knitr, rmarkdown, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 91bbe0bc98938b15b46d51d53831666c NeedsCompilation: yes Title: Meta-analysis for the metagenomic read counts data from different cohorts Description: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. biocViews: ImmunoOncology, BatchEffect, Microbiome, Bayesian Author: ZHENWEI DAI Maintainer: ZHENWEI DAI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BDMMAcorrect git_branch: RELEASE_3_16 git_last_commit: 61ae2d3 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BDMMAcorrect_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BDMMAcorrect_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BDMMAcorrect_1.16.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BDMMAcorrect_1.16.2.tgz vignettes: vignettes/BDMMAcorrect/inst/doc/Vignette.pdf vignetteTitles: BDMMAcorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BDMMAcorrect/inst/doc/Vignette.R dependencyCount: 62 Package: beachmat Version: 2.14.2 Imports: methods, DelayedArray (>= 0.15.14), BiocGenerics, Matrix, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel, HDF5Array License: GPL-3 Archs: x64 MD5sum: 32e51c014f2113a5f2574df224d86d36 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from and writing data to a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, third-party S4 classes may be supported by external linkage, while all other matrices are handled by DelayedArray block processing. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat git_branch: RELEASE_3_16 git_last_commit: bfc3e8a git_last_commit_date: 2023-04-06 Date/Publication: 2023-04-07 source.ver: src/contrib/beachmat_2.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/beachmat_2.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/beachmat_2.14.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/beachmat_2.14.2.tgz vignettes: vignettes/beachmat/inst/doc/external.html, vignettes/beachmat/inst/doc/input.html, vignettes/beachmat/inst/doc/linking.html, vignettes/beachmat/inst/doc/output.html vignetteTitles: 4. Supporting arbitrary matrix classes (v2), 2. Reading data from R matrices in C++ (v2), 1. Developer guide, 3. Writing data into R matrix objects (v2) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/external.R, vignettes/beachmat/inst/doc/input.R, vignettes/beachmat/inst/doc/linking.R, vignettes/beachmat/inst/doc/output.R importsMe: batchelor, BiocSingular, DropletUtils, mumosa, scater, scran, scuttle, SingleR suggestsMe: bsseq, glmGamPoi, mbkmeans, PCAtools, scCB2 linksToMe: BiocSingular, bsseq, DropletUtils, glmGamPoi, mbkmeans, PCAtools, scran, scuttle, SingleR dependencyCount: 16 Package: beadarray Version: 2.48.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE Archs: x64 MD5sum: 5e7875372002d464edbd50a0730ef127 NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: RELEASE_3_16 git_last_commit: 3db0e75 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/beadarray_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beadarray_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beadarray_2.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/beadarray_2.48.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R dependsOnMe: beadarrayExampleData importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases, RobLoxBioC suggestsMe: beadarraySNP, lumi, blimaTestingData, maGUI dependencyCount: 80 Package: beadarraySNP Version: 1.64.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 93d114478e3d57db6f8d64308c0ee93d NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/beadarraySNP git_branch: RELEASE_3_16 git_last_commit: e629daa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/beadarraySNP_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beadarraySNP_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beadarraySNP_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/beadarraySNP_1.64.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R dependencyCount: 17 Package: BeadDataPackR Version: 1.50.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: x64 MD5sum: 956d16e2749f7236da3d7e52f987b5a5 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: RELEASE_3_16 git_last_commit: c84fdae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BeadDataPackR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BeadDataPackR_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BeadDataPackR_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BeadDataPackR_1.50.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray dependencyCount: 2 Package: BEARscc Version: 1.18.0 Depends: R (>= 3.5.0) Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 MD5sum: 4225cd3708a3e94b611f12d829b82b97 NeedsCompilation: no Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters) Description: BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls. biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics Author: David T. Severson Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BEARscc git_branch: RELEASE_3_16 git_last_commit: 12407a6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BEARscc_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BEARscc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BEARscc_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BEARscc_1.18.0.tgz vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R dependencyCount: 55 Package: BEAT Version: 1.36.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: aaa68971dad49206a5dcfca6df94f39e NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: RELEASE_3_16 git_last_commit: 4ad65bd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BEAT_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BEAT_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BEAT_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BEAT_1.36.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 58 Package: BEclear Version: 2.14.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, abind, stats, graphics, utils, methods, dixonTest, ids LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander, seewave License: GPL-3 Archs: x64 MD5sum: 77cc95ab6af022a293812f58a9cb164d NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: David Rasp [aut, cre] (), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: David Rasp URL: https://github.com/uds-helms/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/uds-helms/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: RELEASE_3_16 git_last_commit: 97b981f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BEclear_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BEclear_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BEclear_2.14.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 30 Package: beer Version: 1.2.0 Depends: R (>= 4.2.0), PhIPData (>= 1.1.1), rjags Imports: cli, edgeR, BiocParallel, methods, progressr, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, covr, codetools, knitr, rmarkdown, dplyr, ggplot2, spelling License: MIT + file LICENSE MD5sum: 60b21cf6d049865d7a2cd0517edcc7de NeedsCompilation: no Title: Bayesian Enrichment Estimation in R Description: BEER implements a Bayesian model for analyzing phage-immunoprecipitation sequencing (PhIP-seq) data. Given a PhIPData object, BEER returns posterior probabilities of enriched antibody responses, point estimates for the relative fold-change in comparison to negative control samples, and more. Additionally, BEER provides a convenient implementation for using edgeR to identify enriched antibody responses. biocViews: Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Athena Chen [aut, cre] (), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen URL: https://github.com/athchen/beer/ SystemRequirements: JAGS (4.3.0) VignetteBuilder: knitr BugReports: https://github.com/athchen/beer/issues git_url: https://git.bioconductor.org/packages/beer git_branch: RELEASE_3_16 git_last_commit: bbed750 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/beer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/beer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/beer_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/beer_1.2.0.tgz vignettes: vignettes/beer/inst/doc/beer.html vignetteTitles: beer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beer/inst/doc/beer.R dependencyCount: 84 Package: benchdamic Version: 1.4.0 Depends: R (>= 4.2.0) Imports: stats, stats4, utils, methods, phyloseq, TreeSummarizedExperiment, BiocParallel, zinbwave, edgeR, DESeq2, limma, ALDEx2, SummarizedExperiment, MAST, Seurat, ANCOMBC, NOISeq, dearseq, metagenomeSeq, corncob, MGLM, ggplot2, RColorBrewer, plyr, reshape2, ggdendro, ggridges, graphics, cowplot, tidytext Suggests: knitr, rmarkdown, kableExtra, BiocStyle, SPsimSeq, testthat License: Artistic-2.0 MD5sum: 7e36d2d9efd63244c15e67bd564d0f29 NeedsCompilation: no Title: Benchmark of differential abundance methods on microbiome data Description: Starting from a microbiome dataset (16S or WMS with absolute count values) it is possible to perform several analysis to assess the performances of many differential abundance detection methods. A basic and standardized version of the main differential abundance analysis methods is supplied but the user can also add his method to the benchmark. The analyses focus on 4 main aspects: i) the goodness of fit of each method's distributional assumptions on the observed count data, ii) the ability to control the false discovery rate, iii) the within and between method concordances, iv) the truthfulness of the findings if any apriori knowledge is given. Several graphical functions are available for result visualization. biocViews: Metagenomics, Microbiome, DifferentialExpression, MultipleComparison, Normalization, Preprocessing, Software Author: Matteo Calgaro [aut, cre], Chiara Romualdi [aut], Davide Risso [aut], Nicola Vitulo [aut] Maintainer: Matteo Calgaro VignetteBuilder: knitr BugReports: https://github.com/mcalgaro93/benchdamic/issues git_url: https://git.bioconductor.org/packages/benchdamic git_branch: RELEASE_3_16 git_last_commit: 0e7363b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/benchdamic_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/benchdamic_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/benchdamic_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/benchdamic_1.4.0.tgz vignettes: vignettes/benchdamic/inst/doc/intro.html vignetteTitles: Intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/benchdamic/inst/doc/intro.R dependencyCount: 331 Package: BgeeCall Version: 1.14.0 Depends: R (>= 3.6) Imports: GenomicFeatures, tximport, Biostrings, rtracklayer, biomaRt, jsonlite, methods, dplyr, data.table, sjmisc, grDevices, graphics, stats, utils, rslurm, rhdf5 Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr License: GPL-3 + file LICENSE MD5sum: a0b4e7b812feb24faaed468efff45fc0 NeedsCompilation: no Title: Automatic RNA-Seq present/absent gene expression calls generation Description: BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org). biocViews: Software, GeneExpression, RNASeq Author: Julien Wollbrett [aut, cre], Sara Fonseca Costa [aut], Julien Roux [aut], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues git_url: https://git.bioconductor.org/packages/BgeeCall git_branch: RELEASE_3_16 git_last_commit: 8ac40cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BgeeCall_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BgeeCall_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BgeeCall_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BgeeCall_1.14.0.tgz vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: automatic RNA-Seq present/absent gene expression calls generation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 107 Package: BgeeDB Version: 2.24.0 Depends: R (>= 3.6.0), topGO, tidyr Imports: R.utils, data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: 58c55b02e47352432b74bc6952b1494e NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: RELEASE_3_16 git_last_commit: cfca314 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BgeeDB_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BgeeDB_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BgeeDB_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BgeeDB_2.24.0.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: BgeeDB,, an R package for retrieval of curated expression datasets and for gene list enrichment tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: psygenet2r, RITAN suggestsMe: RITAN dependencyCount: 71 Package: BGmix Version: 1.58.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: f5ddae2d1fd13e1bc75d01e5fe25ebce NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin git_url: https://git.bioconductor.org/packages/BGmix git_branch: RELEASE_3_16 git_last_commit: 65641cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BGmix_1.58.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/BGmix_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BGmix_1.58.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R dependencyCount: 2 Package: bgx Version: 1.64.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Imports: Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: x64 MD5sum: cd8efcdeb0169ceb0c59ed8e05ae46e4 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro git_url: https://git.bioconductor.org/packages/bgx git_branch: RELEASE_3_16 git_last_commit: 9390093 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bgx_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bgx_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bgx_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bgx_1.64.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R dependencyCount: 26 Package: BHC Version: 1.50.0 License: GPL-3 Archs: x64 MD5sum: 96aa50b1c7ae1198f5c1431ca9ec0139 NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage git_url: https://git.bioconductor.org/packages/BHC git_branch: RELEASE_3_16 git_last_commit: 93d57be git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BHC_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BHC_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BHC_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BHC_1.50.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R dependencyCount: 0 Package: BicARE Version: 1.56.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: x64 MD5sum: c2a2edf555aff1e3753cafef7b52fb1a NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: RELEASE_3_16 git_last_commit: 7ee27f5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BicARE_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BicARE_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BicARE_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BicARE_1.56.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R dependsOnMe: RcmdrPlugin.BiclustGUI importsMe: miRSM dependencyCount: 58 Package: BiFET Version: 1.18.0 Depends: R (>= 3.5.0) Imports: stats, poibin, GenomicRanges Suggests: rmarkdown, testthat, knitr License: GPL-3 MD5sum: 7bd7c7d5b809fc67e4366adc10fdb8f3 NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: RELEASE_3_16 git_last_commit: 186c421 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiFET_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiFET_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiFET_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiFET_1.18.0.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 17 Package: BiGGR Version: 1.34.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: 710994f31ca3e07167d94fed4b5e2a8c NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway,Network,GraphAndNetwork, Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/BiGGR git_branch: RELEASE_3_16 git_last_commit: 5770893 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiGGR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiGGR_1.34.0.zip vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R dependencyCount: 29 Package: bigmelon Version: 1.24.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics, illuminaio Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: 8730bb44babecbfa93418203fd947c6b NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [cre, aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [aut] Maintainer: Tyler J. Gorrie-Stone git_url: https://git.bioconductor.org/packages/bigmelon git_branch: RELEASE_3_16 git_last_commit: 77478bf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bigmelon_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bigmelon_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bigmelon_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bigmelon_1.24.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 169 Package: bigPint Version: 1.14.0 Depends: R (>= 3.6.0) Imports: DelayedArray (>= 0.12.2), dplyr (>= 0.7.2), GGally (>= 1.3.2), ggplot2 (>= 2.2.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), grid (>= 3.5.0), gridExtra (>= 2.3), hexbin (>= 1.27.1), Hmisc (>= 4.0.3), htmlwidgets (>= 0.9), methods (>= 3.5.2), plotly (>= 4.7.1), plyr (>= 1.8.4), RColorBrewer (>= 1.1.2), reshape (>= 0.8.7), shiny (>= 1.0.5), shinycssloaders (>= 0.2.0), shinydashboard (>= 0.6.1), stats (>= 3.5.0), stringr (>= 1.3.1), SummarizedExperiment (>= 1.16.1), tidyr (>= 0.7.0), utils (>= 3.5.0) Suggests: BiocGenerics (>= 0.29.1), data.table (>= 1.11.8), EDASeq (>= 2.14.0), edgeR (>= 3.22.2), gtools (>= 3.5.0), knitr (>= 1.13), matrixStats (>= 0.53.1), rmarkdown (>= 1.10), roxygen2 (>= 3.0.0), RUnit (>= 0.4.32), tibble (>= 1.4.2), License: GPL-3 MD5sum: 65d21bef23c46b5574ccad1ad24313fd NeedsCompilation: no Title: Big multivariate data plotted interactively Description: Methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. Includes examples for visualizing RNA-sequencing datasets and differentially expressed genes. biocViews: Clustering, DataImport, DifferentialExpression, GeneExpression, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Sequencing, Software, Transcription, Visualization Author: Lindsay Rutter [aut, cre], Dianne Cook [aut] Maintainer: Lindsay Rutter URL: https://github.com/lindsayrutter/bigPint VignetteBuilder: knitr BugReports: https://github.com/lindsayrutter/bigPint/issues git_url: https://git.bioconductor.org/packages/bigPint git_branch: RELEASE_3_16 git_last_commit: ce713bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bigPint_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bigPint_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bigPint_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bigPint_1.14.0.tgz vignettes: vignettes/bigPint/inst/doc/bioconductor.html, vignettes/bigPint/inst/doc/manuscripts.html, vignettes/bigPint/inst/doc/summarizedExperiment.html vignetteTitles: "bigPint Vignette", "Recommended RNA-seq pipeline", "Data metrics object" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigPint/inst/doc/bioconductor.R, vignettes/bigPint/inst/doc/manuscripts.R, vignettes/bigPint/inst/doc/summarizedExperiment.R dependencyCount: 123 Package: BindingSiteFinder Version: 1.4.0 Depends: GenomicRanges, R (>= 4.2) Imports: tidyr, tibble, dplyr, plyr, matrixStats, stats, ggplot2, methods, rtracklayer, S4Vectors, ggforce, GenomeInfoDb, Gviz Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicAlignments, ComplexHeatmap, forcats, scales License: Artistic-2.0 MD5sum: 52deafdb0aafe75d1f194464a71549a4 NeedsCompilation: no Title: Binding site defintion based on iCLIP data Description: Precise knowledge on the binding sites of an RNA-binding protein (RBP) is key to understand (post-) transcriptional regulatory processes. Here we present a workflow that describes how exact binding sites can be defined from iCLIP data. The package provides functions for binding site definition and result visualization. For details please see the vignette. biocViews: Sequencing, GeneExpression, GeneRegulation, FunctionalGenomics, Coverage, DataImport Author: Mirko Brüggemann [aut, cre] (), Kathi Zarnack [aut] () Maintainer: Mirko Brüggemann VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/BindingSiteFinder/issues git_url: https://git.bioconductor.org/packages/BindingSiteFinder git_branch: RELEASE_3_16 git_last_commit: c66cdc8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BindingSiteFinder_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BindingSiteFinder_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BindingSiteFinder_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BindingSiteFinder_1.4.0.tgz vignettes: vignettes/BindingSiteFinder/inst/doc/vignette.html vignetteTitles: Definition of binding sites from iCLIP signal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BindingSiteFinder/inst/doc/vignette.R dependencyCount: 158 Package: bioassayR Version: 1.36.0 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, cellHTS2, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: 0ab2c2a602f1e8606e530a2b8b0cafd5 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Daniela Cassol URL: https://github.com/girke-lab/bioassayR VignetteBuilder: knitr BugReports: https://github.com/girke-lab/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: RELEASE_3_16 git_last_commit: 3ca74a6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bioassayR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bioassayR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bioassayR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bioassayR_1.36.0.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 84 Package: Biobase Version: 2.58.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets, BiocStyle, knitr License: Artistic-2.0 Archs: x64 MD5sum: 1590e59da0ac37aa41778c4606c78cf3 NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman [aut], V. Carey [aut], M. Morgan [aut], S. Falcon [aut], Haleema Khan [ctb] ('esApply' and 'BiobaseDevelopment' vignette translation from Sweave to Rmarkdown / HTML), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: RELEASE_3_16 git_last_commit: 767f2f3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Biobase_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Biobase_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Biobase_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Biobase_2.58.0.tgz vignettes: vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf, vignettes/Biobase/inst/doc/BiobaseDevelopment.html, vignettes/Biobase/inst/doc/esApply.html vignetteTitles: An introduction to Biobase and ExpressionSets, Notes for eSet developers, esApply Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, BAGS, bandle, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BioMVCClass, BioQC, BLMA, borealis, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, DEXSeq, DFP, diggit, doppelgangR, DSS, dyebias, EBarrays, EDASeq, edge, EGSEA, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GeoDiff, GEOexplorer, GeomxTools, GEOquery, GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, INSPEcT, isobar, iterativeBMA, IVAS, lumi, macat, made4, mAPKL, massiR, MEAL, metagenomeSeq, metavizr, MethPed, methylumi, Mfuzz, MiChip, microbiomeExplorer, mimager, MIMOSA, MineICA, MiRaGE, miRcomp, MLInterfaces, MMDiff2, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NanoStringNCTools, NanoTube, NOISeq, nondetects, normalize450K, NormqPCR, octad, oligo, omicRexposome, OrderedList, OTUbase, pandaR, panp, pcaMethods, pdInfoBuilder, pepStat, phenoTest, PLPE, POWSC, PREDA, pRolocGUI, PROMISE, qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, RefPlus, rexposome, Ringo, Risa, Rmagpie, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, SigCheck, siggenes, singleCellTK, SpeCond, SPEM, spkTools, splineTimeR, STROMA4, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, twilight, UNDO, VegaMC, viper, vsn, wateRmelon, webbioc, XDE, yarn, EuPathDB, affycompData, ALL, bcellViper, beadarrayExampleData, bladderbatch, brgedata, cancerdata, CCl4, CLL, colonCA, CRCL18, curatedBreastData, davidTiling, diggitdata, DLBCL, dressCheck, etec16s, fabiaData, fibroEset, gaschYHS, golubEsets, GSE103322, GSE13015, GSE62944, GSVAdata, harbChIP, Hiiragi2013, HumanAffyData, humanStemCell, Iyer517, kidpack, leeBamViews, leukemiasEset, lumiBarnes, lungExpression, MAQCsubset, MAQCsubsetILM, MetaGxBreast, MetaGxOvarian, miRNATarget, msd16s, mvoutData, Neve2006, PREDAsampledata, ProData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, pumadata, rcellminerData, RUVnormalizeData, SpikeInSubset, TCGAcrcmiRNA, TCGAcrcmRNA, tweeDEseqCountData, yeastCC, countTransformers, crmn, dGAselID, GWASbyCluster, heatmapFlex, InteRD, lmQCM, MM2Sdata, MMDvariance, propOverlap, statVisual importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, adSplit, affyILM, AgiMicroRna, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, attract, ballgown, BASiCS, BayesKnockdown, BgeeDB, biobroom, bioCancer, biocViews, BioNet, biosigner, biscuiteer, BiSeq, blima, bnem, bsseq, BubbleTree, CAFE, canceR, Cardinal, CellScore, CellTrails, CGHnormaliter, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, cicero, clipper, CluMSID, cn.mops, COCOA, coexnet, cogena, combi, CompoundDb, conclus, ConsensusClusterPlus, consensusDE, consensusOV, coRdon, CoreGx, crlmm, crossmeta, cummeRbund, cyanoFilter, cycle, cydar, CytoML, DAPAR, ddCt, debCAM, deco, DEGreport, DESeq2, destiny, DExMA, discordant, easyRNASeq, EBarrays, ecolitk, EGAD, ensembldb, EpiMix, erma, esetVis, ExiMiR, farms, ffpe, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpecs, flowStats, flowViz, flowWorkspace, FRASER, frma, frmaTools, gCrisprTools, gcrma, GCSscore, gemma.R, genbankr, geneClassifiers, GeneExpressionSignature, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GenomicSuperSignature, GEOsubmission, gep2pep, gespeR, ggbio, girafe, GISPA, GlobalAncova, globaltest, gmapR, GSRI, GSVA, Gviz, Harshlight, HEM, hermes, HTqPCR, HTSFilter, imageHTS, ImmuneSpaceR, infinityFlow, IsoformSwitchAnalyzeR, isomiRs, iterClust, katdetectr, kissDE, lapmix, LiquidAssociation, LRBaseDbi, maanova, MAGeCKFlute, makecdfenv, maSigPro, MAST, mBPCR, MeSHDbi, metaseqR2, MethylAid, methylCC, methylclock, methylumi, mfa, MiChip, microbiomeDASim, microbiomeMarker, MIGSA, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, MMAPPR2, mogsa, MoonlightR, MOSim, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, netZooR, NormalyzerDE, npGSEA, nucleR, oligoClasses, omicade4, omicsViewer, ontoProc, oposSOM, oppar, OrganismDbi, panp, phantasus, PharmacoGx, phemd, phenomis, phyloseq, piano, plethy, plgem, plier, podkat, prebs, PrInCE, proBatch, proFIA, progeny, pRoloc, PROMISE, PROPS, protGear, PSEA, psygenet2r, ptairMS, puma, PureCN, pvac, pvca, pwOmics, qcmetrics, QDNAseq, QFeatures, qpgraph, quantiseqr, quantro, QuasR, qusage, RadioGx, randPack, RIVER, Rmagpie, RNAinteract, rols, ropls, ROTS, rqubic, rScudo, Rtpca, Rtreemix, RUVnormalize, scmap, scTGIF, SeqVarTools, ShortRead, SigsPack, sigsquared, SimBindProfiles, singscore, sitadela, SomaticSignatures, SpatialDecon, spkTools, SPONGE, standR, STATegRa, subSeq, synapter, TEQC, TFBSTools, timecourse, TMixClust, TnT, topdownr, ToxicoGx, tradeSeq, traviz, TTMap, twilight, uSORT, VanillaICE, variancePartition, VariantAnnotation, VariantFiltering, VariantTools, vidger, vulcan, wateRmelon, wpm, xcms, Xeva, BloodCancerMultiOmics2017, ccTutorial, DeSousa2013, DExMAdata, Fletcher2013a, GSE13015, hgu133plus2CellScore, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, mcsurvdata, pRolocdata, RNAinteractMAPK, seqc, signatureSearchData, ExpHunterSuite, ExpressionNormalizationWorkflow, GeoMxWorkflows, AnnoProbe, bapred, BisqueRNA, CIARA, ClassComparison, ClassDiscovery, CSCDRNA, easyDifferentialGeneCoexpression, FMradio, geneExpressionFromGEO, IntegratedJM, IsoGene, maGUI, MetaIntegrator, nlcv, NMF, PerseusR, pulseTD, ragt2ridges, RobLox, RobLoxBioC, RPPanalyzer, seAMLess, ssizeRNA, TailRank suggestsMe: AUCell, BiocCheck, BiocGenerics, BiocOncoTK, BSgenome, CellMapper, cellTree, clustComp, coseq, DART, dcanr, dearseq, edgeR, EpiDISH, epivizr, epivizrChart, epivizrStandalone, farms, genefu, GENIE3, GenomicRanges, GSAR, GSgalgoR, Heatplus, interactiveDisplay, kebabs, les, limma, M3Drop, mCSEA, messina, msa, multiClust, OSAT, PCAtools, pkgDepTools, RcisTarget, ReactomeGSA, ROC, RTCGA, scater, scmeth, scran, SeqArray, sparrow, spatialHeatmap, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, tkWidgets, TypeInfo, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, ccTutorial, dorothea, dyebiasexamples, HMP16SData, HMP2Data, mammaPrintData, mAPKLData, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, clValid, CrossValidate, D4TAlink.light, distrDoc, dnet, GenAlgo, ggpicrust2, hexbin, HTSCluster, isatabr, mi4p, Modeler, multiclassPairs, NACHO, ordinalbayes, Patterns, pkgmaker, Platypus, seeker, Seurat, sigminer, SomaDataIO, SourceSet dependencyCount: 5 Package: biobroom Version: 1.30.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, rmarkdown, SummarizedExperiment License: LGPL MD5sum: 4e6af48650fc1466461a42e8d3e998c8 NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: RELEASE_3_16 git_last_commit: 5892f1d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biobroom_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biobroom_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biobroom_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biobroom_1.30.0.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP dependencyCount: 32 Package: biobtreeR Version: 1.10.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat,rmarkdown,markdown License: MIT + file LICENSE MD5sum: b2710bec73234c23adbcfa5342d15f5c NeedsCompilation: no Title: Using biobtree tool from R Description: The biobtreeR package provides an interface to [biobtree](https://github.com/tamerh/biobtree) tool which covers large set of bioinformatics datasets and allows search and chain mappings functionalities. biocViews: Annotation Author: Tamer Gur Maintainer: Tamer Gur URL: https://github.com/tamerh/biobtreeR VignetteBuilder: knitr BugReports: https://github.com/tamerh/biobtreeR/issues git_url: https://git.bioconductor.org/packages/biobtreeR git_branch: RELEASE_3_16 git_last_commit: 1c75efb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biobtreeR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biobtreeR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biobtreeR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biobtreeR_1.10.0.tgz vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html vignetteTitles: The biobtreeR users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R dependencyCount: 19 Package: bioCancer Version: 1.26.04 Depends: R (>= 3.6.0), radiant.data (>= 0.9.1), XML(>= 3.98) Imports: R.oo, httr, DT (>= 0.3), dplyr (>= 0.7.2), shiny (>= 1.0.5), AnnotationDbi, methods, Biobase, geNetClassifier, org.Hs.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(<= 1.01), visNetwork, htmlwidgets, plyr, tibble Suggests: BiocStyle, prettydoc, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: ca87b9777ddfa6dc66dc0f3f6018bebe NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: bioCancer is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: http://kmezhoud.github.io/bioCancer VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: RELEASE_3_16 git_last_commit: 6c0335a git_last_commit_date: 2023-02-03 Date/Publication: 2023-02-03 source.ver: src/contrib/bioCancer_1.26.04.tar.gz win.binary.ver: bin/windows/contrib/4.2/bioCancer_1.26.04.zip mac.binary.ver: bin/macosx/contrib/4.2/bioCancer_1.26.04.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bioCancer_1.26.04.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R dependencyCount: 225 Package: BiocBaseUtils Version: 1.0.0 Depends: R (>= 4.2.0) Imports: methods, utils Suggests: knitr, rmarkdown, BiocStyle, tinytest License: Artistic-2.0 MD5sum: 21d78a98e04513a6fc4d827b62d4f27c NeedsCompilation: no Title: General utility functions for developing Bioconductor packages Description: The package provides utility functions related to package development. These include functions that replace slots, and selectors for show methods. It aims to coalesce the various helper functions often re-used throughout the Bioconductor ecosystem. biocViews: Software, Infrastructure Author: Marcel Ramos [aut, cre] (), Martin Morgan [ctb], Hervé Pagès [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://www.github.com/Bioconductor/BiocBaseUtils/issues git_url: https://git.bioconductor.org/packages/BiocBaseUtils git_branch: RELEASE_3_16 git_last_commit: c6c2cde git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocBaseUtils_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocBaseUtils_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocBaseUtils_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocBaseUtils_1.0.0.tgz vignettes: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.html vignetteTitles: BiocBaseUtils Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocBaseUtils/inst/doc/BiocBaseUtils.R importsMe: BiocFHIR, DNAfusion, MultiAssayExperiment, TENxIO, UniProt.ws dependencyCount: 2 Package: BiocCheck Version: 1.34.3 Depends: R (>= 4.2.0) Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr, tools, codetools, methods, utils, knitr Suggests: RUnit, BiocGenerics, Biobase, jsonlite, rmarkdown, downloader, devtools (>= 1.4.1), usethis, BiocStyle, callr Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 2866b56dceebae29f889f435eca42a4c NeedsCompilation: no Title: Bioconductor-specific package checks Description: BiocCheck guides maintainers through Bioconductor best practicies. It runs Bioconductor-specific package checks by searching through package code, examples, and vignettes. Maintainers are required to address all errors, warnings, and most notes produced. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [aut, cre] (), Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Marcel Ramos URL: https://github.com/Bioconductor/BiocCheck VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocCheck/issues git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_16 git_last_commit: 58b4a85 git_last_commit_date: 2023-03-03 Date/Publication: 2023-03-03 source.ver: src/contrib/BiocCheck_1.34.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocCheck_1.34.3.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocCheck_1.34.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocCheck_1.34.3.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: AnnotationHubData suggestsMe: GEOfastq, packFinder, preciseTAD, SpectralTAD, HMP16SData, HMP2Data, scpdata, MainExistingDatasets dependencyCount: 33 Package: BiocDockerManager Version: 1.10.0 Depends: R (>= 4.1) Imports: httr, whisker, readr, dplyr, utils, methods, memoise Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 0c2b72ec357a5c718b81882e23bfc2ea NeedsCompilation: no Title: Access Bioconductor docker images Description: Package works analogous to BiocManager but for docker images. Use the BiocDockerManager package to install and manage docker images provided by the Bioconductor project. A convenient package to install images, update images and find which Bioconductor based docker images are available. biocViews: Software, Infrastructure, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Nitesh Turaga [aut] Maintainer: Bioconductor Package Maintainer SystemRequirements: docker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocDockerManager/issues git_url: https://git.bioconductor.org/packages/BiocDockerManager git_branch: RELEASE_3_16 git_last_commit: 4f4314a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocDockerManager_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocDockerManager_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocDockerManager_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocDockerManager_1.10.0.tgz vignettes: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.html vignetteTitles: BiocDockerManager Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.R dependencyCount: 44 Package: BiocFHIR Version: 1.0.0 Depends: R (>= 4.2) Imports: DT, shiny, jsonlite, graph, tidyr, visNetwork, igraph, utils, methods, BiocBaseUtils Suggests: knitr, testthat, rjsoncons License: Artistic-2.0 MD5sum: 40e5e24e20ae1bfcffc0dddbfcec198a NeedsCompilation: no Title: Illustration of FHIR ingestion and transformation using R Description: FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations. biocViews: Infrastructure, DataImport, DataRepresentation Author: Vincent Carey [aut, cre] Maintainer: Vincent Carey URL: https://github.com/vjcitn/BiocFHIR VignetteBuilder: knitr BugReports: https://github.com/vjcitn/BiocFHIR/issues git_url: https://git.bioconductor.org/packages/BiocFHIR git_branch: RELEASE_3_16 git_last_commit: c23d2dc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocFHIR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocFHIR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocFHIR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocFHIR_1.0.0.tgz vignettes: vignettes/BiocFHIR/inst/doc/A_upper.html, vignettes/BiocFHIR/inst/doc/B_handling.html, vignettes/BiocFHIR/inst/doc/BiocFHIR.html, vignettes/BiocFHIR/inst/doc/C_tables.html, vignettes/BiocFHIR/inst/doc/D_linking.html vignetteTitles: Upper level FHIR concepts, Handling FHIR documents with BiocFHIR, BiocFHIR -- infrastructure for parsing and analyzing FHIR data, Transforming FHIR documents to tables with BiocFHIR, Linking information between FHIR resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFHIR/inst/doc/A_upper.R, vignettes/BiocFHIR/inst/doc/B_handling.R, vignettes/BiocFHIR/inst/doc/BiocFHIR.R, vignettes/BiocFHIR/inst/doc/C_tables.R, vignettes/BiocFHIR/inst/doc/D_linking.R dependencyCount: 71 Package: BiocFileCache Version: 2.6.1 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs, filelock, curl, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: a833918abe25f22c0c82836382027c1a NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: RELEASE_3_16 git_last_commit: fdeb0ad git_last_commit_date: 2023-02-17 Date/Publication: 2023-02-17 source.ver: src/contrib/BiocFileCache_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocFileCache_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocFileCache_2.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocFileCache_2.6.1.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: BiocFileCache: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, ExperimentHub, RcwlPipelines, JASPAR2022, scATAC.Explorer, TMExplorer, csawBook, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.workflows, SingleRBook importsMe: AMARETTO, atSNP, autonomics, BayesSpace, BiocPkgTools, biodb, biomaRt, BrainSABER, brendaDb, bugsigdbr, cbaf, cBioPortalData, CellBench, conclus, CTDquerier, customCMPdb, dasper, easyRNASeq, enhancerHomologSearch, EnrichmentBrowser, EpiTxDb, fgga, GenomicScores, GenomicSuperSignature, GSEABenchmarkeR, gwascat, hca, MBQN, NxtIRFcore, ODER, ontoProc, Organism.dplyr, PhIPData, psichomics, recount3, recountmethylation, regutools, rpx, sesame, signeR, SpatialExperiment, spatialHeatmap, SpliceWiz, terraTCGAdata, TFutils, tomoseqr, tximeta, UMI4Cats, UniProt.ws, waddR, org.Mxanthus.db, PANTHER.db, BioPlex, HiContactsData, MicrobiomeBenchmarkData, NxtIRFdata, SFEData, SingleCellMultiModal, spatialLIBD, SingscoreAMLMutations suggestsMe: AnnotationForge, bambu, BiocOncoTK, BiocSet, EpiCompare, fastreeR, FLAMES, GRaNIE, HiCDCPlus, HumanTranscriptomeCompendium, MethReg, Nebulosa, progeny, qsvaR, seqsetvis, structToolbox, TCGAutils, TREG, zellkonverter, emtdata, HighlyReplicatedRNASeq, MethylSeqData, msigdb, scRNAseq, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell dependencyCount: 46 Package: BiocGenerics Version: 0.44.0 Depends: R (>= 4.0.0), methods, utils, graphics, stats Imports: methods, utils, graphics, stats Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: 9438855e3f8bb24cecc7588c519ec8dd NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines many S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BiocGenerics 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NutrienTrackeR, openSkies, pagoda2, polyRAD, Rediscover, Seurat dependencyCount: 4 Package: biocGraph Version: 1.60.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 5b20d0b6430960a5202ae6caff59cd89 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: RELEASE_3_16 git_last_commit: ee8dbd0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biocGraph_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biocGraph_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biocGraph_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biocGraph_1.60.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: EnrichmentBrowser dependencyCount: 55 Package: BiocIO Version: 1.8.0 Depends: R (>= 4.0) Imports: BiocGenerics, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 11cb131776448affdac365361d827d64 NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: The `BiocIO` package contains high-level abstract classes and generics used by developers to build IO funcionality within the Bioconductor suite of packages. Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`. biocViews: Annotation,DataImport Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: RELEASE_3_16 git_last_commit: 4a719fa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocIO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocIO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocIO_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocIO_1.8.0.tgz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R dependsOnMe: HelloRanges, LoomExperiment importsMe: BiocSet, extraChIPs, GenomicFeatures, rtracklayer, TENxIO dependencyCount: 8 Package: BiocNeighbors Version: 1.16.0 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: x64 MD5sum: 6d64703d14ad67a79306f3ab5f73b8d4 NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: RELEASE_3_16 git_last_commit: 3b227be git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocNeighbors_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocNeighbors_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocNeighbors_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocNeighbors_1.16.0.tgz vignettes: vignettes/BiocNeighbors/inst/doc/approx.html, vignettes/BiocNeighbors/inst/doc/exact.html, vignettes/BiocNeighbors/inst/doc/range.html vignetteTitles: 2. Detecting approximate nearest neighbors, 1. Detecting exact nearest neighbors, 3. Detecting neighbors within range hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/approx.R, vignettes/BiocNeighbors/inst/doc/exact.R, vignettes/BiocNeighbors/inst/doc/range.R dependsOnMe: OSCA.advanced, OSCA.workflows, SingleRBook importsMe: batchelor, bluster, CellMixS, cydar, imcRtools, miloR, mumosa, scater, scDblFinder, UCell suggestsMe: TrajectoryUtils, TSCAN linksToMe: SingleR dependencyCount: 23 Package: BiocOncoTK Version: 1.18.0 Depends: R (>= 3.6.0), methods, utils Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, stats, httr, rjson, dplyr, magrittr, grid, DT, GenomicRanges, IRanges, ggplot2, SummarizedExperiment, DBI, GenomicFeatures, curatedTCGAData, scales, ggpubr, plyr, car, graph, Rgraphviz Suggests: knitr, dbplyr, org.Hs.eg.db, MultiAssayExperiment, BiocStyle, ontoProc, ontologyPlot, pogos, GenomeInfoDb, restfulSE (>= 1.3.7), BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, Biobase, TxDb.Hsapiens.UCSC.hg18.knownGene, reshape2, testthat, AnnotationDbi, FDb.InfiniumMethylation.hg19, EnsDb.Hsapiens.v75, rmarkdown, rhdf5client License: Artistic-2.0 MD5sum: 24941b10e4a5813d968e902ee2db5d9f NeedsCompilation: no Title: Bioconductor components for general cancer genomics Description: Provide a central interface to various tools for genome-scale analysis of cancer studies. biocViews: CopyNumberVariation, CpGIsland, DNAMethylation, GeneExpression, GeneticVariability, SNP, Transcription, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocOncoTK git_branch: RELEASE_3_16 git_last_commit: bd29ac7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocOncoTK_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocOncoTK_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocOncoTK_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocOncoTK_1.18.0.tgz vignettes: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.html, vignettes/BiocOncoTK/inst/doc/curatedMSIData.html, vignettes/BiocOncoTK/inst/doc/maptcga.html vignetteTitles: BiocOncoTK -- cancer oriented components for Bioconductor, curatedMSIData overview, "Mapping TCGA tumor codes to NCIT" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R, vignettes/BiocOncoTK/inst/doc/curatedMSIData.R, vignettes/BiocOncoTK/inst/doc/maptcga.R dependencyCount: 203 Package: BioCor Version: 1.22.0 Depends: R (>= 3.4.0) Imports: BiocParallel, GSEABase, Matrix, methods Suggests: airway, BiocStyle, boot, DESeq2, GOSemSim, Hmisc, knitr (>= 1.35), org.Hs.eg.db, reactome.db, rmarkdown, spelling, targetscan.Hs.eg.db, testthat, WGCNA License: MIT + file LICENSE MD5sum: 8bfdf170ccf9c7028c607ed1016d7227 NeedsCompilation: no Title: Functional similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (), Pau Sancho-Bru [ths] (), Juan José Salvatella Lozano [ths] () Maintainer: Lluís Revilla Sancho URL: https://bioconductor.org/packages/BioCor, https://llrs.github.io/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: RELEASE_3_16 git_last_commit: 4493ae5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioCor_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioCor_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioCor_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioCor_1.22.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 63 Package: BiocParallel Version: 1.32.6 Depends: methods, R (>= 3.5.0) Imports: stats, utils, futile.logger, parallel, snow, codetools LinkingTo: BH, cpp11 Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, RUnit, BiocStyle, knitr, batchtools, data.table License: GPL-2 | GPL-3 Archs: x64 MD5sum: 5cfc8676932a40a575f94ebe420c0366 NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Martin Morgan [aut, cre], Jiefei Wang [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb], Henrik Bengtsson [ctb], Madelyn Carlson [ctb] (Translated 'Random Numbers' vignette from Sweave to RMarkdown / HTML.), Phylis Atieno [ctb] (Translated 'Introduction to BiocParallel' vignette from Sweave to Rmarkdown / HTML.) Maintainer: Martin Morgan URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_16 git_last_commit: 994f4e7 git_last_commit_date: 2023-03-17 Date/Publication: 2023-03-17 source.ver: src/contrib/BiocParallel_1.32.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocParallel_1.32.6.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocParallel_1.32.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocParallel_1.32.6.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.html, vignettes/BiocParallel/inst/doc/Random_Numbers.html vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, Introduction to BiocParallel, 4. 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This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [ctb], Felix G.M. Ernst [ctb], Jennifer Wokaty [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: RELEASE_3_16 git_last_commit: d8c2727 git_last_commit_date: 2023-02-27 Date/Publication: 2023-02-28 source.ver: src/contrib/BiocPkgTools_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocPkgTools_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocPkgTools_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocPkgTools_1.16.1.tgz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R dependencyCount: 109 Package: BiocSet Version: 1.12.1 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO, AnnotationDbi, KEGGREST, ontologyIndex, tidyr Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: c169d42e903c3a4fab052e1b0c2e2677 NeedsCompilation: no Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. 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Where possible, parallelization is achieved using the BiocParallel framework. biocViews: Software, DimensionReduction, PrincipalComponent Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/BiocSingular SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: RELEASE_3_16 git_last_commit: 6dc42b3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocSingular_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocSingular_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocSingular_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocSingular_1.14.0.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R dependsOnMe: compartmap, OSCA.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows importsMe: batchelor, BayesSpace, clusterExperiment, DelayedTensor, Dino, GSVA, miloR, mumosa, NanoMethViz, NewWave, PCAtools, scater, scDblFinder, scMerge, scran, scry, SingleR, velociraptor suggestsMe: ResidualMatrix, ScaledMatrix, spatialHeatmap, splatter, Voyager, HCAData dependencyCount: 30 Package: BiocSklearn Version: 1.20.1 Depends: R (>= 4.0), reticulate, methods, SummarizedExperiment Imports: basilisk Suggests: testthat, restfulSE, HDF5Array, BiocStyle, rmarkdown, knitr License: Artistic-2.0 MD5sum: c9a9ac896c43b8848a8bd06d946285c7 NeedsCompilation: no Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey [cre, aut] Maintainer: Vince Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSklearn git_branch: RELEASE_3_16 git_last_commit: 7dd118f git_last_commit_date: 2022-12-26 Date/Publication: 2022-12-26 source.ver: src/contrib/BiocSklearn_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocSklearn_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocSklearn_1.20.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocSklearn_1.19.13.tgz vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html vignetteTitles: BiocSklearn overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R dependencyCount: 39 Package: BiocStyle Version: 2.26.0 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: 226b6f4cd70b8cbc70f3a0fb7c0c5039 NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś [aut] (), Mike Smith [ctb] (), Martin Morgan [ctb], Wolfgang Huber [ctb], Bioconductor Package [cre] Maintainer: Bioconductor Package URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: RELEASE_3_16 git_last_commit: add0354 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocStyle_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocStyle_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocStyle_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocStyle_2.26.0.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: 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NetActivityData, OnassisJavaLibs, optimalFlowData, parathyroidSE, pasilla, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, preciseTADhub, ptairData, rcellminerData, RforProteomics, RGMQLlib, RLHub, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, scanMiRData, scATAC.Explorer, SCLCBam, scpdata, scRNAseq, SimBenchData, Single.mTEC.Transcriptomes, SingleCellMultiModal, spatialLIBD, STexampleData, systemPipeRdata, TabulaMurisData, TabulaMurisSenisData, tartare, TCGAbiolinksGUI.data, TENxBrainData, TENxBUSData, TENxPBMCData, TENxVisiumData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, tuberculosis, VariantToolsData, VectraPolarisData, WeberDivechaLCdata, zebrafishRNASeq, annotation, arrays, BiocMetaWorkflow, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, recountWorkflow, RNAseq123, sequencing, SingscoreAMLMutations, variants, aIc, asteRisk, bmstdr, BOSO, cyjShiny, EHRtemporalVariability, ggBubbles, ggcoverage, i2dash, IFAA, MACP, magmaR, metabolomicsR, MetaIntegrator, multiclassPairs, MVN, net4pg, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, rjsoncons, rworkflows, SNPassoc, SourceSet dependencyCount: 38 Package: biocthis Version: 1.8.3 Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1) Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR, rmarkdown, sessioninfo, testthat, utils License: Artistic-2.0 MD5sum: 9b2cc700b44fedafffb057b2805d2dd7 NeedsCompilation: no Title: Automate package and project setup for Bioconductor packages Description: This package expands the usethis package with the goal of helping automate the process of creating R packages for Bioconductor or making them Bioconductor-friendly. biocViews: Software, ReportWriting Author: Leonardo Collado-Torres [aut, cre] (), Marcel Ramos [ctb] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/biocthis git_url: https://git.bioconductor.org/packages/biocthis git_branch: RELEASE_3_16 git_last_commit: a815ce3 git_last_commit_date: 2023-03-10 Date/Publication: 2023-03-12 source.ver: src/contrib/biocthis_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/biocthis_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.2/biocthis_1.8.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biocthis_1.8.3.tgz vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html, vignettes/biocthis/inst/doc/biocthis.html vignetteTitles: biocthis developer notes, Introduction to biocthis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R, vignettes/biocthis/inst/doc/biocthis.R importsMe: HubPub suggestsMe: tripr dependencyCount: 44 Package: BiocVersion Version: 3.16.0 Depends: R (>= 4.2.0) License: Artistic-2.0 MD5sum: 93a3b4f34283a2ed8a199e8d3912f037 NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: master git_last_commit: c681e06 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BiocVersion_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocVersion_3.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocVersion_3.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocVersion_3.16.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub, pkgndep suggestsMe: BiocManager dependencyCount: 0 Package: biocViews Version: 1.66.3 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, knitr, commonmark, BiocStyle License: Artistic-2.0 MD5sum: e2f04a3df7eafbf3591f4e66e08a2296 NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora, Lori Shepherd Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/BiocViews VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_16 git_last_commit: b6995ff git_last_commit_date: 2023-03-06 Date/Publication: 2023-03-06 source.ver: src/contrib/biocViews_1.66.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/biocViews_1.66.3.zip mac.binary.ver: bin/macosx/contrib/4.2/biocViews_1.66.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biocViews_1.66.3.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.html, vignettes/biocViews/inst/doc/HOWTO-BCV.html vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R dependsOnMe: Risa importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, monocle, sigFeature, RforProteomics suggestsMe: packFinder dependencyCount: 16 Package: BiocWorkflowTools Version: 1.24.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: ba2a60c07b35fce25fd357769bc19fda NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: RELEASE_3_16 git_last_commit: 90ca9d7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiocWorkflowTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiocWorkflowTools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiocWorkflowTools_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiocWorkflowTools_1.24.0.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependsOnMe: RNAseq123 suggestsMe: BiocMetaWorkflow, CAGEWorkflow, recountWorkflow, SingscoreAMLMutations dependencyCount: 62 Package: biodb Version: 1.6.1 Depends: R (>= 4.1.0) Imports: BiocFileCache, R6, RCurl, RSQLite, Rcpp, XML, chk, jsonlite, lgr, lifecycle, methods, openssl, plyr, progress, rappdirs, stats, stringr, tools, withr, yaml LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, xml2, git2r License: AGPL-3 Archs: x64 MD5sum: e41073d023fab0a0c8e63a4d70deb20f NeedsCompilation: yes Title: biodb, a library and a development framework for connecting to chemical and biological databases Description: The biodb package provides access to standard remote chemical and biological databases (ChEBI, KEGG, HMDB, ...), as well as to in-house local database files (CSV, SQLite), with easy retrieval of entries, access to web services, search of compounds by mass and/or name, and mass spectra matching for LCMS and MSMS. Its architecture as a development framework facilitates the development of new database connectors for local projects or inside separate published packages. biocViews: Software, Infrastructure, DataImport, KEGG Author: Pierrick Roger [aut, cre] (), Alexis Delabrière [ctb] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodb/issues git_url: https://git.bioconductor.org/packages/biodb git_branch: RELEASE_3_16 git_last_commit: 585246e git_last_commit_date: 2022-11-22 Date/Publication: 2022-11-25 source.ver: src/contrib/biodb_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodb_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/biodb_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodb_1.6.1.tgz vignettes: vignettes/biodb/inst/doc/biodb.html, vignettes/biodb/inst/doc/details.html, vignettes/biodb/inst/doc/entries.html, vignettes/biodb/inst/doc/new_connector.html, vignettes/biodb/inst/doc/new_entry_field.html vignetteTitles: Introduction to the biodb package., Details on general *biodb* usage and principles, Manipulating entry objects, Creating a new connector class for accessing a database., Creating a new field for entries. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodb/inst/doc/biodb.R, vignettes/biodb/inst/doc/details.R, vignettes/biodb/inst/doc/entries.R, vignettes/biodb/inst/doc/new_connector.R, vignettes/biodb/inst/doc/new_entry_field.R importsMe: biodbChebi, biodbExpasy, biodbHmdb, biodbKegg, biodbLipidmaps, biodbMirbase, biodbNcbi, biodbNci, biodbUniprot, phenomis dependencyCount: 75 Package: biodbChebi Version: 1.4.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.1.5) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr License: AGPL-3 MD5sum: 0582144179a30a8feb6f001330e8ce80 NeedsCompilation: no Title: biodbChebi, a library for connecting to the ChEBI Database Description: The biodbChebi library provides access to the ChEBI Database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name, mass or other fields. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbChebi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbChebi/issues git_url: https://git.bioconductor.org/packages/biodbChebi git_branch: RELEASE_3_16 git_last_commit: 4525e08 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbChebi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbChebi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbChebi_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbChebi_1.4.0.tgz vignettes: vignettes/biodbChebi/inst/doc/biodbChebi.html vignetteTitles: Introduction to the biodbChebi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbChebi/inst/doc/biodbChebi.R importsMe: phenomis dependencyCount: 76 Package: biodbExpasy Version: 1.2.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, stringr, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 29971b9c59da9baecd54e391f604e00c NeedsCompilation: no Title: biodbExpasy, a library for connecting to Expasy ENZYME database. Description: The biodbExpasy library provides access to Expasy ENZYME database, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name or comments. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbExpasy git_branch: RELEASE_3_16 git_last_commit: ef6c7ee git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbExpasy_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbExpasy_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbExpasy_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbExpasy_1.2.0.tgz vignettes: vignettes/biodbExpasy/inst/doc/biodbExpasy.html vignetteTitles: Introduction to the biodbExpasy package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbExpasy/inst/doc/biodbExpasy.R dependencyCount: 76 Package: biodbHmdb Version: 1.4.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.3.2), Rcpp LinkingTo: Rcpp, testthat Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: x64 MD5sum: 4c625a5697d02e9ec649e24c689175fa NeedsCompilation: yes Title: biodbHmdb, a library for connecting to the HMDB Database Description: The biodbHmdb library is an extension of the biodb framework package that provides access to the HMDB Metabolites database. It allows to download the whole HMDB Metabolites database locally, access entries and search for entries by name or description. A future version of this package will also include a search by mass and mass spectra annotation. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbHmdb VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbHmdb/issues git_url: https://git.bioconductor.org/packages/biodbHmdb git_branch: RELEASE_3_16 git_last_commit: df94f3a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbHmdb_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbHmdb_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbHmdb_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbHmdb_1.4.0.tgz vignettes: vignettes/biodbHmdb/inst/doc/biodbHmdb.html vignetteTitles: Introduction to the biodbHmdb package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbHmdb/inst/doc/biodbHmdb.R dependencyCount: 76 Package: biodbKegg Version: 1.4.0 Depends: R (>= 4.1) Imports: R6, biodb (>= 1.4.2), chk, lifecycle Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, igraph, magick, lgr License: AGPL-3 MD5sum: bc718cc608d2b6192a74f74cef7c0757 NeedsCompilation: no Title: biodbKegg, a library for connecting to the KEGG Database Description: The biodbKegg library is an extension of the biodb framework package that provides access to the KEGG databases Compound, Enzyme, Genes, Module, Orthology and Reaction. It allows to retrieve entries by their accession numbers. Web services like "find", "list" and "findExactMass" are also available. Some functions for navigating along the pathways have also been implemented. biocViews: Software, Infrastructure, DataImport, Pathways, KEGG Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbKegg VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbKegg/issues git_url: https://git.bioconductor.org/packages/biodbKegg git_branch: RELEASE_3_16 git_last_commit: 824ec0d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbKegg_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbKegg_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbKegg_1.4.0.tgz vignettes: vignettes/biodbKegg/inst/doc/biodbKegg.html vignetteTitles: Introduction to the biodbKegg package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbKegg/inst/doc/biodbKegg.R dependencyCount: 76 Package: biodbLipidmaps Version: 1.4.1 Depends: R (>= 4.1) Imports: biodb (>= 1.3.2), lifecycle, R6 Suggests: BiocStyle, lgr, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, covr License: AGPL-3 MD5sum: 577c1bc6ee30f4e4731dad075d634848 NeedsCompilation: no Title: biodbLipidmaps, a library for connecting to the Lipidmaps Structure database Description: The biodbLipidmaps library provides access to the Lipidmaps Structure Database, using biodb package framework. It allows to retrieve entries by their accession number, and run web the services lmsdSearch and lmsdRecord. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbLipidmaps VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbLipidmaps/issues git_url: https://git.bioconductor.org/packages/biodbLipidmaps git_branch: RELEASE_3_16 git_last_commit: 75afc75 git_last_commit_date: 2022-12-01 Date/Publication: 2022-12-01 source.ver: src/contrib/biodbLipidmaps_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbLipidmaps_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbLipidmaps_1.4.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbLipidmaps_1.4.1.tgz vignettes: vignettes/biodbLipidmaps/inst/doc/biodbLipidmaps.html vignetteTitles: An introduction to biodbLipidmaps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbLipidmaps/inst/doc/biodbLipidmaps.R dependencyCount: 76 Package: biodbMirbase Version: 1.2.2 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, stringr, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 6fe516069de42d9210072f74bc544619 NeedsCompilation: no Title: biodbMirbase, a library for connecting to miRBase mature database Description: The biodbMirbase library is an extension of the biodb framework package, that provides access to miRBase mature database. It allows to retrieve entries by their accession number, and run specific web services. Description: The biodbMirbase library provides access to the miRBase Database, using biodb package framework. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbMirbase git_branch: RELEASE_3_16 git_last_commit: 993efd2 git_last_commit_date: 2023-01-09 Date/Publication: 2023-01-09 source.ver: src/contrib/biodbMirbase_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbMirbase_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbMirbase_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbMirbase_1.2.2.tgz vignettes: vignettes/biodbMirbase/inst/doc/biodbMirbase.html vignetteTitles: Introduction to the biodbMirbase package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbMirbase/inst/doc/biodbMirbase.R dependencyCount: 76 Package: biodbNcbi Version: 1.2.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.2), R6, XML, chk Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 MD5sum: 5bb846259fad18642609b9b73dd79bc0 NeedsCompilation: no Title: biodbNcbi, a library for connecting to NCBI Databases. Description: The biodbNcbi library provides access to the NCBI databases CCDS, Gene, Pubchem Comp and Pubchem Subst, using biodb package framework. It allows to retrieve entries by their accession number. Web services can be accessed for searching the database by name or mass. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbNcbi VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbNCbi/issues git_url: https://git.bioconductor.org/packages/biodbNcbi git_branch: RELEASE_3_16 git_last_commit: 987e49c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbNcbi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbNcbi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbNcbi_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbNcbi_1.2.0.tgz vignettes: vignettes/biodbNcbi/inst/doc/biodbNcbi.html vignetteTitles: Introduction to the biodbNcbi package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbNcbi/inst/doc/biodbNcbi.R dependencyCount: 76 Package: biodbNci Version: 1.2.0 Depends: R (>= 4.1) Imports: biodb (>= 1.3.1), R6, Rcpp, chk LinkingTo: Rcpp, testthat Suggests: roxygen2, BiocStyle, testthat (>= 2.0.0), devtools, knitr, rmarkdown, covr, lgr License: AGPL-3 Archs: x64 MD5sum: 3d48581ac261e785f8f20899598acffa NeedsCompilation: yes Title: biodbNci, a library for connecting to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database Description: The biodbNci library is an extension of the biodb framework package. It provides access to biodbNci, a library for connecting to the National Cancer Institute (USA) CACTUS Database. It allows to retrieve entries by their accession number, and run specific web services. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biodbNci git_branch: RELEASE_3_16 git_last_commit: 768ea26 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbNci_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbNci_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbNci_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbNci_1.2.0.tgz vignettes: vignettes/biodbNci/inst/doc/biodbNci.html vignetteTitles: Introduction to the biodbNci package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbNci/inst/doc/biodbNci.R dependencyCount: 76 Package: biodbUniprot Version: 1.4.0 Depends: R (>= 4.1.0) Imports: R6, biodb (>= 1.4.2) Suggests: BiocStyle, roxygen2, devtools, testthat (>= 2.0.0), knitr, rmarkdown, lgr, covr License: AGPL-3 MD5sum: 343929da9c149f3bef983f1b4c1695c6 NeedsCompilation: no Title: biodbUniprot, a library for connecting to the Uniprot Database Description: The biodbUniprot library is an extension of the biodb framework package. It provides access to the UniProt database. It allows to retrieve entries by their accession number, and run web service queries for searching for entries. biocViews: Software, Infrastructure, DataImport Author: Pierrick Roger [aut, cre] () Maintainer: Pierrick Roger URL: https://github.com/pkrog/biodbUniprot VignetteBuilder: knitr BugReports: https://github.com/pkrog/biodbUniprot/issues git_url: https://git.bioconductor.org/packages/biodbUniprot git_branch: RELEASE_3_16 git_last_commit: 15ae9f4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biodbUniprot_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biodbUniprot_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biodbUniprot_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biodbUniprot_1.4.0.tgz vignettes: vignettes/biodbUniprot/inst/doc/biodbUniprot.html vignetteTitles: Introduction to the biodbUniprot package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biodbUniprot/inst/doc/biodbUniprot.R dependencyCount: 76 Package: bioDist Version: 1.70.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: ae3f92ffb044d8ddd7b79c40a25f91cb NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: RELEASE_3_16 git_last_commit: 78dc18e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bioDist_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bioDist_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bioDist_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bioDist_1.70.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile dependencyCount: 7 Package: biomaRt Version: 2.54.1 Depends: methods Imports: utils, XML (>= 3.99-0.7), AnnotationDbi, progress, stringr, httr, digest, BiocFileCache, rappdirs, xml2 Suggests: BiocStyle, knitr, mockery, rmarkdown, testthat, webmockr License: Artistic-2.0 MD5sum: 8838a78b5a25b7e5b74d26cf1b8a2403 NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintain by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb, cre] () Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biomaRt git_branch: RELEASE_3_16 git_last_commit: d5412fb git_last_commit_date: 2023-03-20 Date/Publication: 2023-03-20 source.ver: src/contrib/biomaRt_2.54.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/biomaRt_2.54.1.zip mac.binary.ver: bin/macosx/contrib/4.2/biomaRt_2.54.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biomaRt_2.54.1.tgz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/accessing_other_marts.html vignetteTitles: Accessing Ensembl annotation with biomaRt, Using a BioMart other than Ensembl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/accessing_other_marts.R dependsOnMe: BrainSABER, chromPlot, coMET, customProDB, DrugVsDisease, genefu, GenomicOZone, MineICA, NetSAM, PPInfer, RepViz, VegaMC, annotation importsMe: ArrayExpressHTS, ASpediaFI, BadRegionFinder, BgeeCall, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, conclus, dagLogo, DEXSeq, DominoEffect, easyRNASeq, EDASeq, ELMER, EpiMix, epimutacions, FRASER, GDCRNATools, GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet, GOexpress, goSTAG, GRaNIE, Gviz, hermes, InterCellar, isobar, LACE, mCSEA, MEDIPS, MetaboSignal, metaseqR2, MGFR, MouseFM, OncoScore, oposSOM, ORFik, pcaExplorer, phenoTest, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, recoup, rgsepd, RIPAT, scPipe, seq2pathway, SeqGSEA, sitadela, SPLINTER, SPONGE, surfaltr, SWATH2stats, TCGAbiolinks, TEKRABber, TFEA.ChIP, TimiRGeN, transcriptogramer, trena, ViSEAGO, yarn, ExpHunterSuite, biomartr, BioVenn, convertid, DiNAMIC.Duo, GeSciLiVis, GOxploreR, kangar00, liayson, MantaID, seeker, snplist, utr.annotation suggestsMe: AnnotationForge, bioassayR, celda, cellTree, chromstaR, ClusterJudge, CNVgears, crisprDesign, cTRAP, epistack, fedup, FELLA, h5vc, MAGeCKFlute, martini, massiR, MethReg, MineICA, MiRaGE, MutationalPatterns, netSmooth, oligo, OmnipathR, OrganismDbi, piano, Pigengene, progeny, R3CPET, Rcade, RnBeads, rTRM, scater, ShortRead, SIM, sincell, SummarizedBenchmark, trackViewer, wiggleplotr, zinbwave, BloodCancerMultiOmics2017, ccTutorial, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, DGEobj, DGEobj.utils, dnapath, grandR, MoBPS, Patterns, Platypus, R.SamBada, scDiffCom, SNPassoc dependencyCount: 69 Package: biomformat Version: 1.26.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 MD5sum: 021924c181e047394aace9efd7d498f8 NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: RELEASE_3_16 git_last_commit: f851ba2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biomformat_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biomformat_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biomformat_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biomformat_1.26.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: animalcules, microbiomeExplorer, microbiomeMarker, phyloseq suggestsMe: metagenomeSeq, mia, MicrobiotaProcess, metacoder dependencyCount: 14 Package: BioMM Version: 1.14.0 Depends: R (>= 3.6) Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms, precrec, nsprcomp, ranger, e1071, ggplot2, vioplot, CMplot, imager, topGO, xlsx Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 181ed1b7245517d5dc0a2c0ad75cd855 NeedsCompilation: no Title: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data Description: The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information. biocViews: Genetics, Classification, Regression, Pathways, GO, Software Author: Junfang Chen and Emanuel Schwarz Maintainer: Junfang Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioMM git_branch: RELEASE_3_16 git_last_commit: 5ba232e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioMM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioMM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioMM_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioMM_1.14.0.tgz vignettes: vignettes/BioMM/inst/doc/BioMMtutorial.html vignetteTitles: BioMMtutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMM/inst/doc/BioMMtutorial.R dependencyCount: 163 Package: BioMVCClass Version: 1.66.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 63143097972f2eff40345d1bbb5550bf NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: RELEASE_3_16 git_last_commit: 964bec3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioMVCClass_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioMVCClass_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioMVCClass_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioMVCClass_1.66.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: biomvRCNS Version: 1.38.0 Depends: R (>= 3.5.0), IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: x64 MD5sum: 890c20df1dbdd0c83b8b6b833ba18871 NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: RELEASE_3_16 git_last_commit: 911a52a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biomvRCNS_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biomvRCNS_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biomvRCNS_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biomvRCNS_1.38.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 154 Package: BioNAR Version: 1.0.0 Depends: R (>= 3.5.0), igraph, poweRlaw, latex2exp, RSpectra, Rdpack Imports: stringr, synaptome.db, clusterCons, fgsea, grid, methods, scales, AnnotationDbi, dplyr, GO.db, org.Hs.eg.db, rSpectral, WGCNA, ggplot2, ggrepel Suggests: knitr, rmarkdown, igraphdata, testthat (>= 3.0.0), vdiffr, devtools, pander, plotly, randomcoloR License: Artistic-2.0 MD5sum: 59af065d7e33a18ae629d5b805b732f9 NeedsCompilation: no Title: Biological Network Analysis in R Description: the R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms. biocViews: Software, GraphAndNetwork, Network Author: Colin Mclean [aut], Anatoly Sorokin [aut, cre], Oksana Sorokina [aut], J. Douglas Armstrong [aut, fnd], T. Ian Simpson [ctb, fnd] Maintainer: Anatoly Sorokin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioNAR git_branch: RELEASE_3_16 git_last_commit: f9c6c3c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioNAR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNAR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNAR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioNAR_1.0.0.tgz vignettes: vignettes/BioNAR/inst/doc/BioNAR_overview.html vignetteTitles: BioNAR_overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNAR/inst/doc/BioNAR_overview.R dependencyCount: 165 Package: BioNERO Version: 1.6.1 Depends: R (>= 4.1) Imports: WGCNA, dynamicTreeCut, matrixStats, sva, RColorBrewer, ComplexHeatmap, ggplot2, ggrepel, patchwork, reshape2, igraph, ggnetwork, intergraph, networkD3, ggnewscale, NetRep, stats, grDevices, graphics, utils, methods, BiocParallel, minet, GENIE3, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0), BiocStyle, DESeq2, covr License: GPL-3 MD5sum: 85e8ef0188240b7d9d0fe514d712b7c9 NeedsCompilation: no Title: Biological Network Reconstruction Omnibus Description: BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks. biocViews: Software, GeneExpression, GeneRegulation, SystemsBiology, GraphAndNetwork, Preprocessing, Network Author: Fabricio Almeida-Silva [cre, aut] (), Thiago Venancio [aut] () Maintainer: Fabricio Almeida-Silva URL: https://github.com/almeidasilvaf/BioNERO VignetteBuilder: knitr BugReports: https://github.com/almeidasilvaf/BioNERO/issues git_url: https://git.bioconductor.org/packages/BioNERO git_branch: RELEASE_3_16 git_last_commit: af9f87a git_last_commit_date: 2023-03-23 Date/Publication: 2023-03-24 source.ver: src/contrib/BioNERO_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNERO_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNERO_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioNERO_1.6.1.tgz vignettes: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.html, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.html, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.html vignetteTitles: Gene coexpression network inference, Gene regulatory network inference with BioNERO, Network comparison: consensus modules and module preservation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNERO/inst/doc/vignette_01_GCN_inference.R, vignettes/BioNERO/inst/doc/vignette_02_GRN_inference.R, vignettes/BioNERO/inst/doc/vignette_03_network_comparison.R importsMe: cageminer dependencyCount: 166 Package: BioNet Version: 1.58.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 7537a515415a778836a02e20c93e2daf NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: RELEASE_3_16 git_last_commit: 462aa3b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioNet_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNet_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNet_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioNet_1.58.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: SMITE suggestsMe: SANTA, mwcsr dependencyCount: 54 Package: BioNetStat Version: 1.18.0 Depends: R (>= 4.0), shiny, igraph, shinyBS, pathview, DT Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr, utils, stats, RColorBrewer, Hmisc, psych, knitr, rmarkdown, markdown License: GPL (>= 3) MD5sum: aedf149a8f3bcb6b4e51adc8e29a32da NeedsCompilation: no Title: Biological Network Analysis Description: A package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view. biocViews: Network, NetworkInference, Pathways, GraphAndNetwork, Sequencing, Microarray, Metabolomics, Proteomics, GeneExpression, RNASeq, SystemsBiology, DifferentialExpression, GeneSetEnrichment, ImmunoOncology Author: Vinícius Jardim, Suzana Santos, André Fujita, and Marcos Buckeridge Maintainer: Vinicius Jardim URL: http://github.com/jardimViniciusC/BioNetStat VignetteBuilder: knitr, rmarkdown BugReports: http://github.com/jardimViniciusC/BioNetStat/issues git_url: https://git.bioconductor.org/packages/BioNetStat git_branch: RELEASE_3_16 git_last_commit: c00a54e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioNetStat_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioNetStat_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioNetStat_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioNetStat_1.18.0.tgz vignettes: vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_pt.html, vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_us.html, vignettes/BioNetStat/inst/doc/vignette.html vignetteTitles: 3. Tutorial para o console do R, 2. R console tutorial, 1. Interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 137 Package: BioQC Version: 1.26.0 Depends: R (>= 3.5.0), Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, hgu133plus2.db, ggplot2, reshape2, plyr, ineq, covr, limma, RColorBrewer License: GPL (>=3) + file LICENSE Archs: x64 MD5sum: eddd368e698f022ce631789f40da4319 NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang [cre, aut], Laura Badi [aut], Gregor Sturm [aut], Roland Ambs [aut], Iakov Davydov [aut] Maintainer: Jitao David Zhang URL: https://accio.github.io/BioQC VignetteBuilder: knitr BugReports: https://accio.github.io/BioQC/issues git_url: https://git.bioconductor.org/packages/BioQC git_branch: RELEASE_3_16 git_last_commit: c4cfa15 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioQC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioQC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioQC_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioQC_1.26.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-introduction.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc-simulation.html, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.html, vignettes/BioQC/inst/doc/BioQC.html vignetteTitles: BioQC Algorithm: Speeding up the Wilcoxon-Mann-Whitney Test, BioQC: Detect tissue heterogeneity in gene expression data, Using BioQC with signed genesets, BioQC-benchmark: Testing Efficiency,, Sensitivity and Specificity of BioQC on simulated and real-world data, Comparing the Wilcoxon-Mann-Whitney to alternative statistical tests, BioQC-kidney: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-introduction.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc-simulation.R, vignettes/BioQC/inst/doc/bioqc-wmw-test-performance.R, vignettes/BioQC/inst/doc/BioQC.R dependencyCount: 13 Package: biosigner Version: 1.26.0 Imports: Biobase, methods, e1071, grDevices, graphics, MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats, SummarizedExperiment, utils Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 60fa2538b344a969630de1198d161c01 NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] () Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.3389/fmolb.2016.00026 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_16 git_last_commit: 17c649e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biosigner_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biosigner_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biosigner_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biosigner_1.26.0.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: biosigner-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R importsMe: multiSight suggestsMe: phenomis dependencyCount: 107 Package: Biostrings Version: 2.66.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), XVector (>= 0.37.1), GenomeInfoDb Imports: methods, utils, grDevices, graphics, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit Enhances: Rmpi License: Artistic-2.0 Archs: x64 MD5sum: 0a8f1876901ffad59e00da7c74db5352 NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pagès URL: https://bioconductor.org/packages/Biostrings BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: RELEASE_3_16 git_last_commit: 3470ca7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Biostrings_2.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Biostrings_2.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Biostrings_2.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Biostrings_2.66.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, chimeraviz, ChIPanalyser, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, msa, muscle, oligo, ORFhunteR, periodicDNA, pqsfinder, PWMEnrich, qrqc, QSutils, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, rRDP, Rsamtools, RSVSim, sangeranalyseR, sangerseqR, SCAN.UPC, SELEX, seqbias, ShortRead, SICtools, SimFFPE, ssviz, Structstrings, svaNUMT, systemPipeR, topdownr, TreeSummarizedExperiment, triplex, VarCon, FDb.FANTOM4.promoters.hg19, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP, JASPAR2014, NestLink, generegulation, sequencing, CleanBSequences, pagoo, SimRAD, STRMPS, SubVis importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder, AnnotationHubData, appreci8R, ArrayExpressHTS, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BgeeCall, biovizBase, brainflowprobes, branchpointer, BSgenome, bsseq, BUMHMM, BUSpaRse, CellaRepertorium, CellBarcode, 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seqmagick, simMP, SMITIDstruct, TrustVDJ, utr.annotation, vhcub suggestsMe: annotate, AnnotationForge, AnnotationHub, bambu, BANDITS, BiocGenerics, BRGenomics, CSAR, eisaR, exomeCopy, GenomicFiles, GenomicRanges, GWASTools, HPiP, maftools, methrix, methylumi, MiRaGE, mitoClone2, nuCpos, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, rTRM, spatzie, splatter, systemPipeTools, treeio, tripr, XVector, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, AhoCorasickTrie, bbl, bio3d, DDPNA, file2meco, gkmSVM, maGUI, MARVEL, msaR, NameNeedle, phangorn, polyRAD, protr, rDNAse, sigminer, Signac, tidysq linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 17 Package: BioTIP Version: 1.12.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, MASS, scran Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: debeb275ee85a57c16e5e569a1624960 NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier An, Antonio Feliciano, Xinan Yang Maintainer: Yuxi (Jennifer) Sun , Zhezhen Wang , and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: RELEASE_3_16 git_last_commit: 0cb382e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BioTIP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BioTIP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BioTIP_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BioTIP_1.12.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 71 Package: biotmle Version: 1.22.0 Depends: R (>= 4.0) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, ranger, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: MIT + file LICENSE MD5sum: 434822e3db3e2e447e0e7c7d297799c7 NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions. biocViews: Regression, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (), Alan Hubbard [aut, ths] (), Mark van der Laan [aut, ths] (), Weixin Cai [ctb] (), Philippe Boileau [ctb] () Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: RELEASE_3_16 git_last_commit: bfa9e7d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biotmle_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biotmle_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biotmle_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biotmle_1.22.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html vignetteTitles: Identifying Biomarkers from an Exposure Variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R dependencyCount: 99 Package: biovizBase Version: 1.46.0 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4), ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 2dc0a153a578a5dc82e5595a32e24089 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: RELEASE_3_16 git_last_commit: a47060c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biovizBase_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biovizBase_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biovizBase_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biovizBase_1.46.0.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE, qrqc importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, qrqc, Rqc, MOCHA suggestsMe: CINdex, derfinderPlot, NanoStringNCTools, R3CPET, regionReport, StructuralVariantAnnotation, Signac dependencyCount: 145 Package: BiRewire Version: 3.30.0 Depends: igraph, slam, Rtsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: 2ef7ae52e84cc2541bb70d3a75b44648 NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted. biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: RELEASE_3_16 git_last_commit: 870cea3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiRewire_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiRewire_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiRewire_3.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BiRewire_3.30.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R dependencyCount: 16 Package: biscuiteer Version: 1.12.0 Depends: R (>= 4.1.0), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, IRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: DSS, covr, knitr, rmarkdown, markdown, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10, BiocStyle License: GPL-3 MD5sum: d680ca31a47cf98af9fa5c3a7b7ec4eb NeedsCompilation: no Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche [aut], Wanding Zhou [aut], Benjamin Johnson [aut], Jacob Morrison [aut, cre], Lyong Heo [aut], James Eapen [aut] Maintainer: Jacob Morrison URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues git_url: https://git.bioconductor.org/packages/biscuiteer git_branch: RELEASE_3_16 git_last_commit: 236bbc1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/biscuiteer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/biscuiteer_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/biscuiteer_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/biscuiteer_1.12.0.tgz vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html vignetteTitles: Biscuiteer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R dependencyCount: 199 Package: BiSeq Version: 1.38.0 Depends: R (>= 3.5.0), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: e95fe02b16d38066b7ab706055aab081 NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: RELEASE_3_16 git_last_commit: 2291602 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BiSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BiSeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BiSeq_1.38.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R dependsOnMe: RRBSdata dependencyCount: 87 Package: BitSeq Version: 1.41.0 Depends: Rsamtools (>= 1.99.3) Imports: S4Vectors, IRanges, methods, utils LinkingTo: Rhtslib (>= 1.15.5) Suggests: BiocStyle License: Artistic-2.0 + file LICENSE MD5sum: d461266598d29b16f5b6bddca83061ef NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/BitSeq git_branch: master git_last_commit: 223ea69 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/BitSeq_1.41.0.tar.gz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R dependencyCount: 31 Package: blacksheepr Version: 1.12.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE MD5sum: 1e20177df9e32d3a2b62000faec31d9e NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blacksheepr/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: RELEASE_3_16 git_last_commit: 70c1d05 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/blacksheepr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/blacksheepr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/blacksheepr_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/blacksheepr_1.12.0.tgz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 129 Package: blima Version: 1.32.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 Archs: x64 MD5sum: 8ccffcd5590d1e04a45acabbd76f984f NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: RELEASE_3_16 git_last_commit: c31ee22 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/blima_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/blima_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/blima_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/blima_1.32.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R suggestsMe: blimaTestingData dependencyCount: 81 Package: BLMA Version: 1.22.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: a5c4cb33e1247fdd14d6cce7f877df19 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen , Hung Nguyen , and Sorin Draghici Maintainer: Hung Nguyen git_url: https://git.bioconductor.org/packages/BLMA git_branch: RELEASE_3_16 git_last_commit: af85045 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BLMA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BLMA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BLMA_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BLMA_1.22.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R dependencyCount: 75 Package: BloodGen3Module Version: 1.6.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, ExperimentHub, methods, grid, graphics, stats, grDevices, circlize, testthat, ComplexHeatmap(>= 1.99.8), ggplot2, matrixStats, gtools, reshape2, preprocessCore, randomcoloR, V8, limma Suggests: RUnit, devtools, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: 5a0d4ae1698fdffe6f8d91ea2657baf1 NeedsCompilation: no Title: This R package for performing module repertoire analyses and generating fingerprint representations Description: The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows. biocViews: Software, Visualization, GeneExpression Author: Darawan Rinchai [aut, cre] () Maintainer: Darawan Rinchai VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BloodGen3Module git_branch: RELEASE_3_16 git_last_commit: abb8ce4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BloodGen3Module_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BloodGen3Module_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BloodGen3Module_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BloodGen3Module_1.6.0.tgz vignettes: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.html vignetteTitles: BloodGen3Module: Modular Repertoire Analysis and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BloodGen3Module/inst/doc/BloodGen3Module.R dependencyCount: 151 Package: bluster Version: 1.8.0 Imports: stats, methods, utils, cluster, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis, mbkmeans, kohonen, apcluster License: GPL-3 Archs: x64 MD5sum: 4698411f72478c5b8a840281b72d9d9e NeedsCompilation: yes Title: Clustering Algorithms for Bioconductor Description: Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Stephanie Hicks [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: RELEASE_3_16 git_last_commit: 156115c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bluster_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bluster_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bluster_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bluster_1.8.0.tgz vignettes: vignettes/bluster/inst/doc/clusterRows.html, vignettes/bluster/inst/doc/diagnostics.html vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bluster/inst/doc/clusterRows.R, vignettes/bluster/inst/doc/diagnostics.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: scDblFinder, scran, Voyager, Canek suggestsMe: batchelor, ChromSCape, dittoSeq, mbkmeans, mumosa dependencyCount: 29 Package: bnbc Version: 1.20.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: x64 MD5sum: cdf39b35db170ecbe2be8035e3b93259 NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues git_url: https://git.bioconductor.org/packages/bnbc git_branch: RELEASE_3_16 git_last_commit: aad63b8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bnbc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bnbc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bnbc_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bnbc_1.20.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R dependencyCount: 107 Package: bnem Version: 1.6.0 Depends: R (>= 4.1) Imports: CellNOptR, matrixStats, snowfall, Rgraphviz, cluster, flexclust, stats, RColorBrewer, epiNEM, mnem, Biobase, methods, utils, graphics, graph, affy, binom, limma, sva, vsn, rmarkdown Suggests: knitr, BiocGenerics License: GPL-3 MD5sum: 04804ab38025438dc9d0d64bbccdeddd NeedsCompilation: no Title: Training of logical models from indirect measurements of perturbation experiments Description: bnem combines the use of indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR. Perturbation experiments of signalling nodes in cells are analysed for their effect on the global gene expression profile. Those profiles give evidence for the Boolean regulation of down-stream nodes in the network, e.g., whether two parents activate their child independently (OR-gate) or jointly (AND-gate). biocViews: Pathways, SystemsBiology, NetworkInference, Network, GeneExpression, GeneRegulation, Preprocessing Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/MartinFXP/bnem/ VignetteBuilder: knitr BugReports: https://github.com/MartinFXP/bnem/issues git_url: https://git.bioconductor.org/packages/bnem git_branch: RELEASE_3_16 git_last_commit: 150583c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bnem_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bnem_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bnem_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bnem_1.6.0.tgz vignettes: vignettes/bnem/inst/doc/bnem.html vignetteTitles: bnem.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnem/inst/doc/bnem.R dependencyCount: 175 Package: BOBaFIT Version: 1.2.0 Depends: R (>= 2.10) Imports: dplyr, NbClust, ggplot2, ggbio, grDevices, stats, tidyr, GenomicRanges, ggforce, stringr, plyranges, methods, utils, magrittr Suggests: rmarkdown, markdown, BiocStyle, knitr, testthat (>= 3.0.0), utils, testthat License: GPL (>= 3) MD5sum: ea9ad057b5982223cd55b4c6dfc9508d NeedsCompilation: no Title: Refitting diploid region profiles using a clustering procedure Description: This package provides a method to refit and correct the diploid region in copy number profiles. It uses a clustering algorithm to identify pathology-specific normal (diploid) chromosomes and then use their copy number signal to refit the whole profile. The package is composed by three functions: DRrefit (the main function), ComputeNormalChromosome and PlotCluster. biocViews: CopyNumberVariation, Clustering, Visualization, Normalization, Software Author: Andrea Poletti [aut], Gaia Mazzocchetti [aut, cre], Vincenza Solli [aut] Maintainer: Gaia Mazzocchetti URL: https://github.com/andrea-poletti-unibo/BOBaFIT VignetteBuilder: knitr BugReports: https://github.com/andrea-poletti-unibo/BOBaFIT/issues git_url: https://git.bioconductor.org/packages/BOBaFIT git_branch: RELEASE_3_16 git_last_commit: e5675d1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BOBaFIT_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BOBaFIT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BOBaFIT_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BOBaFIT_1.2.0.tgz vignettes: vignettes/BOBaFIT/inst/doc/BOBaFIT.html, vignettes/BOBaFIT/inst/doc/Data-Preparation.html vignetteTitles: BOBaFIT.Rmd, Data preparation using TCGA-BRCA database.Rmd hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BOBaFIT/inst/doc/BOBaFIT.R, vignettes/BOBaFIT/inst/doc/Data-Preparation.R dependencyCount: 164 Package: borealis Version: 1.2.2 Depends: R (>= 4.2.0), Biobase Imports: doParallel, snow, purrr, plyr, foreach, gamlss, gamlss.dist, bsseq, methods, DSS, R.utils, utils, stats, ggplot2, cowplot, dplyr, rlang, GenomicRanges Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, annotatr, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL-3 MD5sum: fb57b46a5208f7ef2a08f9902beacec7 NeedsCompilation: no Title: Bisulfite-seq OutlieR mEthylation At singLe-sIte reSolution Description: Borealis is an R library performing outlier analysis for count-based bisulfite sequencing data. It detectes outlier methylated CpG sites from bisulfite sequencing (BS-seq). The core of Borealis is modeling Beta-Binomial distributions. This can be useful for rare disease diagnoses. biocViews: Sequencing, Coverage, DNAMethylation, DifferentialMethylation Author: Garrett Jenkinson [aut, cre] () Maintainer: Garrett Jenkinson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/borealis git_branch: RELEASE_3_16 git_last_commit: 3993da3 git_last_commit_date: 2022-12-30 Date/Publication: 2022-12-30 source.ver: src/contrib/borealis_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/borealis_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/borealis_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/borealis_1.2.2.tgz vignettes: vignettes/borealis/inst/doc/borealis.html vignetteTitles: Borealis outlier methylation detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/borealis/inst/doc/borealis.R dependencyCount: 105 Package: BPRMeth Version: 1.24.2 Depends: R (>= 3.5.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE Archs: x64 MD5sum: 1bd11df337708b265eecfb88ea34a0cc NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: RELEASE_3_16 git_last_commit: 0543cfa git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BPRMeth_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BPRMeth_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BPRMeth_1.24.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BPRMeth_1.24.2.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html vignetteTitles: BPRMeth: Model higher-order methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R dependsOnMe: Melissa dependencyCount: 97 Package: BRAIN Version: 1.44.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 6cb636ac649543bb675f477ca5fdf7a8 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: RELEASE_3_16 git_last_commit: 6cd8b8e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BRAIN_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BRAIN_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BRAIN_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BRAIN_1.44.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, synapter, RforProteomics dependencyCount: 22 Package: brainflowprobes Version: 1.12.0 Depends: R (>= 3.6.0) Imports: Biostrings (>= 2.52.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), bumphunter (>= 1.26.0), cowplot (>= 1.0.0), derfinder (>= 1.18.1), derfinderPlot (>= 1.18.1), GenomicRanges (>= 1.36.0), ggplot2 (>= 3.1.1), RColorBrewer (>= 1.1), utils, grDevices, GenomicState (>= 0.99.7) Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: 581d38fac413337532381092c59afeee NeedsCompilation: no Title: Plots and annotation for choosing BrainFlow target probe sequence Description: Use these functions to characterize genomic regions for BrainFlow target probe design. biocViews: Coverage, Visualization, ExperimentalDesign, Transcriptomics, FlowCytometry, GeneTarget Author: Amanda Price [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Amanda Price URL: https://github.com/LieberInstitute/brainflowprobes VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/brainflowprobes git_url: https://git.bioconductor.org/packages/brainflowprobes git_branch: RELEASE_3_16 git_last_commit: 2039aa8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/brainflowprobes_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/brainflowprobes_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/brainflowprobes_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/brainflowprobes_1.12.0.tgz vignettes: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.html vignetteTitles: brainflowprobes users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.R dependencyCount: 185 Package: BrainSABER Version: 1.8.0 Depends: R (>= 4.1.0), biomaRt, SummarizedExperiment Imports: data.table, lsa, methods, S4Vectors, utils, BiocFileCache, shiny Suggests: BiocStyle, ComplexHeatmap, fastcluster, heatmaply, knitr, plotly, rmarkdown License: Artistic-2.0 MD5sum: 5ef803604825ff12dc0dc612e5f987dc NeedsCompilation: no Title: Brain Span Atlas in Biobase Expressionset R toolset Description: The Allen Institute for Brain Science provides an RNA sequencing (RNA-Seq) data resource for studying transcriptional mechanisms involved in human brain development known as BrainSpan. BrainSABER is an R package that facilitates comparison of user data with the various developmental stages and brain structures found in the BrainSpan atlas by generating dynamic similarity heatmaps for the two data sets. It also provides a self-validating container for user data. biocViews: GeneExpression, Visualization, Sequencing Author: Carrie Minette [aut], Evgeni Radichev [aut], USD Biomedical Engineering [aut, cre] Maintainer: USD Biomedical Engineering VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/BrainSABER git_branch: RELEASE_3_16 git_last_commit: 263677f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BrainSABER_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BrainSABER_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BrainSABER_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BrainSABER_1.8.0.tgz vignettes: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.html vignetteTitles: BrainSABER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.R dependencyCount: 97 Package: branchpointer Version: 1.24.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, GenomeInfoDb, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE MD5sum: fdbd1d83d96ade59470abd333637e3da NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: RELEASE_3_16 git_last_commit: 60877dc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/branchpointer_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/branchpointer_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/branchpointer_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/branchpointer_1.24.0.tgz vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R dependencyCount: 152 Package: breakpointR Version: 1.16.0 Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: 34da220824ebf7a965d1ee67364bc1d3 NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: RELEASE_3_16 git_last_commit: 04d0691 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/breakpointR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/breakpointR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/breakpointR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/breakpointR_1.16.0.tgz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 73 Package: brendaDb Version: 1.12.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE Archs: x64 MD5sum: ef642ac03af1060b439cbfd1619af06f NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] () Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: RELEASE_3_16 git_last_commit: 412f587 git_last_commit_date: 2022-12-10 Date/Publication: 2022-12-13 source.ver: src/contrib/brendaDb_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/brendaDb_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/brendaDb_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/brendaDb_1.12.0.tgz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 58 Package: BRGenomics Version: 1.10.0 Depends: R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors Imports: GenomicRanges, parallel, IRanges, stats, Rsamtools, GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods Suggests: BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings License: Artistic-2.0 MD5sum: 48f16fb86fff2cf4ede70737f51db599 NeedsCompilation: no Title: Tools for the Efficient Analysis of High-Resolution Genomics Data Description: This package provides useful and efficient utilites for the analysis of high-resolution genomic data using standard Bioconductor methods and classes. BRGenomics is feature-rich and simplifies a number of post-alignment processing steps and data handling. Emphasis is on efficient analysis of multiple datasets, with support for normalization and blacklisting. Included are functions for: spike-in normalizing data; generating basepair-resolution readcounts and coverage data (e.g. for heatmaps); importing and processing bam files (e.g. for conversion to bigWig files); generating metaplots/metaprofiles (bootstrapped mean profiles) with confidence intervals; conveniently calling DESeq2 without using sample-blind estimates of genewise dispersion; among other features. biocViews: Software, DataImport, Sequencing, Coverage, RNASeq, ATACSeq, ChIPSeq, Transcription, GeneRegulation, GeneExpression, Normalization Author: Mike DeBerardine [aut, cre] Maintainer: Mike DeBerardine URL: https://mdeber.github.io VignetteBuilder: knitr BugReports: https://github.com/mdeber/BRGenomics/issues git_url: https://git.bioconductor.org/packages/BRGenomics git_branch: RELEASE_3_16 git_last_commit: 966826e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BRGenomics_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BRGenomics_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BRGenomics_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BRGenomics_1.10.0.tgz vignettes: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.html, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.html, vignettes/BRGenomics/inst/doc/GettingStarted.html, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.html, vignettes/BRGenomics/inst/doc/ImportingProcessingData.html, vignettes/BRGenomics/inst/doc/Overview.html, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.html, vignettes/BRGenomics/inst/doc/SequenceExtraction.html, vignettes/BRGenomics/inst/doc/SignalCounting.html, vignettes/BRGenomics/inst/doc/SpikeInNormalization.html vignetteTitles: Analyzing Multiple Datasets, DESeq2 with Global Perturbations, Getting Started, Importing and Modifying Annotations, Importing and Processing Data, Overview, Profile Plots and Bootstrapping, Sequence Extraction, Signal Counting, Spike-in Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.R, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.R, vignettes/BRGenomics/inst/doc/GettingStarted.R, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.R, vignettes/BRGenomics/inst/doc/ImportingProcessingData.R, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.R, vignettes/BRGenomics/inst/doc/SequenceExtraction.R, vignettes/BRGenomics/inst/doc/SignalCounting.R, vignettes/BRGenomics/inst/doc/SpikeInNormalization.R importsMe: EpiCompare dependencyCount: 99 Package: bridge Version: 1.62.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: x64 MD5sum: 8827b980b1d8ef469f4b0988c39a31d0 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/bridge git_branch: RELEASE_3_16 git_last_commit: 1e5a52f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bridge_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bridge_1.62.0.zip vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R dependencyCount: 1 Package: BridgeDbR Version: 2.8.0 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 19105686f769045eff22e23256c6271c NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Denise Slenter, Anwesha Bohler , Lars Eijssen , Tooba Abbassi-Daloii Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: RELEASE_3_16 git_last_commit: 2c251fc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BridgeDbR_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BridgeDbR_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BridgeDbR_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BridgeDbR_2.8.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R suggestsMe: rWikiPathways dependencyCount: 3 Package: BrowserViz Version: 2.20.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: aa90122bfd4b56e52a1a3a51b8c1838d NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/paul-shannon/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: RELEASE_3_16 git_last_commit: 2c9c380 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BrowserViz_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BrowserViz_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BrowserViz_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BrowserViz_2.20.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 13 Package: BSgenome Version: 1.66.3 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.25.6), GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7) Imports: methods, utils, stats, matrixStats, BiocGenerics, S4Vectors, IRanges, XVector (>= 0.29.3), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocManager, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: 3db38e1f7e7fb0d685c4659e623b410f NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès [aut, cre], Prisca Chidimma Maduka [ctb] (add 'replace' argument to forgeBSgenomeDataPkg()) Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: RELEASE_3_16 git_last_commit: f2f40e0 git_last_commit_date: 2023-02-16 Date/Publication: 2023-02-16 source.ver: src/contrib/BSgenome_1.66.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/BSgenome_1.66.3.zip mac.binary.ver: bin/macosx/contrib/4.2/BSgenome_1.66.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BSgenome_1.66.3.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: bambu, ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, rGADEM, VarCon, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.major, BSgenome.Hsapiens.UCSC.hg38.dbSNP151.minor, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, cliProfiler, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, crisprseekplus, crisprViz, diffHic, dpeak, enhancerHomologSearch, enrichTF, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, IsoformSwitchAnalyzeR, katdetectr, m6Aboost, MADSEQ, methrix, MethylSeekR, MMDiff2, monaLisa, Motif2Site, motifbreakR, motifmatchr, msgbsR, multicrispr, MungeSumstats, musicatk, MutationalPatterns, NxtIRFcore, ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, RCAS, regioneR, REMP, Repitools, RESOLVE, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, signeR, SigsPack, SingleMoleculeFootprinting, SparseSignatures, spatzie, spiky, SpliceWiz, TAPseq, TFBSTools, trena, tRNAscanImport, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, XNAString, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Btaurus.UCSC.bosTau9.masked, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Cjacchus.UCSC.calJac4, BSgenome.CneoformansVarGrubiiKN99.NCBI.ASM221672v1, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Gmax.NCBI.Gmv40, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.NCBI.T2T.CHM13v2.0, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hs1, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfascicularis.NCBI.6.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm39, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, GenomicDistributionsData, ActiveDriverWGS, deconstructSigs, ggcoverage, ICAMS, simMP suggestsMe: Biostrings, biovizBase, ChIPpeakAnno, chipseq, easyRNASeq, eisaR, factR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, maftools, metaseqR2, MiRaGE, plotgardener, ProteoDisco, PWMEnrich, QDNAseq, recoup, RiboCrypt, rtracklayer, sitadela, gkmSVM, MARVEL, sigminer, Signac dependencyCount: 46 Package: bsseq Version: 1.34.0 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.19.11), rhdf5 LinkingTo: Rcpp, beachmat Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, beachmat (>= 1.5.2), BatchJobs License: Artistic-2.0 Archs: x64 MD5sum: 295c85403b7f8c27ed534e157d89e19b NeedsCompilation: yes Title: Analyze, manage and store bisulfite sequencing data Description: A collection of tools for analyzing and visualizing bisulfite sequencing data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_16 git_last_commit: 98239c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bsseq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bsseq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bsseq_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bsseq_1.34.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData importsMe: borealis, DMRcate, MethCP, methylCC, methylSig, MIRA, NanoMethViz, scmeth suggestsMe: methrix, tissueTreg dependencyCount: 75 Package: BubbleTree Version: 2.28.0 Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: 5de859d09914c8875949f216ee088047 NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: RELEASE_3_16 git_last_commit: a9640da git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BubbleTree_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BubbleTree_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BubbleTree_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BubbleTree_2.28.0.tgz vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html vignetteTitles: BubbleTree Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R dependencyCount: 157 Package: BufferedMatrix Version: 1.62.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: x64 MD5sum: e18e7b7edba2e994ba32c27260fcd69f NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: RELEASE_3_16 git_last_commit: fce9086 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/BufferedMatrix_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BufferedMatrix_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BufferedMatrix_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BufferedMatrix_1.62.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.62.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: x64 MD5sum: 1c53238a43259f250d21c7240b4657af NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: RELEASE_3_16 git_last_commit: db8164b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/BufferedMatrixMethods_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BufferedMatrixMethods_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BufferedMatrixMethods_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BufferedMatrixMethods_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: bugsigdbr Version: 1.4.3 Depends: R (>= 4.1) Imports: BiocFileCache, methods, vroom, utils Suggests: BiocStyle, knitr, ontologyIndex, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 45530792fdde1e647d868d4b67aa2a09 NeedsCompilation: no Title: R-side access to published microbial signatures from BugSigDB Description: The bugsigdbr package implements convenient access to bugsigdb.org from within R/Bioconductor. The goal of the package is to facilitate import of BugSigDB data into R/Bioconductor, provide utilities for extracting microbe signatures, and enable export of the extracted signatures to plain text files in standard file formats such as GMT. biocViews: DataImport, GeneSetEnrichment, Metagenomics, Microbiome Author: Ludwig Geistlinger [aut, cre], Jennifer Wokaty [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/bugsigdbr VignetteBuilder: knitr BugReports: https://github.com/waldronlab/bugsigdbr/issues git_url: https://git.bioconductor.org/packages/bugsigdbr git_branch: RELEASE_3_16 git_last_commit: d871eb6 git_last_commit_date: 2023-02-20 Date/Publication: 2023-02-20 source.ver: src/contrib/bugsigdbr_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/bugsigdbr_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.2/bugsigdbr_1.4.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bugsigdbr_1.4.3.tgz vignettes: vignettes/bugsigdbr/inst/doc/bugsigdbr.html vignetteTitles: R-side access to BugSigDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bugsigdbr/inst/doc/bugsigdbr.R dependencyCount: 53 Package: BUMHMM Version: 1.22.0 Depends: R (>= 3.5.0) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: ae2c6614e88d942903a1d660bee079de NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: RELEASE_3_16 git_last_commit: fe36b58 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BUMHMM_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUMHMM_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUMHMM_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BUMHMM_1.22.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 123 Package: bumphunter Version: 1.40.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 11090040312a7467aea161f87c3e698f NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb], Tamilselvi Guharaj [cre] Maintainer: Tamilselvi Guharaj URL: https://github.com/rafalab/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: RELEASE_3_16 git_last_commit: f655014 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/bumphunter_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/bumphunter_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/bumphunter_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/bumphunter_1.40.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: brainflowprobes, coMethDMR, DAMEfinder, derfinder, dmrseq, epimutacions, epivizr, methylCC, rnaEditr, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 102 Package: BumpyMatrix Version: 1.6.0 Imports: utils, methods, Matrix, S4Vectors, IRanges Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 89450a903d6afc27773fe77ff4466628 NeedsCompilation: no Title: Bumpy Matrix of Non-Scalar Objects Description: Implements the BumpyMatrix class and several subclasses for holding non-scalar objects in each entry of the matrix. This is akin to a ragged array but the raggedness is in the third dimension, much like a bumpy surface - hence the name. Of particular interest is the BumpyDataFrameMatrix, where each entry is a Bioconductor data frame. This allows us to naturally represent multivariate data in a format that is compatible with two-dimensional containers like the SummarizedExperiment and MultiAssayExperiment objects. biocViews: Software, Infrastructure, DataRepresentation Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/BumpyMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/BumpyMatrix/issues git_url: https://git.bioconductor.org/packages/BumpyMatrix git_branch: RELEASE_3_16 git_last_commit: 48710ea git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BumpyMatrix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BumpyMatrix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BumpyMatrix_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BumpyMatrix_1.6.0.tgz vignettes: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.html vignetteTitles: The BumpyMatrix class hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BumpyMatrix/inst/doc/BumpyMatrix.R importsMe: CoreGx, MerfishData, MouseGastrulationData suggestsMe: ggspavis, SpatialExperiment, STexampleData dependencyCount: 12 Package: BUS Version: 1.54.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: x64 MD5sum: 795ce8ceb3743ba0d277347c7fd0964b NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: RELEASE_3_16 git_last_commit: deb5cc3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BUS_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUS_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUS_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BUS_1.54.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.16.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 9a0174f9b7706b61138789c5916b9a7f NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: RELEASE_3_16 git_last_commit: e1645d7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/BUScorrect_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUScorrect_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/BUScorrect_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BUScorrect_1.16.0.tgz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 29 Package: BUSpaRse Version: 1.12.2 Depends: R (>= 3.6) Imports: AnnotationDbi, AnnotationFilter, biomaRt, BiocGenerics, Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, magrittr, Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr, tibble, tidyr, utils, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH Suggests: knitr, rmarkdown, testthat, BiocStyle, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: e98132824f4f50aef7dcf42a3ca4a619 NeedsCompilation: yes Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues git_url: https://git.bioconductor.org/packages/BUSpaRse git_branch: RELEASE_3_16 git_last_commit: 071ebd9 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BUSpaRse_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUSpaRse_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BUSpaRse_1.12.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BUSpaRse_1.12.2.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, Transcript to gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 120 Package: BUSseq Version: 1.4.2 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, S4Vectors, gplots, grDevices, methods, stats, utils Suggests: BiocStyle, knitr, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 465e002323176343980266938e7a1ac2 NeedsCompilation: yes Title: Batch Effect Correction with Unknow Subtypes for scRNA-seq data Description: BUSseq R package fits an interpretable Bayesian hierarchical model---the Batch Effects Correction with Unknown Subtypes for scRNA seq Data (BUSseq)---to correct batch effects in the presence of unknown cell types. BUSseq is able to simultaneously correct batch effects, clusters cell types, and takes care of the count data nature, the overdispersion, the dropout events, and the cell-specific sequencing depth of scRNA-seq data. After correcting the batch effects with BUSseq, the corrected value can be used for downstream analysis as if all cells were sequenced in a single batch. BUSseq can integrate read count matrices obtained from different scRNA-seq platforms and allow cell types to be measured in some but not all of the batches as long as the experimental design fulfills the conditions listed in our manuscript. biocViews: ExperimentalDesign, GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect, SingleCell, Sequencing Author: Fangda Song [aut, cre] (), Ga Ming Chan [aut], Yingying Wei [aut] () Maintainer: Fangda Song URL: https://github.com/songfd2018/BUSseq VignetteBuilder: knitr BugReports: https://github.com/songfd2018/BUSseq/issues git_url: https://git.bioconductor.org/packages/BUSseq git_branch: RELEASE_3_16 git_last_commit: f389627 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/BUSseq_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/BUSseq_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/BUSseq_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/BUSseq_1.4.2.tgz vignettes: vignettes/BUSseq/inst/doc/BUSseq_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUSseq/inst/doc/BUSseq_user_guide.R dependencyCount: 30 Package: CAEN Version: 1.6.0 Depends: R (>= 4.1) Imports: stats,PoiClaClu,SummarizedExperiment,methods Suggests: knitr,rmarkdown License: GPL-2 MD5sum: 4346618674e4f1318dc42bbd73f3b270 NeedsCompilation: no Title: Category encoding method for selecting feature genes for the classification of single-cell RNA-seq Description: With the development of high-throughput techniques, more and more gene expression analysis tend to replace hybridization-based microarrays with the revolutionary technology.The novel method encodes the category again by employing the rank of samples for each gene in each class. We then consider the correlation coefficient of gene and class with rank of sample and new rank of category. The highest correlation coefficient genes are considered as the feature genes which are most effective to classify the samples. biocViews: DifferentialExpression, Sequencing, Classification, RNASeq, ATACSeq, SingleCell, GeneExpression, RIPSeq Author: Zhou Yan [aut, cre] Maintainer: Zhou Yan <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAEN git_branch: RELEASE_3_16 git_last_commit: ceea9cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CAEN_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAEN_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAEN_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CAEN_1.6.0.tgz vignettes: vignettes/CAEN/inst/doc/CAEN.html vignetteTitles: CAEN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAEN/inst/doc/CAEN.R dependencyCount: 26 Package: CAFE Version: 1.34.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 62169e448f1215a7304f65f86a076695 NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen git_url: https://git.bioconductor.org/packages/CAFE git_branch: RELEASE_3_16 git_last_commit: 61e9936 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CAFE_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAFE_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAFE_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CAFE_1.34.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R dependencyCount: 163 Package: CAGEfightR Version: 1.18.0 Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>= 1.38.2), SummarizedExperiment (>= 1.8.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3), Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0), GenomicFeatures(>= 1.29.11), GenomicAlignments(>= 1.22.1), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: 0506c9ea2f66d97185fd3f603830fa4f NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: RELEASE_3_16 git_last_commit: de07aac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CAGEfightR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAGEfightR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAGEfightR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CAGEfightR_1.18.0.tgz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependsOnMe: CAGEWorkflow suggestsMe: nanotubes dependencyCount: 160 Package: cageminer Version: 1.4.0 Depends: R (>= 4.1) Imports: ggplot2, ggbio, ggtext, GenomeInfoDb, GenomicRanges, IRanges, reshape2, methods, BioNERO Suggests: testthat (>= 3.0.0), SummarizedExperiment, knitr, BiocStyle, rmarkdown, covr, sessioninfo License: GPL-3 MD5sum: 316111044e2be074413c1c3504b7f560 NeedsCompilation: no Title: Candidate Gene Miner Description: This package aims to integrate GWAS-derived SNPs and coexpression networks to mine candidate genes associated with a particular phenotype. For that, users must define a set of guide genes, which are known genes involved in the studied phenotype. Additionally, the mined candidates can be given a score that favor candidates that are hubs and/or transcription factors. The scores can then be used to rank and select the top n most promising genes for downstream experiments. biocViews: Software, SNP, FunctionalPrediction, GenomeWideAssociation, GeneExpression, NetworkEnrichment, VariantAnnotation, FunctionalGenomics, Network Author: Fabrício Almeida-Silva [aut, cre] (), Thiago Venancio [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cageminer VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cageminer git_url: https://git.bioconductor.org/packages/cageminer git_branch: RELEASE_3_16 git_last_commit: 228f8c2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cageminer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cageminer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cageminer_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cageminer_1.4.0.tgz vignettes: vignettes/cageminer/inst/doc/cageminer.html vignetteTitles: Mining high-confidence candidate genes with cageminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cageminer/inst/doc/cageminer.R dependencyCount: 205 Package: CAGEr Version: 2.4.0 Depends: methods, MultiAssayExperiment, R (>= 4.1.0) Imports: BiocGenerics, BiocParallel, BSgenome, data.table, DelayedArray, DelayedMatrixStats, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape2, rtracklayer, S4Vectors (>= 0.27.5), som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 68ec18ea718b95086d52c29ea6369b6a NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle [aut], Charles Plessy [cre], Damir Baranasic [ctb], Sarvesh Nikumbh [ctb] Maintainer: Charles Plessy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: RELEASE_3_16 git_last_commit: 4ce371f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CAGEr_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAGEr_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAGEr_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CAGEr_2.4.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 102 Package: calm Version: 1.12.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: d4fd62e2e7e4970b1f60bb0dcd92cb70 NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: RELEASE_3_16 git_last_commit: e9153a7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/calm_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/calm_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/calm_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/calm_1.12.0.tgz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CAMERA Version: 1.54.0 Depends: R (>= 3.5.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: x64 MD5sum: 6406a3585bf0df9ba748dcba06cf20e5 NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new git_url: https://git.bioconductor.org/packages/CAMERA git_branch: RELEASE_3_16 git_last_commit: 44fbac4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CAMERA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CAMERA_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CAMERA_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CAMERA_1.54.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2 dependencyCount: 132 Package: canceR Version: 1.32.0 Depends: R (>= 4.1), tcltk Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils, grDevices, R.oo, R.methodsS3, httr Suggests: testthat (>= 3.1), knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 51791983ed57a2ed350a74b2abc42af3 NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud SystemRequirements: Tktable, BWidget VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/canceR/issues git_url: https://git.bioconductor.org/packages/canceR git_branch: RELEASE_3_16 git_last_commit: febcd13 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/canceR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/canceR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/canceR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/canceR_1.32.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.html vignetteTitles: canceR: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/canceR/inst/doc/canceR.R dependencyCount: 161 Package: cancerclass Version: 1.42.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: x64 MD5sum: cda38b5d70655976fec88534cfb4e03f NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: RELEASE_3_16 git_last_commit: 550e5df git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cancerclass_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cancerclass_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cancerclass_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cancerclass_1.42.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 7 Package: CancerInSilico Version: 2.18.0 Depends: R (>= 3.4), Rcpp Imports: methods, utils, graphics, stats LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl, gplots License: GPL-2 Archs: x64 MD5sum: 716dc250caa885da7b8b0783f0871e16 NeedsCompilation: yes Title: An R interface for computational modeling of tumor progression Description: The CancerInSilico package provides an R interface for running mathematical models of tumor progresson and generating gene expression data from the results. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: ImmunoOncology, MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics, GeneExpression, RNASeq, SingleCell Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CancerInSilico git_branch: RELEASE_3_16 git_last_commit: ad25a00 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CancerInSilico_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CancerInSilico_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CancerInSilico_2.18.0.tgz vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html vignetteTitles: The CancerInSilico Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R dependencyCount: 6 Package: CancerSubtypes Version: 1.24.0 Depends: R (>= 4.0), sigclust, NMF Imports: cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, knitr, RTCGA.mRNA, rmarkdown License: GPL (>= 2) MD5sum: 6408f7a2caaf66cf3fa980489f7ca579 NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on multiple genomic data sets Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based multi-omics data, such as gene expression, miRNA expression, DNA methylation and others. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu [aut, cre] Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/CancerSubtypes VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/CancerSubtypes/issues git_url: https://git.bioconductor.org/packages/CancerSubtypes git_branch: RELEASE_3_16 git_last_commit: 8c36b22 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CancerSubtypes_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CancerSubtypes_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CancerSubtypes_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CancerSubtypes_1.24.0.tgz vignettes: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html vignetteTitles: CancerSubtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R dependencyCount: 61 Package: cardelino Version: 1.0.0 Depends: R (>= 4.2), stats Imports: combinat, GenomeInfoDb, GenomicRanges, ggplot2, ggtree, Matrix, matrixStats, methods, pheatmap, snpStats, S4Vectors, utils, VariantAnnotation, vcfR Suggests: BiocStyle, foreach, knitr, pcaMethods, rmarkdown, testthat, VGAM Enhances: doMC License: GPL-3 MD5sum: 525534b70799dee1c832844736da4e2b NeedsCompilation: yes Title: Clone Identification from Single Cell Data Description: Methods to infer clonal tree configuration for a population of cells using single-cell RNA-seq data (scRNA-seq), and possibly other data modalities. Methods are also provided to assign cells to inferred clones and explore differences in gene expression between clones. These methods can flexibly integrate information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. A flexible beta-binomial error model that accounts for stochastic dropout events as well as systematic allelic imbalance is used. biocViews: SingleCell, RNASeq, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, ExomeSeq Author: Jeffrey Pullin [aut, cre], Yuanhua Huang [aut], Davis McCarthy [aut] Maintainer: Jeffrey Pullin URL: https://github.com/single-cell-genetics/cardelino VignetteBuilder: knitr BugReports: https://github.com/single-cell-genetics/cardelino/issues git_url: https://git.bioconductor.org/packages/cardelino git_branch: RELEASE_3_16 git_last_commit: f77a6f6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cardelino_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cardelino_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cardelino_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cardelino_1.0.0.tgz vignettes: vignettes/cardelino/inst/doc/vignette-cloneid.html vignetteTitles: Clone ID with cardelino hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cardelino/inst/doc/vignette-cloneid.R dependencyCount: 134 Package: Cardinal Version: 3.0.1 Depends: ProtGenerics, BiocGenerics, BiocParallel, EBImage, graphics, methods, S4Vectors (>= 0.27.3), stats Imports: Biobase, irlba, Matrix, matter, magrittr, mclust, nlme, parallel, signal, sp, stats4, utils, viridisLite Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 2c46e941821bd76a335c8183110843d2 NeedsCompilation: yes Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_16 git_last_commit: 9de5cdc git_last_commit_date: 2022-11-14 Date/Publication: 2022-11-15 source.ver: src/contrib/Cardinal_3.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cardinal_3.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/Cardinal_3.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Cardinal_3.0.1.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-3-guide.html, vignettes/Cardinal/inst/doc/Cardinal-3-stats.html vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging analysis, 2. Cardinal 3: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-3-guide.R, vignettes/Cardinal/inst/doc/Cardinal-3-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 75 Package: CARNIVAL Version: 2.8.0 Depends: R (>= 4.0) Imports: readr, stringr, lpSolve, igraph, dplyr, tibble, tidyr, rjson, rmarkdown Suggests: RefManageR, BiocStyle, covr, knitr, testthat (>= 3.0.0), sessioninfo License: GPL-3 MD5sum: 8a625a5d177683644276069e2ec83656 NeedsCompilation: no Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. biocViews: Transcriptomics, GeneExpression, Network Author: Enio Gjerga [aut] (), Panuwat Trairatphisan [aut], Anika Liu [ctb], Alberto Valdeolivas [ctb], Nikolas Peschke [ctb], Aurelien Dugourd [ctb], Attila Gabor [cre], Olga Ivanova [aut] Maintainer: Attila Gabor URL: https://github.com/saezlab/CARNIVAL VignetteBuilder: knitr BugReports: https://github.com/saezlab/CARNIVAL/issues git_url: https://git.bioconductor.org/packages/CARNIVAL git_branch: RELEASE_3_16 git_last_commit: e3a8438 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CARNIVAL_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CARNIVAL_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CARNIVAL_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CARNIVAL_2.8.0.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: Contextualizing large scale signalling networks from expression footprints with CARNIVAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R importsMe: cosmosR suggestsMe: dce dependencyCount: 65 Package: casper Version: 2.32.0 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: x64 MD5sum: 7861bc853a3ed19eb4294b9df2e41c5a NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/casper git_branch: RELEASE_3_16 git_last_commit: d0c72cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/casper_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/casper_2.32.0.zip vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R dependencyCount: 111 Package: CATALYST Version: 1.22.0 Depends: R (>= 4.0), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, magrittr, Matrix, matrixStats, methods, nnls, purrr, RColorBrewer, reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto, rmarkdown, testthat, uwot License: GPL (>=2) MD5sum: fbb099454e99a5e73e65484e0cf569f2 NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. biocViews: Clustering, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry, Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Vito R.T. Zanotelli [aut], Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd], Bernd Bodenmiller [fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: RELEASE_3_16 git_last_commit: fa9b337 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CATALYST_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CATALYST_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CATALYST_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CATALYST_1.22.0.tgz vignettes: vignettes/CATALYST/inst/doc/differential.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "2. Differential discovery", "1. Preprocessing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential.R, vignettes/CATALYST/inst/doc/preprocessing.R dependsOnMe: cytofWorkflow suggestsMe: diffcyt, imcRtools, treekoR dependencyCount: 183 Package: Category Version: 2.64.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGGREST, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: 25661de98f2a1dd3a725ffc32fd87088 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Category git_branch: RELEASE_3_16 git_last_commit: b512cbe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Category_2.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Category_2.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Category_2.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Category_2.64.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats importsMe: categoryCompare, cellHTS2, GmicR, interactiveDisplay, meshr, miRLAB, phenoTest, scTensor suggestsMe: qpgraph, RnBeads, maGUI dependencyCount: 59 Package: categoryCompare Version: 1.42.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGGREST, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter, rmarkdown License: GPL-2 MD5sum: 13384458a320bba4f400bb5b7e46a0e4 NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues git_url: https://git.bioconductor.org/packages/categoryCompare git_branch: RELEASE_3_16 git_last_commit: a854c2a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/categoryCompare_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/categoryCompare_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/categoryCompare_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/categoryCompare_1.42.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R dependencyCount: 90 Package: CausalR Version: 1.30.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: fbe15290f057338668875e6b43ceca18 NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: RELEASE_3_16 git_last_commit: 8f36c59 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CausalR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CausalR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CausalR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CausalR_1.30.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 13 Package: cbaf Version: 1.20.1 Depends: R (>= 4.1) Imports: BiocFileCache, RColorBrewer, cBioPortalData, genefilter, gplots, grDevices, stats, utils, openxlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 115d84712602fa29faf41ee492e87a80 NeedsCompilation: no Title: Automated functions for comparing various omic data from cbioportal.org Description: This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, Microarray,ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: RELEASE_3_16 git_last_commit: 9f57cd1 git_last_commit_date: 2022-12-18 Date/Publication: 2022-12-18 source.ver: src/contrib/cbaf_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cbaf_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cbaf_1.20.1.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R dependencyCount: 154 Package: CBEA Version: 1.2.0 Depends: R (>= 4.2.0) Imports: BiocParallel, BiocSet, dplyr, lmom, fitdistrplus, magrittr, methods, mixtools, Rcpp (>= 1.0.7), stats, SummarizedExperiment, tibble, TreeSummarizedExperiment, tidyr, glue, generics, rlang, goftest LinkingTo: Rcpp Suggests: phyloseq, BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), tidyverse, roxygen2, mia, purrr License: MIT + file LICENSE Archs: x64 MD5sum: 1f8ecb231aa28156f298d9876ef4568c NeedsCompilation: yes Title: Competitive Balances for Taxonomic Enrichment Analysis in R Description: This package implements CBEA, a method to perform set-based analysis for microbiome relative abundance data. This approach constructs a competitive balance between taxa within the set and remainder taxa per sample. More details can be found in the Nguyen et al. 2021+ manuscript. Additionally, this package adds support functions to help users perform taxa-set enrichment analyses using existing gene set analysis methods. In the future we hope to also provide curated knowledge driven taxa sets. biocViews: Software, Microbiome, Metagenomics, GeneSetEnrichment, DataImport Author: Quang Nguyen [aut, cre] () Maintainer: Quang Nguyen URL: https://github.com/qpmnguyen/CBEA, https://qpmnguyen.github.io/CBEA/ VignetteBuilder: knitr BugReports: https://github.com/qpmnguyen/CBEA//issues git_url: https://git.bioconductor.org/packages/CBEA git_branch: RELEASE_3_16 git_last_commit: 106fc67 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CBEA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CBEA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CBEA_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CBEA_1.2.0.tgz vignettes: vignettes/CBEA/inst/doc/basic_usage.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CBEA/inst/doc/basic_usage.R dependencyCount: 132 Package: cBioPortalData Version: 2.10.3 Depends: R (>= 4.2.0), AnVIL (>= 1.7.1), MultiAssayExperiment Imports: BiocFileCache (>= 1.5.3), digest, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, knitr, survival, survminer, rmarkdown, testthat License: AGPL-3 MD5sum: 784d9c462a7386e9e290927827321a6e NeedsCompilation: no Title: Exposes and makes available data from the cBioPortal web resources Description: The cBioPortalData R package accesses study datasets from the cBio Cancer Genomics Portal. It accesses the data either from the pre-packaged zip / tar files or from the API interface that was recently implemented by the cBioPortal Data Team. The package can provide data in either tabular format or with MultiAssayExperiment object that uses familiar Bioconductor data representations. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] (), Karim Mezhoud [ctb] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: RELEASE_3_16 git_last_commit: 8302e09 git_last_commit_date: 2023-01-03 Date/Publication: 2023-01-04 source.ver: src/contrib/cBioPortalData_2.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/cBioPortalData_2.10.3.zip mac.binary.ver: bin/macosx/contrib/4.2/cBioPortalData_2.10.3.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html, vignettes/cBioPortalData/inst/doc/cgdsrMigration.html vignetteTitles: cBioPortalData User Guide, cBioPortal Data Build Errors, cBioPortal Developer Guide, cgdsr to cBioPortalData Migration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R, vignettes/cBioPortalData/inst/doc/cBioPortalDataErrors.R, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R, vignettes/cBioPortalData/inst/doc/cgdsrMigration.R importsMe: cbaf, LowMACA, g3viz dependencyCount: 144 Package: cbpManager Version: 1.6.0 Depends: shiny, shinydashboard Imports: utils, DT, htmltools, vroom, plyr, dplyr, magrittr, jsonlite, rapportools, basilisk, reticulate, shinyBS, shinycssloaders, rintrojs, markdown Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE MD5sum: 0a95ceef8400c5025368ef64e6b32724 NeedsCompilation: no Title: Generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics Description: This R package provides an R Shiny application that enables the user to generate, manage, and edit data and metadata files suitable for the import in cBioPortal for Cancer Genomics. Create cancer studies and edit its metadata. Upload mutation data of a patient that will be concatenated to the data_mutation_extended.txt file of the study. Create and edit clinical patient data, sample data, and timeline data. Create custom timeline tracks for patients. biocViews: ImmunoOncology, DataImport, DataRepresentation, GUI, ThirdPartyClient, Preprocessing, Visualization Author: Arsenij Ustjanzew [aut, cre, cph] (), Federico Marini [aut] () Maintainer: Arsenij Ustjanzew URL: https://arsenij-ust.github.io/cbpManager/index.html VignetteBuilder: knitr BugReports: https://github.com/arsenij-ust/cbpManager/issues git_url: https://git.bioconductor.org/packages/cbpManager git_branch: RELEASE_3_16 git_last_commit: c194d7a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cbpManager_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cbpManager_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cbpManager_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cbpManager_1.6.0.tgz vignettes: vignettes/cbpManager/inst/doc/intro.html vignetteTitles: intro.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cbpManager/inst/doc/intro.R dependencyCount: 90 Package: ccfindR Version: 1.18.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 8ea675f3900bdcaf4dddf8be74a1695d NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: RELEASE_3_16 git_last_commit: 7bbe315 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ccfindR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccfindR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ccfindR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ccfindR_1.18.0.tgz vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R suggestsMe: MutationalPatterns dependencyCount: 39 Package: ccImpute Version: 1.0.2 Imports: Rcpp, matrixStats, stats, SIMLR, BiocParallel LinkingTo: Rcpp, RcppEigen Suggests: knitr, rmarkdown, BiocStyle, sessioninfo, scRNAseq, scater, SingleCellExperiment, mclust, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 1ac1abc19bb44bf0c2249b6a297363ef NeedsCompilation: yes Title: ccImpute: an accurate and scalable consensus clustering based approach to impute dropout events in the single-cell RNA-seq data (https://doi.org/10.1186/s12859-022-04814-8) Description: Dropout events make the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. ccImpute is an imputation algorithm that uses cell similarity established by consensus clustering to impute the most probable dropout events in the scRNA-seq datasets. ccImpute demonstrated performance which exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities. biocViews: SingleCell, PrincipalComponent, DimensionReduction, Clustering, RNASeq, Transcriptomics Author: Marcin Malec [cre, aut] () Maintainer: Marcin Malec VignetteBuilder: knitr BugReports: https://github.com/khazum/ccImpute/issues git_url: https://git.bioconductor.org/packages/ccImpute git_branch: RELEASE_3_16 git_last_commit: 6666ce4 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/ccImpute_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccImpute_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ccImpute_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ccImpute_1.0.2.tgz vignettes: vignettes/ccImpute/inst/doc/ccImpute.html vignetteTitles: ccImpute package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccImpute/inst/doc/ccImpute.R dependencyCount: 25 Package: ccmap Version: 1.24.0 Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE MD5sum: 2d0f89a7ecd5e5d211c68a1eb0ddeddb NeedsCompilation: no Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccmap git_branch: RELEASE_3_16 git_last_commit: 724592b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ccmap_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccmap_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ccmap_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ccmap_1.24.0.tgz vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html vignetteTitles: ccmap vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R dependencyCount: 60 Package: CCPROMISE Version: 1.24.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: e5df52ec7c541dbe5abf101f600d5a40 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: RELEASE_3_16 git_last_commit: 75bf4c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CCPROMISE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CCPROMISE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CCPROMISE_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CCPROMISE_1.24.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 53 Package: ccrepe Version: 1.34.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: c6b2ef4dd4e56156cde5af1694668736 NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: RELEASE_3_16 git_last_commit: e55fb2d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ccrepe_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ccrepe_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ccrepe_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ccrepe_1.34.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R dependencyCount: 1 Package: celaref Version: 1.16.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: def8841541fbb88e90e389155856ee82 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: RELEASE_3_16 git_last_commit: 2556728 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/celaref_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/celaref_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/celaref_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/celaref_1.16.0.tgz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 76 Package: celda Version: 1.14.2 Depends: R (>= 4.0), SingleCellExperiment, Matrix Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, methods, reshape2, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, scater (>= 1.14.4), scran, dbscan, DelayedArray, stringr, ComplexHeatmap, multipanelfigure, circlize LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, TENxPBMCData, singleCellTK, M3DExampleData License: MIT + file LICENSE Archs: x64 MD5sum: 2603abcb61b99eccd9d29dd81c68015b NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian, ImmunoOncology, DataImport Author: Joshua Campbell [aut, cre], Shiyi Yang [aut], Zhe Wang [aut], Sean Corbett [aut], Yusuke Koga [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: RELEASE_3_16 git_last_commit: b0beb97 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/celda_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/celda_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/celda_1.14.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/celda_1.14.2.tgz vignettes: vignettes/celda/inst/doc/celda.html, vignettes/celda/inst/doc/decontX.html vignetteTitles: Analysis of single-cell genomic data with celda, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda.R, vignettes/celda/inst/doc/decontX.R importsMe: singleCellTK dependencyCount: 144 Package: CellaRepertorium Version: 1.8.0 Depends: R (>= 4.0) Imports: dplyr, tibble, stringr, Biostrings, Rcpp, reshape2, methods, rlang (>= 0.3), purrr, Matrix, S4Vectors, BiocGenerics, tidyr, forcats, progress, stats, utils, generics, glue LinkingTo: Rcpp Suggests: testthat, readr, knitr, rmarkdown, ggplot2, BiocStyle, ggdendro, broom, lme4, RColorBrewer, SingleCellExperiment, scater, broom.mixed, cowplot, igraph, ggraph License: GPL-3 Archs: x64 MD5sum: 48a4dd0487cead52230abf5ed7e7a0af NeedsCompilation: yes Title: Data structures, clustering and testing for single cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq) Description: Methods to cluster and analyze high-throughput single cell immune cell repertoires, especially from the 10X Genomics VDJ solution. Contains an R interface to CD-HIT (Li and Godzik 2006). Methods to visualize and analyze paired heavy-light chain data. Tests for specific expansion, as well as omnibus oligoclonality under hypergeometric models. biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing, Technology, ImmunoOncology, Clustering Author: Andrew McDavid [aut, cre], Yu Gu [aut], Erik VonKaenel [aut], Aaron Wagner [aut], Thomas Lin Pedersen [ctb] Maintainer: Andrew McDavid URL: https://github.com/amcdavid/CellaRepertorium VignetteBuilder: knitr BugReports: https://github.com/amcdavid/CellaRepertorium/issues git_url: https://git.bioconductor.org/packages/CellaRepertorium git_branch: RELEASE_3_16 git_last_commit: 5346a3b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellaRepertorium_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellaRepertorium_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellaRepertorium_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellaRepertorium_1.8.0.tgz vignettes: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.html, vignettes/CellaRepertorium/inst/doc/cr-overview.html, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.html, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.html vignetteTitles: Clustering and differential usage of repertoire CDR3 sequences, An Introduction to CellaRepertorium, Quality control and Exploration of UMI-based repertoire data, Combining Repertoire with Expression with SingleCellExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.R, vignettes/CellaRepertorium/inst/doc/cr-overview.R, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.R, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.R dependencyCount: 49 Package: CellBarcode Version: 1.4.0 Depends: R (>= 4.1.0) Imports: methods, stats, Rcpp (>= 1.0.5), data.table (>= 1.12.6), plyr, ggplot2, stringr, magrittr, ShortRead (>= 1.48.0), Biostrings (>= 2.58.0), egg, Ckmeans.1d.dp, utils, S4Vectors, seqinr, zlibbioc LinkingTo: Rcpp, BH Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 1fd5df9967ebe140361b27ea20d1e84b NeedsCompilation: yes Title: Cellular DNA Barcode Analysis toolkit Description: This package performs Cellular DNA Barcode (genetic lineage tracing) analysis. The package can handle all kinds of DNA barcodes, as long as the barcode within a single sequencing read and has a pattern which can be matched by a regular expression. This package can handle barcode with flexible length, with or without UMI (unique molecular identifier). This tool also can be used for pre-processing of some amplicon sequencing such as CRISPR gRNA screening, immune repertoire sequencing and meta genome data. biocViews: Preprocessing, QualityControl, Sequencing, CRISPR Author: Wenjie Sun [cre], Anne-Marie Lyne [aut], Leila Perie [aut] Maintainer: Wenjie Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellBarcode git_branch: RELEASE_3_16 git_last_commit: 6685d49 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellBarcode_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellBarcode_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellBarcode_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellBarcode_1.4.0.tgz vignettes: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.html, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.html vignetteTitles: 10X_Barcode, UMI_Barcode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CellBarcode/inst/doc/Barcode_in_10X_scRNASeq.R, vignettes/CellBarcode/inst/doc/UMI_VDJ_Barcode.R dependencyCount: 91 Package: cellbaseR Version: 1.22.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: c083374dafa795b5ebe6fb233f3d6ed3 NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: RELEASE_3_16 git_last_commit: 03e6b83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cellbaseR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellbaseR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellbaseR_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cellbaseR_1.22.0.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 66 Package: CellBench Version: 1.14.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: BiocGenerics, BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 MD5sum: aa09efb7df8ad188a110e6dea609c455 NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: RELEASE_3_16 git_last_commit: 19cfb0e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellBench_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellBench_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellBench_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellBench_1.14.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.pdf, vignettes/CellBench/inst/doc/TidyversePatterns.pdf, vignettes/CellBench/inst/doc/CellBenchCaseStudy.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Tidyverse Patterns, CellBenchCaseStudy.html, Introduction, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R suggestsMe: corral dependencyCount: 78 Package: cellHTS2 Version: 2.62.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: GSEABase, Category, stats4, BiocGenerics Suggests: ggplot2 License: Artistic-2.0 MD5sum: b205b46809e7b5f298b169cd4f5b8820 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber git_url: https://git.bioconductor.org/packages/cellHTS2 git_branch: RELEASE_3_16 git_last_commit: 02b3576 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cellHTS2_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellHTS2_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellHTS2_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cellHTS2_2.62.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: imageHTS, staRank importsMe: gespeR, RNAinteract suggestsMe: bioassayR dependencyCount: 88 Package: CelliD Version: 1.6.2 Depends: R (>= 4.1), Seurat (>= 4.0.1), SingleCellExperiment Imports: Rcpp, RcppArmadillo, stats, utils, Matrix, tictoc, scater, stringr, irlba, data.table, glue, pbapply, umap, Rtsne, reticulate, fastmatch, matrixStats, ggplot2, BiocParallel, SummarizedExperiment, fgsea LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, BiocStyle, testthat, tidyverse, ggpubr, destiny, ggrepel License: GPL-3 + file LICENSE Archs: x64 MD5sum: d2064f0a093ee693877f42a65a3e4a6b NeedsCompilation: yes Title: Unbiased Extraction of Single Cell gene signatures using Multiple Correspondence Analysis Description: CelliD is a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell RNA-seq. CelliD allows unbiased cell identity recognition across different donors, tissues-of-origin, model organisms and single-cell omics protocols. The package can also be used to explore functional pathways enrichment in single cell data. biocViews: RNASeq, SingleCell, DimensionReduction, Clustering, GeneSetEnrichment, GeneExpression, ATACSeq Author: Akira Cortal [aut, cre], Antonio Rausell [aut, ctb] Maintainer: Akira Cortal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CelliD git_branch: RELEASE_3_16 git_last_commit: 92f1ce7 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/CelliD_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/CelliD_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.2/CelliD_1.6.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CelliD_1.6.2.tgz vignettes: vignettes/CelliD/inst/doc/BioconductorVignette.html vignetteTitles: CelliD Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CelliD/inst/doc/BioconductorVignette.R dependencyCount: 197 Package: cellity Version: 1.26.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: 6b0693fd2235f9007ee4c51d0437ed1a NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: RELEASE_3_16 git_last_commit: fa441f5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cellity_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellity_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellity_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cellity_1.26.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 83 Package: CellMapper Version: 1.24.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 22bf22e2b604129c559f9b548377ef66 NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: RELEASE_3_16 git_last_commit: 26dfb1b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellMapper_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellMapper_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellMapper_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellMapper_1.24.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependsOnMe: CellMapperData dependencyCount: 7 Package: cellmigRation Version: 1.6.0 Depends: R (>= 4.1), methods, foreach Imports: tiff, graphics, stats, utils, reshape2, parallel, doParallel, grDevices, matrixStats, FME, SpatialTools, sp, vioplot, FactoMineR, Hmisc Suggests: knitr, rmarkdown, dplyr, ggplot2, RUnit, BiocGenerics, BiocManager, kableExtra, rgl License: GPL-2 MD5sum: 0e7346ecd57612d11e6d9bce52ff0faa NeedsCompilation: no Title: Track Cells, Analyze Cell Trajectories and Compute Migration Statistics Description: Import TIFF images of fluorescently labeled cells, and track cell movements over time. Parallelization is supported for image processing and for fast computation of cell trajectories. In-depth analysis of cell trajectories is enabled by 15 trajectory analysis functions. biocViews: CellBiology, DataRepresentation, DataImport Author: Salim Ghannoum [aut, cph], Damiano Fantini [aut, cph], Waldir Leoncio [cre, aut], Øystein Sørensen [aut] Maintainer: Waldir Leoncio URL: https://github.com/ocbe-uio/cellmigRation/ VignetteBuilder: knitr BugReports: https://github.com/ocbe-uio/cellmigRation/issues git_url: https://git.bioconductor.org/packages/cellmigRation git_branch: RELEASE_3_16 git_last_commit: f639003 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cellmigRation_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellmigRation_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellmigRation_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cellmigRation_1.6.0.tgz vignettes: vignettes/cellmigRation/inst/doc/cellmigRation.html vignetteTitles: cellmigRation hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellmigRation/inst/doc/cellmigRation.R dependencyCount: 146 Package: CellMixS Version: 1.14.0 Depends: kSamples, R (>= 4.0) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne License: GPL (>=2) MD5sum: 8d530f1dd51eed4e5f116cfb4f3eb069 NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge [aut, cre] Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: RELEASE_3_16 git_last_commit: 8a0f25b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellMixS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellMixS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellMixS_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellMixS_1.14.0.tgz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 108 Package: CellNOptR Version: 1.44.0 Depends: R (>= 4.0.0), RBGL, graph, methods, RCurl, Rgraphviz, XML, ggplot2, rmarkdown Imports: igraph, stringi, stringr Suggests: data.table, dplyr, tidyr, readr, knitr, RUnit, BiocGenerics, Enhances: doParallel, foreach License: GPL-3 Archs: x64 MD5sum: e438afa72966a32111828601470f9750 NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network, TimeCourse, ImmunoOncology Author: Thomas Cokelaer [aut], Federica Eduati [aut], Aidan MacNamara [aut], S Schrier [ctb], Camille Terfve [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor SystemRequirements: Graphviz version >= 2.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_16 git_last_commit: 8b4cf64 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellNOptR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellNOptR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellNOptR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellNOptR_1.44.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.html vignetteTitles: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data with CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode importsMe: bnem suggestsMe: MEIGOR dependencyCount: 71 Package: cellscape Version: 1.22.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 1701c0939c4550c1ef73fc2fff0e2d81 NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: RELEASE_3_16 git_last_commit: 23a29dc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cellscape_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellscape_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellscape_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cellscape_1.22.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 51 Package: CellScore Version: 1.18.0 Depends: R (>= 3.5.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0) Suggests: hgu133plus2CellScore, knitr License: GPL-3 MD5sum: aaebfc5737cde4ceea6edce15ea93a66 NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah, Katerina Taskova Maintainer: Nancy Mah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: RELEASE_3_16 git_last_commit: da4bc20 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellScore_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellScore_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellScore_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellScore_1.18.0.tgz vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf vignetteTitles: R packages: CellScore hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R dependencyCount: 16 Package: CellTrails Version: 1.16.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 MD5sum: ed5caae7127f8a9705808b4aad121f41 NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: RELEASE_3_16 git_last_commit: cfff969 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CellTrails_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CellTrails_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CellTrails_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CellTrails_1.16.0.tgz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 74 Package: cellTree Version: 1.27.0 Depends: R (>= 3.3), topGO Imports: topicmodels, slam, maptpx, igraph, xtable, gplots Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db, Biobase, tools License: Artistic-2.0 MD5sum: 09dd76ef8fac155feb230900554f963f NeedsCompilation: no Title: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure Description: This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray Author: David duVerle [aut, cre], Koji Tsuda [aut] Maintainer: David duVerle URL: http://tsudalab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellTree git_branch: master git_last_commit: 5667f3b git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/cellTree_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellTree_1.27.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cellTree_1.27.0.tgz vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data data as a hierarchical tree structure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R dependencyCount: 71 Package: cellxgenedp Version: 1.2.2 Depends: dplyr Imports: httr, curl, jsonlite, utils, tools, shiny, DT, rjsoncons Suggests: zellkonverter, SingleCellExperiment, HDF5Array, BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0), mockery License: Artistic-2.0 MD5sum: a527093bfc882291b356a95354dcb16e NeedsCompilation: no Title: Discover and Access Single Cell Data Sets in the cellxgene Data Portal Description: The cellxgene data portal (https://cellxgene.cziscience.com/) provides a graphical user interface to collections of single-cell sequence data processed in standard ways to 'count matrix' summaries. The cellxgenedp package provides an alternative, R-based inteface, allowind data discovery, viewing, and downloading. biocViews: SingleCell, DataImport, ThirdPartyClient Author: Martin Morgan [aut, cre] (), Kayla Interdonato [aut] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellxgenedp git_branch: RELEASE_3_16 git_last_commit: 9687a46 git_last_commit_date: 2023-01-31 Date/Publication: 2023-02-01 source.ver: src/contrib/cellxgenedp_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/cellxgenedp_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cellxgenedp_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cellxgenedp_1.2.2.tgz vignettes: vignettes/cellxgenedp/inst/doc/using_cellxgenedp.html vignetteTitles: Discover and download datasets and files from the cellxgene data portal hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellxgenedp/inst/doc/using_cellxgenedp.R dependencyCount: 66 Package: CEMiTool Version: 1.22.0 Depends: R (>= 4.0) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable, fastcluster Suggests: testthat, BiocManager License: GPL-3 MD5sum: 2fd357e544a8cd2072467d91315c1a0a NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: RELEASE_3_16 git_last_commit: 4e4a030 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CEMiTool_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CEMiTool_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CEMiTool_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CEMiTool_1.22.0.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 192 Package: censcyt Version: 1.6.1 Depends: R (>= 4.0), diffcyt Imports: BiocParallel, broom.mixed, dirmult, dplyr, edgeR, fitdistrplus, lme4, magrittr, MASS, methods, mice, multcomp, purrr, rlang, S4Vectors, stats, stringr, SummarizedExperiment, survival, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2 License: MIT + file LICENSE MD5sum: 4c30f4eba33c97242e721d741a9285b6 NeedsCompilation: no Title: Differential abundance analysis with a right censored covariate in high-dimensional cytometry Description: Methods for differential abundance analysis in high-dimensional cytometry data when a covariate is subject to right censoring (e.g. survival time) based on multiple imputation and generalized linear mixed models. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software, Survival Author: Reto Gerber [aut, cre] () Maintainer: Reto Gerber URL: https://github.com/retogerber/censcyt VignetteBuilder: knitr BugReports: https://github.com/retogerber/censcyt/issues git_url: https://git.bioconductor.org/packages/censcyt git_branch: RELEASE_3_16 git_last_commit: 91a9cbe git_last_commit_date: 2023-02-16 Date/Publication: 2023-02-16 source.ver: src/contrib/censcyt_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/censcyt_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/censcyt_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/censcyt_1.6.1.tgz vignettes: vignettes/censcyt/inst/doc/censored_covariate.html vignetteTitles: Censored covariate hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/censcyt/inst/doc/censored_covariate.R dependencyCount: 164 Package: Cepo Version: 1.4.0 Depends: GSEABase, R (>= 4.1) Imports: DelayedMatrixStats, DelayedArray, HDF5Array, S4Vectors, methods, SingleCellExperiment, SummarizedExperiment, ggplot2, rlang, grDevices, patchwork, reshape2, BiocParallel, stats, dplyr Suggests: knitr, rmarkdown, BiocStyle, testthat, covr, UpSetR, scater, scMerge, fgsea, escape, pheatmap, patchwork License: MIT + file LICENSE MD5sum: b2a6d789154bb11b06542da41130f855 NeedsCompilation: no Title: Cepo for the identification of differentially stable genes Description: Defining the identity of a cell is fundamental to understand the heterogeneity of cells to various environmental signals and perturbations. We present Cepo, a new method to explore cell identities from single-cell RNA-sequencing data using differential stability as a new metric to define cell identity genes. Cepo computes cell-type specific gene statistics pertaining to differential stable gene expression. biocViews: Classification, GeneExpression, SingleCell, Software, Sequencing, DifferentialExpression Author: Hani Jieun Kim [aut, cre] (), Kevin Wang [aut] () Maintainer: Hani Jieun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cepo git_branch: RELEASE_3_16 git_last_commit: 7410238 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Cepo_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cepo_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cepo_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Cepo_1.4.0.tgz vignettes: vignettes/Cepo/inst/doc/cepo.html vignetteTitles: Cepo method for differential stability analysis of scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cepo/inst/doc/cepo.R importsMe: scClassify dependencyCount: 104 Package: ceRNAnetsim Version: 1.10.0 Depends: R (>= 4.0.0), dplyr, tidygraph Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr, future, stats Suggests: knitr, png, rmarkdown, testthat, covr License: GPL (>= 3.0) MD5sum: 0ba92d9314ddb687b61cbf1dc4c6f865 NeedsCompilation: no Title: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA) Description: This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables. biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork, Transcriptomics Author: Selcen Ari Yuka [aut, cre] (), Alper Yilmaz [aut] () Maintainer: Selcen Ari Yuka URL: https://github.com/selcenari/ceRNAnetsim VignetteBuilder: knitr BugReports: https://github.com/selcenari/ceRNAnetsim/issues git_url: https://git.bioconductor.org/packages/ceRNAnetsim git_branch: RELEASE_3_16 git_last_commit: 3692bc4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ceRNAnetsim_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ceRNAnetsim_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ceRNAnetsim_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ceRNAnetsim_1.10.0.tgz vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html, vignettes/ceRNAnetsim/inst/doc/basic_usage.html, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to Find the Appropriate Iteration for Simulation, An TCGA dataset application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R, vignettes/ceRNAnetsim/inst/doc/basic_usage.R, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R dependencyCount: 66 Package: CeTF Version: 1.10.2 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, dplyr, GenomicTools.fileHandler, GGally, ggnetwork, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, stats, SummarizedExperiment, S4Vectors, utils, methods LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 28cedf0c4cd853a975391fc707b40af8 NeedsCompilation: yes Title: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis Description: This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data). biocViews: Sequencing, RNASeq, Microarray, GeneExpression, Transcription, Normalization, DifferentialExpression, SingleCell, Network, Regression, ChIPSeq, ImmunoOncology, Coverage Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo Bianchi Ximenez [ctb], Patrícia de Cássia Ruy [ctb], Marcelo Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson Araújo da Silva Junior [aut, ths] Maintainer: Carlos Alberto Oliveira de Biagi Junior SystemRequirements: libcurl4-openssl-dev, libxml2-dev, libssl-dev, gfortran, build-essential, libz-dev, zlib1g-dev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CeTF git_branch: RELEASE_3_16 git_last_commit: b161152 git_last_commit_date: 2023-01-26 Date/Publication: 2023-01-26 source.ver: src/contrib/CeTF_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/CeTF_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/CeTF_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CeTF_1.10.2.tgz vignettes: vignettes/CeTF/inst/doc/CeTF.html vignetteTitles: Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CeTF/inst/doc/CeTF.R dependencyCount: 217 Package: CexoR Version: 1.36.0 Depends: R (>= 4.2.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: e7f2187bbbfee2532977ab3c274e3c08 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Then, irreproducible discovery rate for overlapping peak-pairs across biological replicates is computed. biocViews: FunctionalGenomics, Sequencing, Coverage, ChIPSeq, PeakDetection Author: Pedro Madrigal [aut, cre] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/CexoR git_branch: RELEASE_3_16 git_last_commit: 69bd679 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CexoR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CexoR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CexoR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CexoR_1.36.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 97 Package: CFAssay Version: 1.32.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: 36ca55c8314da07a31bd1b8a4fba1981 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: RELEASE_3_16 git_last_commit: d20143b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CFAssay_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CFAssay_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CFAssay_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CFAssay_1.32.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: cfDNAPro Version: 1.4.0 Depends: R (>= 4.1.0), magrittr (>= 1.5.0) Imports: tibble, GenomicAlignments, IRanges, plyranges, GenomeInfoDb, GenomicRanges, BiocGenerics, stats, utils, dplyr (>= 0.8.3), stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1), Rsamtools (>= 2.4.0), rlang (>= 0.4.0) Suggests: BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: fbf3fc44c1dc0538e3a0d36352e3b13e NeedsCompilation: no Title: cfDNAPro extracts and Visualises biological features from whole genome sequencing data of cell-free DNA Description: cfDNA fragments carry important features for building cancer sample classification ML models, such as fragment size, and fragment end motif etc. Analyzing and visualizing fragment size metrics, as well as other biological features in a curated, standardized, scalable, well-documented, and reproducible way might be time intensive. This package intends to resolve these problems and simplify the process. It offers two sets of functions for cfDNA feature characterization and visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Hui Zhao [ctb], Elkie Chan [ctb], Christopher Smith [ctb], Tomer Kaplan [ctb], Florian Markowetz [ctb], Nitzan Rosenfeld [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: RELEASE_3_16 git_last_commit: e32b87d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cfDNAPro_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cfDNAPro_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cfDNAPro_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cfDNAPro_1.4.0.tgz vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html vignetteTitles: cfDNAPro Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R dependencyCount: 85 Package: CGEN Version: 3.34.3 Depends: R (>= 4.0), survival, mvtnorm Imports: stats, graphics, utils, grDevices Suggests: cluster License: GPL-2 + file LICENSE Archs: x64 MD5sum: 2148ba0c8f18721bb7deb3ef2ace1a2c NeedsCompilation: yes Title: An R package for analysis of case-control studies in genetic epidemiology Description: This is a package for analysis of case-control data in genetic epidemiology. It provides a set of statistical methods for evaluating gene-environment (or gene-genes) interactions under multiplicative and additive risk models, with or without assuming gene-environment (or gene-gene) independence in the underlying population. biocViews: SNP, MultipleComparison, Clustering Author: Samsiddhi Bhattacharjee [aut], Nilanjan Chatterjee [aut], Summer Han [aut], Minsun Song [aut], William Wheeler [aut], Matthieu de Rochemonteix [aut], Nilotpal Sanyal [aut], Justin Lee [cre] Maintainer: Justin Lee git_url: https://git.bioconductor.org/packages/CGEN git_branch: RELEASE_3_16 git_last_commit: b7e5a46 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/CGEN_3.34.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGEN_3.34.3.zip mac.binary.ver: bin/macosx/contrib/4.2/CGEN_3.34.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CGEN_3.34.3.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Scan Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R dependencyCount: 11 Package: CGHbase Version: 1.58.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: ce342c83c38a08c2ef580abb3333a36e NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: RELEASE_3_16 git_last_commit: fd24024 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CGHbase_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHbase_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHbase_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CGHbase_1.58.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq, ragt2ridges dependencyCount: 9 Package: CGHcall Version: 2.60.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: ab902489569708506d010a32d9100d0d NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: RELEASE_3_16 git_last_commit: 3c4d2aa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CGHcall_2.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHcall_2.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHcall_2.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CGHcall_2.60.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 14 Package: cghMCR Version: 1.56.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 9c7845e115b75ebff33c9b64b94c2297 NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: RELEASE_3_16 git_last_commit: f472286 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cghMCR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cghMCR_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cghMCR_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cghMCR_1.56.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 58 Package: CGHnormaliter Version: 1.52.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: ea6aef7b8e212d64a85aa6fbfff849f3 NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: RELEASE_3_16 git_last_commit: 27c7682 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CGHnormaliter_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHnormaliter_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHnormaliter_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CGHnormaliter_1.52.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 15 Package: CGHregions Version: 1.56.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 5316ebaef92d8e2b990043fe7710a754 NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: RELEASE_3_16 git_last_commit: 42276e8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CGHregions_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CGHregions_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CGHregions_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CGHregions_1.56.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 10 Package: ChAMP Version: 2.28.0 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,kpmt,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: 02dc474ce839ed542579b8f49ff2511c NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_16 git_last_commit: 3d27ac6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChAMP_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChAMP_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChAMP_2.28.0.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R dependencyCount: 255 Package: ChemmineOB Version: 1.36.0 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp, zlibbioc Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager, rmarkdown Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: x64 MD5sum: 46652319b1500546c3c7c224b9f5e7ed NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 3.0.0) with headers (http://openbabel.org). Eigen3 with headers. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: RELEASE_3_16 git_last_commit: 666338d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChemmineOB_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChemmineOB_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChemmineOB_1.36.0.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R dependencyCount: 8 Package: ChemmineR Version: 3.50.0 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg,jsonlite,stringi LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs, png,rmarkdown, BiocManager,bibtex Enhances: ChemmineOB License: Artistic-2.0 Archs: x64 MD5sum: e3d8cedf5f17fd8e90cf7924e2eec5e0 NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: RELEASE_3_16 git_last_commit: 79a4b34 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChemmineR_3.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChemmineR_3.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChemmineR_3.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChemmineR_3.50.0.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR, ChemmineDrugs importsMe: bioassayR, CompoundDb, customCMPdb, eiR, fmcsR, MetID, chemodiv, DrugSim2DR, DRviaSPCN, uCAREChemSuiteCLI suggestsMe: ChemmineOB, xnet dependencyCount: 76 Package: CHETAH Version: 1.14.0 Depends: R (>= 4.2), ggplot2, SingleCellExperiment Imports: shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE MD5sum: a52a859e01182cf8c33581b13ba93bae NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering, GeneExpression, ImmunoOncology Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: RELEASE_3_16 git_last_commit: 55fbdbc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CHETAH_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CHETAH_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CHETAH_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CHETAH_1.14.0.tgz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R dependencyCount: 113 Package: ChIC Version: 1.18.0 Depends: spp, R (>= 3.6) Imports: ChIC.data (>= 1.11.1), caTools, methods, GenomicRanges, IRanges, parallel, progress, randomForest, caret, grDevices, stats, utils, graphics, S4Vectors, BiocGenerics, genomeIntervals, Rsamtools License: GPL-2 MD5sum: 70a3090cf6bb12d00eb12c6cfd5c6ac8 NeedsCompilation: no Title: Quality Control Pipeline for ChIP-Seq Data Description: Quality control (QC) pipeline for ChIP-seq data using a comprehensive set of QC metrics, including previously proposed metrics as well as novel ones, based on local characteristics of the enrichment profile. The package provides functions to calculate a set of QC metrics, a compendium with reference values and machine learning models to score sample quality. biocViews: ChIPSeq, QualityControl Author: Carmen Maria Livi Maintainer: Carmen Maria Livi git_url: https://git.bioconductor.org/packages/ChIC git_branch: RELEASE_3_16 git_last_commit: 728615f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIC_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ChIC_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIC_1.18.0.tgz vignettes: vignettes/ChIC/inst/doc/ChIC-Vignette.pdf vignetteTitles: ChIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIC/inst/doc/ChIC-Vignette.R dependencyCount: 116 Package: Chicago Version: 1.26.0 Depends: R (>= 3.3.1), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: b9c3508624df6f3cebbc449010119456 NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: RELEASE_3_16 git_last_commit: 7c0e4a9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Chicago_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Chicago_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Chicago_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Chicago_1.26.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependsOnMe: PCHiCdata dependencyCount: 76 Package: chimeraviz Version: 1.24.0 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, checkmate, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 7f4150f23a890de4cd7b08c83320ce6b NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: RELEASE_3_16 git_last_commit: 0f9835a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chimeraviz_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chimeraviz_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chimeraviz_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chimeraviz_1.24.0.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 168 Package: ChIPanalyser Version: 1.20.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb,RColorBrewer Suggests: BSgenome.Dmelanogaster.UCSC.dm6,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 7fd61fb3752887826cd04c8ccb89fdf8 NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: ChIPanalyser is a package to predict and understand TF binding by utilizing a statistical thermodynamic model. The model incorporates 4 main factors thought to drive TF binding: Chromatin State, Binding energy, Number of bound molecules and a scaling factor modulating TF binding affinity. Taken together, ChIPanalyser produces ChIP-like profiles that closely mimic the patterns seens in real ChIP-seq data. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: RELEASE_3_16 git_last_commit: ca0ec5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPanalyser_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPanalyser_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPanalyser_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPanalyser_1.20.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide, ChIPanalyser User's Guide for Genetic Algorithms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R, vignettes/ChIPanalyser/inst/doc/GA_ChIPanalyser.R dependencyCount: 56 Package: ChIPComp Version: 1.28.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL Archs: x64 MD5sum: ebe6fa661b216dc006bafd1e9e0bc6c4 NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: RELEASE_3_16 git_last_commit: 690350e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPComp_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPComp_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPComp_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPComp_1.28.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R dependencyCount: 50 Package: chipenrich Version: 2.22.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: 3042e3493296572812e80941c35ed137 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut], Kai Wang [cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: RELEASE_3_16 git_last_commit: 9191beb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chipenrich_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chipenrich_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chipenrich_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chipenrich_2.22.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 166 Package: ChIPexoQual Version: 1.22.0 Depends: R (>= 3.5.0), GenomicAlignments (>= 1.0.1) Imports: methods, utils, GenomeInfoDb, stats, BiocParallel, GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: 7386edb04765f5dfdd4338b2a4f770ff NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: RELEASE_3_16 git_last_commit: 60f340b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPexoQual_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPexoQual_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPexoQual_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPexoQual_1.22.0.tgz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 148 Package: ChIPpeakAnno Version: 3.32.0 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), DBI, dplyr, ensembldb, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, InteractionSet, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils Suggests: AnnotationHub, BSgenome, limma, reactome.db, BiocManager, BiocStyle, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: e7776d3eea21385d4e3c394fc8aa3424 NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_16 git_last_commit: 016661f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPpeakAnno_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPpeakAnno_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPpeakAnno_3.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPpeakAnno_3.32.0.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/FAQs.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/FAQs.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R dependsOnMe: REDseq, csawBook importsMe: ATACseqQC, DEScan2, GUIDEseq suggestsMe: R3CPET, seqsetvis, chipseqDB dependencyCount: 123 Package: ChIPQC Version: 1.34.1 Depends: R (>= 3.5.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19), BiocParallel Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: ca1f27bd26ae5cd83fe4abf0ae5c9dd1 NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: RELEASE_3_16 git_last_commit: dabb39a git_last_commit_date: 2023-01-10 Date/Publication: 2023-01-10 source.ver: src/contrib/ChIPQC_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPQC_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPQC_1.34.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPQC_1.34.1.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R dependencyCount: 176 Package: ChIPseeker Version: 1.34.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils Suggests: clusterProfiler, ggimage, ggplotify, ggupset, ggVennDiagram, ReactomePA, org.Hs.eg.db, knitr, rmarkdown, testthat, tibble License: Artistic-2.0 MD5sum: 08517b750d902389822f02dcdd9ae93e NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (), Ming Li [ctb], Qianwen Wang [ctb], Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb] Maintainer: Guangchuang Yu URL: https://onlinelibrary.wiley.com/share/author/GYJGUBYCTRMYJFN2JFZZ?target=10.1002/cpz1.585 VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: RELEASE_3_16 git_last_commit: 4bf5e4f git_last_commit_date: 2022-11-20 Date/Publication: 2022-11-21 source.ver: src/contrib/ChIPseeker_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPseeker_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPseeker_1.34.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPseeker_1.34.1.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: EpiCompare, esATAC, segmenter, cinaR suggestsMe: GRaNIE, curatedAdipoChIP dependencyCount: 157 Package: chipseq Version: 1.48.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 Archs: x64 MD5sum: 12523fcdb2921dcd67ce0fd77d22ddf7 NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/chipseq git_branch: RELEASE_3_16 git_last_commit: 9c78296 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chipseq_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chipseq_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chipseq_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chipseq_1.48.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR dependencyCount: 51 Package: ChIPseqR Version: 1.52.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: x64 MD5sum: 0f10d9a727359519b003c354ad5460c8 NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: RELEASE_3_16 git_last_commit: 984d136 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPseqR_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPseqR_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPseqR_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPseqR_1.52.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 59 Package: ChIPsim Version: 1.52.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 73897564066708cf01ca43112dd3c895 NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: RELEASE_3_16 git_last_commit: 70641ce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPsim_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChIPsim_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChIPsim_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPsim_1.52.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 51 Package: ChIPXpress Version: 1.42.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 92fafe405e063e4f03ae1569dd4384eb NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: RELEASE_3_16 git_last_commit: 845364a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChIPXpress_1.42.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ChIPXpress_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChIPXpress_1.42.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 94 Package: chopsticks Version: 1.64.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: x64 MD5sum: 6fec4040cd526a97457c35334689e4cb NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: RELEASE_3_16 git_last_commit: d013026 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chopsticks_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chopsticks_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chopsticks_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chopsticks_1.64.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R dependencyCount: 10 Package: chromDraw Version: 2.28.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: x64 MD5sum: d14e634a9ce4023dc23fe1317bb6d201 NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: RELEASE_3_16 git_last_commit: 6922aac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chromDraw_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromDraw_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromDraw_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chromDraw_2.28.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 17 Package: ChromHeatMap Version: 1.52.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: 9eee313b94d630cbbf3180996dd6927b NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: RELEASE_3_16 git_last_commit: eebc617 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChromHeatMap_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChromHeatMap_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChromHeatMap_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChromHeatMap_1.52.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 74 Package: chromPlot Version: 1.26.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 92c0f6ecc20d34fe9497eb91bbad444e NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: RELEASE_3_16 git_last_commit: 72130bb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chromPlot_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromPlot_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromPlot_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chromPlot_1.26.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 73 Package: ChromSCape Version: 1.8.0 Depends: R (>= 4.1) Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles, shinyhelper, shinyWidgets, shinydashboardPlus, shinycssloaders, Matrix, plotly, shinydashboard, colorRamps, kableExtra, viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2, ggrepel, gggenes, gridExtra, qualV, stringdist, stringr, fs, qs, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR, stats, graphics, grDevices, utils, S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr, forcats, Rcpp, coop, matrixTests, DelayedArray LinkingTo: Rcpp Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, Signac, future, igraph, bluster, httr License: GPL-3 Archs: x64 MD5sum: 9b9d43cc77922c6921a8a2c40c7b3d9d NeedsCompilation: yes Title: Analysis of single-cell epigenomics datasets with a Shiny App Description: ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis. biocViews: ShinyApps, Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq, Classification, Clustering, Epigenetics, PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect, MultipleComparison, Normalization, Pathways, Preprocessing, QualityControl, ReportWriting, Visualization, GeneSetEnrichment, DifferentialPeakCalling Author: Pacome Prompsy [aut, cre] (), Celine Vallot [aut] () Maintainer: Pacome Prompsy URL: https://github.com/vallotlab/ChromSCape VignetteBuilder: knitr BugReports: https://github.com/vallotlab/ChromSCape/issues git_url: https://git.bioconductor.org/packages/ChromSCape git_branch: RELEASE_3_16 git_last_commit: d32bd33 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ChromSCape_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ChromSCape_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ChromSCape_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ChromSCape_1.8.0.tgz vignettes: vignettes/ChromSCape/inst/doc/vignette.html vignetteTitles: ChromSCape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromSCape/inst/doc/vignette.R dependencyCount: 215 Package: chromstaR Version: 1.24.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 Archs: x64 MD5sum: 2c25efa82fab1bd33690a6577700f19b NeedsCompilation: yes Title: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data Description: This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses. biocViews: ImmunoOncology, Software, DifferentialPeakCalling, HiddenMarkovModel, ChIPSeq, HistoneModification, MultipleComparison, Sequencing, PeakDetection, ATACSeq Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues git_url: https://git.bioconductor.org/packages/chromstaR git_branch: RELEASE_3_16 git_last_commit: a38e62b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chromstaR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromstaR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromstaR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chromstaR_1.24.0.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R dependencyCount: 78 Package: chromswitch Version: 1.20.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.26.4) Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>= 1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics, grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats (>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6), rtracklayer (>= 1.36.4), S4Vectors (>= 0.23.19), stats, tidyr (>= 0.6.3) Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3), GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3), testthat License: MIT + file LICENSE MD5sum: 64fc31f3238fd1bcffb0d93068b7c6af NeedsCompilation: no Title: An R package to detect chromatin state switches from epigenomic data Description: Chromswitch implements a flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data. biocViews: ImmunoOncology, MultipleComparison, Transcription, GeneExpression, DifferentialPeakCalling, HistoneModification, Epigenetics, FunctionalGenomics, Clustering Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut] Maintainer: Selin Jessa URL: https://github.com/sjessa/chromswitch VignetteBuilder: knitr BugReports: https://github.com/sjessa/chromswitch/issues git_url: https://git.bioconductor.org/packages/chromswitch git_branch: RELEASE_3_16 git_last_commit: cc484bf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/chromswitch_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromswitch_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/chromswitch_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chromswitch_1.20.0.tgz vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html vignetteTitles: An introduction to `chromswitch` for detecting chromatin state switches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R dependencyCount: 98 Package: chromVAR Version: 1.20.2 Depends: R (>= 3.5.0) Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE Archs: x64 MD5sum: ef863e0ecb0d442d151bb8d1a60e2c3b NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: RELEASE_3_16 git_last_commit: 4e0ed21 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/chromVAR_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/chromVAR_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/chromVAR_1.20.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/chromVAR_1.20.2.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R importsMe: ATACCoGAPS suggestsMe: Signac dependencyCount: 158 Package: CHRONOS Version: 1.26.0 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt, rJava Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-2 MD5sum: ca4a9eaadbb4f1a5a574e173eec68e59 NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: RELEASE_3_16 git_last_commit: 1b06943 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CHRONOS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CHRONOS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CHRONOS_1.26.0.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 90 Package: cicero Version: 1.16.2 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, markdown, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE MD5sum: b0b5995418250f045c280c3d6fd29a5b NeedsCompilation: no Title: Predict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology, GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: RELEASE_3_16 git_last_commit: 11394fb git_last_commit_date: 2023-02-25 Date/Publication: 2023-02-26 source.ver: src/contrib/cicero_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/cicero_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.2/cicero_1.16.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cicero_1.16.2.tgz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R dependencyCount: 181 Package: CIMICE Version: 1.6.0 Imports: dplyr, ggplot2, glue, tidyr, igraph, networkD3, visNetwork, ggcorrplot, purrr, ggraph, stats, utils, maftools, assertthat, tidygraph, expm, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, webshot License: Artistic-2.0 MD5sum: 067f4a89e8ceb27898e8926a71d931fb NeedsCompilation: no Title: CIMICE-R: (Markov) Chain Method to Inferr Cancer Evolution Description: CIMICE is a tool in the field of tumor phylogenetics and its goal is to build a Markov Chain (called Cancer Progression Markov Chain, CPMC) in order to model tumor subtypes evolution. The input of CIMICE is a Mutational Matrix, so a boolean matrix representing altered genes in a collection of samples. These samples are assumed to be obtained with single-cell DNA analysis techniques and the tool is specifically written to use the peculiarities of this data for the CMPC construction. biocViews: Software, BiologicalQuestion, NetworkInference, ResearchField, Phylogenetics, StatisticalMethod, GraphAndNetwork, Technology, SingleCell Author: Nicolò Rossi [aut, cre] (Lab. of Computational Biology and Bioinformatics, Department of Mathematics, Computer Science and Physics, University of Udine, ) Maintainer: Nicolò Rossi URL: https://github.com/redsnic/CIMICE VignetteBuilder: knitr BugReports: https://github.com/redsnic/CIMICE/issues git_url: https://git.bioconductor.org/packages/CIMICE git_branch: RELEASE_3_16 git_last_commit: c17e627 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CIMICE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CIMICE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CIMICE_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CIMICE_1.6.0.tgz vignettes: vignettes/CIMICE/inst/doc/CIMICE_SHORT.html, vignettes/CIMICE/inst/doc/CIMICER.html vignetteTitles: Quick guide, CIMICE-R: (Markov) Chain Method to Infer Cancer Evolution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CIMICE/inst/doc/CIMICE_SHORT.R, vignettes/CIMICE/inst/doc/CIMICER.R dependencyCount: 94 Package: CINdex Version: 1.26.0 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, GenomeInfoDb,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens, R.utils License: GPL (>= 2) MD5sum: b8104159cc449c9485b62a34d376505b NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Krithika Bhuvaneshwar [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yue Wang [aut, ths] (Virginia Polytechnic Institute and State University), Yuanjian Feng [aut] (Virginia Polytechnic Institute and State University), Ie-Ming Shih [aut] (Johns Hopkins University School of Medicine), Subha Madhavan [aut] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center), Yuriy Gusev [aut, cre] (Innovation Center for Biomedical Informatics, Georgetown University Medical Center) Maintainer: Yuriy Gusev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CINdex git_branch: RELEASE_3_16 git_last_commit: e6a247a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CINdex_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CINdex_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CINdex_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CINdex_1.26.0.tgz vignettes: vignettes/CINdex/inst/doc/CINdex.pdf, vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.pdf, vignettes/CINdex/inst/doc/PrepareInputData.pdf vignetteTitles: CINdex Tutorial, How to obtain Cytoband and Stain Information, Prepare input data for CINdex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CINdex/inst/doc/CINdex.R, vignettes/CINdex/inst/doc/HowToDownloadCytobandInfo.R, vignettes/CINdex/inst/doc/PrepareInputData.R dependencyCount: 44 Package: circRNAprofiler Version: 1.12.2 Depends: R(>= 4.2.0) Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb, universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, Biostrings, gwascat, BSgenome, Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocManager, License: GPL-3 MD5sum: 1b8790b72126190820a8deab7d7134c1 NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: RELEASE_3_16 git_last_commit: e499486 git_last_commit_date: 2023-01-23 Date/Publication: 2023-01-23 source.ver: src/contrib/circRNAprofiler_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/circRNAprofiler_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/circRNAprofiler_1.12.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/circRNAprofiler_1.12.2.tgz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 169 Package: CircSeqAlignTk Version: 1.0.0 Depends: R (>= 4.2) Imports: stats, tools, utils, methods, S4Vectors, rlang, magrittr, dplyr, tidyr, ggplot2, BiocGenerics, Biostrings, IRanges, ShortRead, Rsamtools, Rbowtie2, Rhisat2 Suggests: knitr, rmarkdown, testthat, R.utils, BiocStyle License: MIT + file LICENSE MD5sum: b2aaeb513c902a58a5cc4e2d243042b2 NeedsCompilation: no Title: A toolkit for end-to-end analysis of RNA-seq data for circular genomes Description: CircSeqAlignTk is designed for end-to-end RNA-Seq data analysis of circular genome sequences, from alignment to visualization. It mainly targets viroids which are composed of 246-401 nt circular RNAs. In addition, CircSeqAlignTk implements a tidy interface to generate synthetic sequencing data that mimic real RNA-Seq data, allowing developers to evaluate the performance of alignment tools and workflows. biocViews: Sequencing, SmallRNA, Alignment, Software Author: Jianqiang Sun [aut, cre] (), Xi Fu [aut], Wei Cao [aut] Maintainer: Jianqiang Sun URL: https://github.com/jsun/CircSeqAlignTk VignetteBuilder: knitr BugReports: https://github.com/jsun/CircSeqAlignTk/issues git_url: https://git.bioconductor.org/packages/CircSeqAlignTk git_branch: RELEASE_3_16 git_last_commit: 0166720 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CircSeqAlignTk_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CircSeqAlignTk_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CircSeqAlignTk_1.0.0.tgz vignettes: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.html vignetteTitles: CircSeqAlignTk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CircSeqAlignTk/inst/doc/CircSeqAlignTk.R dependencyCount: 123 Package: cisPath Version: 1.38.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: x64 MD5sum: 8f1af7ef0e763b6dab056251f14ef31a NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: RELEASE_3_16 git_last_commit: aefa566 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cisPath_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cisPath_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cisPath_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cisPath_1.38.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R dependencyCount: 2 Package: CiteFuse Version: 1.10.0 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, propr, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: 14f2a647d9266f4dfca6300610dadaf9 NeedsCompilation: no Title: CiteFuse: multi-modal analysis of CITE-seq data Description: CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses. biocViews: SingleCell, GeneExpression Author: Yingxin Lin [aut, cre], Hani Kim [aut] Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: RELEASE_3_16 git_last_commit: 627e004 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CiteFuse_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CiteFuse_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CiteFuse_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CiteFuse_1.10.0.tgz vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html vignetteTitles: CiteFuse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R suggestsMe: MuData dependencyCount: 158 Package: ClassifyR Version: 3.2.7 Depends: R (>= 4.1.0), generics, methods, S4Vectors, MultiAssayExperiment, BiocParallel, survival Imports: grid, genefilter, utils, dplyr, tidyr, rlang, ranger Suggests: limma, edgeR, car, Rmixmod, ggplot2 (>= 3.0.0), gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, robustbase, glmnet, class, randomForestSRC, MatrixModels, xgboost License: GPL-3 Archs: x64 MD5sum: 65d79214520ac63de7ab00347fc519aa NeedsCompilation: yes Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac, Ellis Patrick, Sourish Iyengar, Harry Robertson, Andy Tran, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_16 git_last_commit: 04d9a3e git_last_commit_date: 2023-02-03 Date/Publication: 2023-02-03 source.ver: src/contrib/ClassifyR_3.2.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/ClassifyR_3.2.7.zip mac.binary.ver: bin/macosx/contrib/4.2/ClassifyR_3.2.7.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ClassifyR_3.2.7.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/DevelopersGuide.html vignetteTitles: An Introduction to the ClassifyR Package, Developer's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/DevelopersGuide.R dependencyCount: 87 Package: cleanUpdTSeq Version: 1.36.0 Depends: R (>= 3.5.0), BSgenome.Drerio.UCSC.danRer7, methods Imports: BSgenome, GenomicRanges, seqinr, e1071, Biostrings, GenomeInfoDb, IRanges, utils, stringr, stats, S4Vectors Suggests: BiocStyle, rmarkdown, knitr, RUnit, BiocGenerics (>= 0.1.0) License: GPL-2 MD5sum: 8c259d3d2eddcf20bc35f4324daf51d7 NeedsCompilation: no Title: cleanUpdTSeq cleans up artifacts from polyadenylation sites from oligo(dT)-mediated 3' end RNA sequending data Description: This package implements a Naive Bayes classifier for accurately differentiating true polyadenylation sites (pA sites) from oligo(dT)-mediated 3' end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq by filtering out false polyadenylation sites, mainly due to oligo(dT)-mediated internal priming during reverse transcription. The classifer is highly accurate and outperforms other heuristic methods. biocViews: Sequencing, 3' end sequencing, polyadenylation site, internal priming Author: Sarah Sheppard, Haibo Liu, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: RELEASE_3_16 git_last_commit: 324eb5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cleanUpdTSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cleanUpdTSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cleanUpdTSeq_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cleanUpdTSeq_1.36.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R dependencyCount: 68 Package: cleaver Version: 1.36.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.36.5) License: GPL (>= 3) MD5sum: 11a24b190615830308ce0a0c5701becd NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: RELEASE_3_16 git_last_commit: b3e3fd9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cleaver_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cleaver_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cleaver_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cleaver_1.36.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: ProteoDisco, synapter suggestsMe: RforProteomics dependencyCount: 18 Package: clippda Version: 1.48.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 08da96aea84b3ee08cf84f2736f5258f NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: RELEASE_3_16 git_last_commit: 45a4976 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clippda_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clippda_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clippda_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clippda_1.48.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 46 Package: clipper Version: 1.38.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: b70d35ef76965a6e10776313731e7a12 NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: RELEASE_3_16 git_last_commit: 71cba9f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clipper_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clipper_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clipper_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clipper_1.38.0.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite, simPATHy dependencyCount: 112 Package: cliProfiler Version: 1.4.0 Depends: S4Vectors, methods, R (>= 4.1) Imports: dplyr, rtracklayer, GenomicRanges, ggplot2, BSgenome, Biostrings, utils Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 09046d98eb39f94dfd42272f5a4d77bc NeedsCompilation: no Title: A package for the CLIP data visualization Description: An easy and fast way to visualize and profile the high-throughput IP data. This package generates the meta gene profile and other profiles. These profiles could provide valuable information for understanding the IP experiment results. biocViews: Sequencing, ChIPSeq, Visualization, Epigenetics, Genetics Author: You Zhou [aut, cre] (), Kathi Zarnack [aut] () Maintainer: You Zhou URL: https://github.com/Codezy99/cliProfiler VignetteBuilder: knitr BugReports: https://github.com/Codezy99/cliProfiler/issues git_url: https://git.bioconductor.org/packages/cliProfiler git_branch: RELEASE_3_16 git_last_commit: 39988d3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cliProfiler_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cliProfiler_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cliProfiler_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cliProfiler_1.4.0.tgz vignettes: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.html vignetteTitles: cliProfiler Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliProfiler/inst/doc/cliProfilerIntroduction.R dependencyCount: 77 Package: cliqueMS Version: 1.12.2 Depends: R (>= 3.6.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, qlcMatrix, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) Archs: x64 MD5sum: 7ab9c65e054da01811bf9b9f72b96cd0 NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: RELEASE_3_16 git_last_commit: 8d802c4 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/cliqueMS_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/cliqueMS_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/cliqueMS_1.12.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cliqueMS_1.12.2.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 99 Package: Clomial Version: 1.34.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: a01dc5636a3d650aeafe3eded92250bd NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: RELEASE_3_16 git_last_commit: f87dd93 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Clomial_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Clomial_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Clomial_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Clomial_1.34.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: Clonality Version: 1.46.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 MD5sum: a0299b04405e204ec17f61c10157fc35 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: CopyNumber, Classification, aCGH, Mutations, Diagnosis, metastasis Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya git_url: https://git.bioconductor.org/packages/Clonality git_branch: RELEASE_3_16 git_last_commit: 03de234 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Clonality_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Clonality_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Clonality_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Clonality_1.46.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R dependencyCount: 5 Package: clst Version: 1.46.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 31192f1730085270dc8910075bc5a9be NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: RELEASE_3_16 git_last_commit: 9c1ce28 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clst_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clst_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clst_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clst_1.46.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 14 Package: clstutils Version: 1.46.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit License: GPL-3 MD5sum: 55e243db171f8d4286ee6edf43181e80 NeedsCompilation: no Title: Tools for performing taxonomic assignment Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: RELEASE_3_16 git_last_commit: 5626d4a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clstutils_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clstutils_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clstutils_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clstutils_1.46.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 37 Package: CluMSID Version: 1.14.0 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE MD5sum: 95c1aa358fd631ef647ff53df0af018e NeedsCompilation: no Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues git_url: https://git.bioconductor.org/packages/CluMSID git_branch: RELEASE_3_16 git_last_commit: 144ca3f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CluMSID_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CluMSID_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CluMSID_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CluMSID_1.14.0.tgz vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html, vignettes/CluMSID/inst/doc/CluMSID_tutorial.html vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial, CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R, vignettes/CluMSID/inst/doc/CluMSID_tutorial.R dependencyCount: 135 Package: clustComp Version: 1.26.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 00e715730c8305ae8755a0ccf508f411 NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: RELEASE_3_16 git_last_commit: e4c84d1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clustComp_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clustComp_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clustComp_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clustComp_1.26.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: clusterExperiment Version: 2.18.2 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, howmany, locfdr, matrixStats, graphics, parallel, BiocSingular, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma, mbkmeans LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: a40f13c9bf44c76916489853a12ab0b6 NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: RELEASE_3_16 git_last_commit: eae8335 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/clusterExperiment_2.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterExperiment_2.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterExperiment_2.18.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clusterExperiment_2.18.2.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html, vignettes/clusterExperiment/inst/doc/largeDataSets.html vignetteTitles: clusterExperiment Vignette, Working with Large Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R, vignettes/clusterExperiment/inst/doc/largeDataSets.R dependsOnMe: netSmooth suggestsMe: netDx, slingshot, tradeSeq dependencyCount: 146 Package: ClusterJudge Version: 1.20.0 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 55bf309c7ca917b967afab96e3490542 NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: RELEASE_3_16 git_last_commit: c1025ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ClusterJudge_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ClusterJudge_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ClusterJudge_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ClusterJudge_1.20.0.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 27 Package: clusterProfiler Version: 4.6.2 Depends: R (>= 3.5.0) Imports: AnnotationDbi, downloader, DOSE (>= 3.23.2), dplyr, enrichplot (>= 1.9.3), GO.db, GOSemSim, gson (>= 0.0.7), magrittr, methods, plyr, qvalue, rlang, stats, tidyr, utils, yulab.utils Suggests: AnnotationHub, knitr, rmarkdown, org.Hs.eg.db, prettydoc, ReactomePA, testthat License: Artistic-2.0 MD5sum: cec2c019b8731fa6b37e74e0d73d7015 NeedsCompilation: no Title: A universal enrichment tool for interpreting omics data Description: This package supports functional characteristics of both coding and non-coding genomics data for thousands of species with up-to-date gene annotation. It provides a univeral interface for gene functional annotation from a variety of sources and thus can be applied in diverse scenarios. It provides a tidy interface to access, manipulate, and visualize enrichment results to help users achieve efficient data interpretation. Datasets obtained from multiple treatments and time points can be analyzed and compared in a single run, easily revealing functional consensus and differences among distinct conditions. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Li-Gen Wang [ctb], Erqiang Hu [ctb], Xiao Luo [ctb], Meijun Chen [ctb], Giovanni Dall'Olio [ctb], Wanqian Wei [ctb], Chun-Hui Gao [ctb] () Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ (docs), https://doi.org/10.1016/j.xinn.2021.100141 (paper) VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: RELEASE_3_16 git_last_commit: 7d3f3cd git_last_commit_date: 2023-03-05 Date/Publication: 2023-03-05 source.ver: src/contrib/clusterProfiler_4.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterProfiler_4.6.2.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterProfiler_4.6.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clusterProfiler_4.6.2.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R importsMe: bioCancer, CEMiTool, CeTF, conclus, debrowser, EasyCellType, eegc, enrichTF, esATAC, famat, fcoex, GDCRNATools, goSorensen, IRISFGM, MAGeCKFlute, MetaPhOR, methylGSA, MicrobiomeProfiler, miRspongeR, MoonlightR, multiSight, netboxr, PanomiR, PFP, Pigengene, RNASeqR, signatureSearch, TCGAbiolinksGUI, TimiRGeN, ExpHunterSuite, recountWorkflow, DRviaSPCN, genekitr, immcp, pathwayTMB, PMAPscore, RVA suggestsMe: ChIPseeker, cola, DAPAR, DOSE, enrichplot, EpiCompare, epihet, EpiMix, GeneTonic, GenomicSuperSignature, GOSemSim, GRaNIE, GSEAmining, MesKit, paxtoolsr, ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, vsclust, org.Mxanthus.db, grandR, MARVEL, OlinkAnalyze, SCpubr dependencyCount: 128 Package: clusterSeq Version: 1.22.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: 4ab3e551009171b8fc613cc27e76f0d0 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle & Irene Papatheodorou Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: RELEASE_3_16 git_last_commit: 6989724 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clusterSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterSeq_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clusterSeq_1.22.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 35 Package: ClusterSignificance Version: 1.26.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: daa7c28296949b221850b3b867149723 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: RELEASE_3_16 git_last_commit: 07d2b89 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ClusterSignificance_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ClusterSignificance_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ClusterSignificance_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ClusterSignificance_1.26.0.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.70.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 1724beb3fe1efe32ce25c25882d24d09 NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: RELEASE_3_16 git_last_commit: 7505ddf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clusterStab_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clusterStab_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clusterStab_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clusterStab_1.70.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 6 Package: clustifyr Version: 1.10.0 Depends: R (>= 3.5.0) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, readr, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, matrixStats, S4Vectors, proxy, httr, utils Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle, BiocManager, remotes, shiny, Seurat, gprofiler2, purrr License: MIT + file LICENSE MD5sum: 65ff873bbf6384c2b28f62b5545b05a4 NeedsCompilation: no Title: Classifier for Single-cell RNA-seq Using Cell Clusters Description: Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment. biocViews: SingleCell, Annotation, Sequencing, Microarray, GeneExpression Author: Rui Fu [aut], Kent Riemondy [cre, aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb], RNA Bioscience Initiative [fnd, cph] Maintainer: Kent Riemondy URL: https://github.com/rnabioco/clustifyr, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: RELEASE_3_16 git_last_commit: 768cfde git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/clustifyr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/clustifyr_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/clustifyr_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/clustifyr_1.10.0.tgz vignettes: vignettes/clustifyr/inst/doc/clustifyr.html, vignettes/clustifyr/inst/doc/geo-annotations.html vignetteTitles: Introduction to clustifyr, geo-annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clustifyr/inst/doc/clustifyr.R, vignettes/clustifyr/inst/doc/geo-annotations.R suggestsMe: clustifyrdatahub dependencyCount: 94 Package: CMA Version: 1.56.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 2ec9d62ada714c1cdc0942d957cb8038 NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: RELEASE_3_16 git_last_commit: e1be385 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CMA_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CMA_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CMA_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CMA_1.56.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 6 Package: cmapR Version: 1.10.0 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle, rmarkdown License: file LICENSE MD5sum: 784186014a29cd30e2d0212b9509e1eb NeedsCompilation: no Title: CMap Tools in R Description: The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. biocViews: DataImport, DataRepresentation, GeneExpression Author: Ted Natoli [aut, cre] () Maintainer: Ted Natoli URL: https://github.com/cmap/cmapR VignetteBuilder: knitr BugReports: https://github.com/cmap/cmapR/issues git_url: https://git.bioconductor.org/packages/cmapR git_branch: RELEASE_3_16 git_last_commit: d7752f6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cmapR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cmapR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cmapR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cmapR_1.10.0.tgz vignettes: vignettes/cmapR/inst/doc/tutorial.html vignetteTitles: cmapR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cmapR/inst/doc/tutorial.R dependencyCount: 35 Package: cn.farms Version: 1.46.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: x64 MD5sum: bd0750a42f29c050e9b24cdd62cfd235 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: RELEASE_3_16 git_last_commit: 620aca1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cn.farms_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cn.farms_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cn.farms_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cn.farms_1.46.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 56 Package: cn.mops Version: 1.44.0 Depends: R (>= 3.5.0), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) Archs: x64 MD5sum: b6bf66a32ff29f2b9e512500bd0386f0 NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer [aut], Gundula Povysil [cre] Maintainer: Gundula Povysil URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: RELEASE_3_16 git_last_commit: 72945ca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cn.mops_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cn.mops_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cn.mops_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cn.mops_1.44.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots suggestsMe: CNVgears dependencyCount: 33 Package: CNAnorm Version: 1.44.3 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: x64 MD5sum: d4ff73bbd6cd0ac92ce65040c0636e13 NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: RELEASE_3_16 git_last_commit: 06b7d11 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/CNAnorm_1.44.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNAnorm_1.44.3.zip mac.binary.ver: bin/macosx/contrib/4.2/CNAnorm_1.44.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNAnorm_1.44.3.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.34.0 Depends: R (>= 3.5.0) Imports: Biostrings (>= 2.33.4), DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: x64 MD5sum: 81f050e673f6840a936db181b04b5566 NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: https://github.com/ge11232002/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: RELEASE_3_16 git_last_commit: 878de98 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNEr_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNEr_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNEr_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNEr_1.34.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R importsMe: TFBSTools dependencyCount: 113 Package: CNORdt Version: 1.40.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: x64 MD5sum: 3a1d5e1faf2bf1205f10a053c594ec19 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: RELEASE_3_16 git_last_commit: 86e4481 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNORdt_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORdt_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORdt_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNORdt_1.40.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 73 Package: CNORfeeder Version: 1.38.0 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: bd08b30eb1f73ae6e10a712c8e3ae940 NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: Federica Eduati [aut], Enio Gjerga [ctb], Attila Gabor [cre] Maintainer: Attila Gabor git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_16 git_last_commit: 6bee137 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNORfeeder_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORfeeder_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORfeeder_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNORfeeder_1.38.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 72 Package: CNORfuzzy Version: 1.40.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: 8f53be92ad13d10c66b8297298221d0f NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: RELEASE_3_16 git_last_commit: 35e8d51 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNORfuzzy_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORfuzzy_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORfuzzy_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNORfuzzy_1.40.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 86 Package: CNORode Version: 1.40.0 Depends: CellNOptR, genalg, knitr Enhances: MEIGOR, doParallel, foreach License: GPL-2 Archs: x64 MD5sum: d38ac5b9ea40fccdd4efb02afe3bd407 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: Logic based ordinary differential equation (ODE) add-on to CellNOptR. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Attila Gabor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_16 git_last_commit: 8eb16ff git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNORode_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNORode_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNORode_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNORode_1.40.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Training Signalling Pathway Maps to Biochemical Data with Logic-Based Ordinary Differential Equations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 73 Package: CNTools Version: 1.54.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: x64 MD5sum: 4edaefb85eee163d531eba241e05d8df NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: RELEASE_3_16 git_last_commit: c8c9d79 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNTools_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNTools_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNTools_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNTools_1.54.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 55 Package: CNVfilteR Version: 1.12.2 Depends: R (>= 4.1) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 MD5sum: c7dbeb22092db917ce036fd90e805e30 NeedsCompilation: no Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies those CNVs that can be discarded by using the single nucleotide variant (SNV) calls that are usually obtained in common NGS pipelines. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera [aut, cre] (), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues git_url: https://git.bioconductor.org/packages/CNVfilteR git_branch: RELEASE_3_16 git_last_commit: 77c9d47 git_last_commit_date: 2023-03-20 Date/Publication: 2023-03-20 source.ver: src/contrib/CNVfilteR_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVfilteR_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVfilteR_1.12.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNVfilteR_1.12.2.tgz vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html vignetteTitles: CNVfilteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R dependencyCount: 159 Package: CNVgears Version: 1.6.0 Depends: R (>= 4.1), data.table Imports: ggplot2 Suggests: VariantAnnotation, DelayedArray, knitr, biomaRt, evobiR, rmarkdown, devtools, cowplot, usethis, scales, testthat, GenomicRanges, cn.mops, R.utils License: GPL-3 MD5sum: 3810d95060270e506c56d24dde5352f0 NeedsCompilation: no Title: A Framework of Functions to Combine, Analize and Interpret CNVs Calling Results Description: This package contains a set of functions to perform several type of processing and analysis on CNVs calling pipelines/algorithms results in an integrated manner and regardless of the raw data type (SNPs array or NGS). It provides functions to combine multiple CNV calling results into a single object, filter them, compute CNVRs (CNV Regions) and inheritance patterns, detect genic load, and more. The package is best suited for studies in human family-based cohorts. biocViews: Software, WorkflowStep, Preprocessing Author: Simone Montalbano [cre, aut] Maintainer: Simone Montalbano VignetteBuilder: knitr BugReports: https://github.com/SinomeM/CNVgears/issues git_url: https://git.bioconductor.org/packages/CNVgears git_branch: RELEASE_3_16 git_last_commit: c74c9fb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNVgears_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVgears_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVgears_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNVgears_1.6.0.tgz vignettes: vignettes/CNVgears/inst/doc/CNVgears.html vignetteTitles: CNVgears package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVgears/inst/doc/CNVgears.R dependencyCount: 36 Package: cnvGSA Version: 1.42.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 3a433de501c850e9023324d740ca2fce NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: RELEASE_3_16 git_last_commit: da464c8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cnvGSA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cnvGSA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cnvGSA_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cnvGSA_1.42.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cnvGSAdata dependencyCount: 25 Package: CNViz Version: 1.6.0 Depends: R (>= 4.0), shiny (>= 1.5.0) Imports: dplyr, stats, utils, grDevices, plotly, karyoploteR, CopyNumberPlots, GenomicRanges, magrittr, DT, scales, graphics Suggests: rmarkdown, knitr License: Artistic-2.0 MD5sum: 37a75170c81c1080176a3b1a14ac2fd1 NeedsCompilation: no Title: Copy Number Visualization Description: CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data. biocViews: Visualization, CopyNumberVariation, Sequencing, DNASeq Author: Rebecca Greenblatt [aut, cre] Maintainer: Rebecca Greenblatt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNViz git_branch: RELEASE_3_16 git_last_commit: a44a348 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNViz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNViz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNViz_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNViz_1.6.0.tgz vignettes: vignettes/CNViz/inst/doc/CNViz.html vignetteTitles: CNViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNViz/inst/doc/CNViz.R dependencyCount: 167 Package: CNVMetrics Version: 1.2.0 Depends: R (>= 4.1) Imports: GenomicRanges, IRanges, S4Vectors, BiocParallel, methods, magrittr, stats, pheatmap, gridExtra, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 99eb635069617690dc0fba019f8d2211 NeedsCompilation: no Title: Copy Number Variant Metrics Description: The CNVMetrics package calculates similarity metrics to facilitate copy number variant comparison among samples and/or methods. Similarity metrics can be employed to compare CNV profiles of genetically unrelated samples as well as those with a common genetic background. Some metrics are based on the shared amplified/deleted regions while other metrics rely on the level of amplification/deletion. The data type used as input is a plain text file containing the genomic position of the copy number variations, as well as the status and/or the log2 ratio values. Finally, a visualization tool is provided to explore resulting metrics. biocViews: BiologicalQuestion, Software, CopyNumberVariation Author: Astrid Deschênes [aut, cre] (), Pascal Belleau [aut] (), David A. Tuveson [aut], Alexander Krasnitz [aut] Maintainer: Astrid Deschênes URL: https://github.com/krasnitzlab/CNVMetrics, https://krasnitzlab.github.io/CNVMetrics/ VignetteBuilder: knitr BugReports: https://github.com/krasnitzlab/CNVMetrics/issues git_url: https://git.bioconductor.org/packages/CNVMetrics git_branch: RELEASE_3_16 git_last_commit: 7188f31 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNVMetrics_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVMetrics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVMetrics_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNVMetrics_1.2.0.tgz vignettes: vignettes/CNVMetrics/inst/doc/CNVMetrics.html vignetteTitles: Copy number variant metrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVMetrics/inst/doc/CNVMetrics.R dependencyCount: 44 Package: CNVPanelizer Version: 1.30.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: d5912fbc4bcc0f1d01d4f507750b9664 NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: RELEASE_3_16 git_last_commit: e0047ba git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNVPanelizer_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVPanelizer_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVPanelizer_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNVPanelizer_1.30.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 114 Package: CNVRanger Version: 1.14.0 Depends: GenomicRanges, RaggedExperiment Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment, TCGAutils, curatedTCGAData, ensembldb, grid, knitr, regioneR, rmarkdown License: Artistic-2.0 MD5sum: 989274fbcd20ed1f98306b589487aecb NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva [aut], Marcel Ramos [ctb], Levi Waldron [ctb] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: RELEASE_3_16 git_last_commit: 59487b4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNVRanger_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVRanger_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVRanger_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNVRanger_1.14.0.tgz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 61 Package: CNVrd2 Version: 1.36.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: b111328cdda3706b344730d5e7532819 NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: RELEASE_3_16 git_last_commit: 27b3c4c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CNVrd2_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CNVrd2_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CNVrd2_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CNVrd2_1.36.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 116 Package: CoCiteStats Version: 1.70.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: bc803ee260ba9fd3f4569d029bdc6545 NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: RELEASE_3_16 git_last_commit: 78eeb05 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CoCiteStats_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoCiteStats_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoCiteStats_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CoCiteStats_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 47 Package: COCOA Version: 2.12.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: bca4b890f49be10d6a5da3ffb942575f NeedsCompilation: no Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA git_url: https://git.bioconductor.org/packages/COCOA git_branch: RELEASE_3_16 git_last_commit: 2460fac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/COCOA_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COCOA_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COCOA_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/COCOA_2.12.0.tgz vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html vignetteTitles: Introduction to Coordinate Covariation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R dependencyCount: 114 Package: codelink Version: 1.66.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 20e9a7bbe95d5dc0b776cb942bb94827 NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: RELEASE_3_16 git_last_commit: 7f0679a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/codelink_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/codelink_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/codelink_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/codelink_1.66.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R suggestsMe: MAQCsubset dependencyCount: 50 Package: CODEX Version: 1.30.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 790f5347cfb6832e984516548f4aa788 NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: RELEASE_3_16 git_last_commit: 0694f11 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CODEX_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CODEX_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CODEX_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CODEX_1.30.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 48 Package: coexnet Version: 1.19.1 Depends: R (>= 3.6) Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma, graphics, stats, utils, STRINGdb, SummarizedExperiment, minet, rmarkdown Suggests: RUnit, BiocGenerics, knitr License: LGPL MD5sum: f9215da077a9b10c836431510d44be43 NeedsCompilation: no Title: coexnet: An R package to build CO-EXpression NETworks from Microarray Data Description: Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach. biocViews: GeneExpression, Microarray, DifferentialExpression, GraphAndNetwork, NetworkInference, SystemsBiology, Normalization, Network Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut] Maintainer: Juan David Henao VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/coexnet git_branch: master git_last_commit: 091421d git_last_commit_date: 2022-05-10 Date/Publication: 2022-08-24 source.ver: src/contrib/coexnet_1.19.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/coexnet_1.19.1.zip mac.binary.ver: bin/macosx/contrib/4.2/coexnet_1.19.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/coexnet_1.20.0.tgz vignettes: vignettes/coexnet/inst/doc/coexnet.pdf vignetteTitles: The title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coexnet/inst/doc/coexnet.R dependencyCount: 132 Package: CoGAPS Version: 3.18.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5 LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 05f7b1c2afa281c6c59e38513d4baafd NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman , Jeanette Johnson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_16 git_last_commit: 659a6a2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CoGAPS_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoGAPS_3.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoGAPS_3.18.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R dependsOnMe: ATACCoGAPS importsMe: projectR dependencyCount: 46 Package: cogena Version: 1.32.0 Depends: R (>= 3.6), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, stringr, tibble, tidyr, dplyr, devtools Suggests: knitr, rmarkdown (>= 2.1) License: LGPL-3 MD5sum: 37198eecd973e419c7b2662e03e83864 NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: RELEASE_3_16 git_last_commit: 19a00f2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cogena_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cogena_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cogena_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cogena_1.32.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set enrichment analysis of co-expressed genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 148 Package: cogeqc Version: 1.2.1 Depends: R (>= 4.2.0) Imports: utils, graphics, stats, methods, reshape2, ggplot2, ggtree, patchwork, igraph, Biostrings Suggests: testthat (>= 3.0.0), sessioninfo, knitr, BiocStyle, rmarkdown, covr License: GPL-3 MD5sum: 3c18a968fdd923c8a1b254bfc43045ed NeedsCompilation: no Title: Systematic quality checks on comparative genomics analyses Description: cogeqc aims to facilitate systematic quality checks on standard comparative genomics analyses to help researchers detect issues and select the most suitable parameters for each data set. cogeqc can be used to asses: i. genome assembly quality with BUSCOs; ii. orthogroup inference using a protein domain-based approach and; iii. synteny detection using synteny network properties. There are also data visualization functions to explore QC summary statistics. biocViews: Software, GenomeAssembly, ComparativeGenomics, FunctionalGenomics, Phylogenetics, QualityControl, Network Author: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/cogeqc SystemRequirements: BUSCO (>= 5.1.3) VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/cogeqc git_url: https://git.bioconductor.org/packages/cogeqc git_branch: RELEASE_3_16 git_last_commit: b3a2e59 git_last_commit_date: 2023-01-24 Date/Publication: 2023-01-24 source.ver: src/contrib/cogeqc_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cogeqc_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cogeqc_1.2.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cogeqc_1.2.1.tgz vignettes: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.html, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.html, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.html vignetteTitles: Assessing genome assembly, Assessing orthogroup inference, Assessing synteny identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogeqc/inst/doc/vignette_01_assessing_genome_assembly.R, vignettes/cogeqc/inst/doc/vignette_02_assessing_orthogroup_inference.R, vignettes/cogeqc/inst/doc/vignette_03_assessing_synteny.R dependencyCount: 74 Package: Cogito Version: 1.4.0 Depends: R (>= 4.1), GenomicRanges, jsonlite, GenomicFeatures, entropy Imports: BiocManager, rmarkdown, GenomeInfoDb, S4Vectors, AnnotationDbi, graphics, stats, utils, methods, magrittr, ggplot2, TxDb.Mmusculus.UCSC.mm9.knownGene Suggests: BiocStyle, knitr, markdown, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 29b37b2f1a6717440ae3e1894a202705 NeedsCompilation: no Title: Compare genomic intervals tool - Automated, complete, reproducible and clear report about genomic and epigenomic data sets Description: Biological studies often consist of multiple conditions which are examined with different laboratory set ups like RNA-sequencing or ChIP-sequencing. To get an overview about the whole resulting data set, Cogito provides an automated, complete, reproducible and clear report about all samples and basic comparisons between all different samples. This report can be used as documentation about the data set or as starting point for further custom analysis. biocViews: FunctionalGenomics, GeneRegulation, Software, Sequencing Author: Annika Bürger [cre, aut] Maintainer: Annika Bürger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cogito git_branch: RELEASE_3_16 git_last_commit: 39b4f95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Cogito_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cogito_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cogito_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Cogito_1.4.0.tgz vignettes: vignettes/Cogito/inst/doc/Cogito.html vignetteTitles: Cogito: Compare annotated genomic intervals tool hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cogito/inst/doc/Cogito.R dependencyCount: 127 Package: coGPS Version: 1.42.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 920989190a041d2ba7837e1376949ed2 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: RELEASE_3_16 git_last_commit: fc91536 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/coGPS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coGPS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coGPS_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/coGPS_1.42.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: COHCAP Version: 1.44.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 Archs: x64 MD5sum: 9642e77797ebf65e2f773b4cb4e36974 NeedsCompilation: yes Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: COHCAP (pronounced "co-cap") provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k/EPIC methylation array, targeted BS-Seq, etc.). It provides differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, with visualizaton options. Discussion Group: https://sourceforge.net/p/cohcap/discussion/bioconductor/ biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden , Yate-Ching Yuan , Xiwei Wu Maintainer: Charles Warden SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/COHCAP git_branch: RELEASE_3_16 git_last_commit: c11fd3d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/COHCAP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COHCAP_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COHCAP_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/COHCAP_1.44.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R dependencyCount: 14 Package: cola Version: 2.4.0 Depends: R (>= 3.6.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats, GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr, markdown, digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr, foreach, doParallel, irlba LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree, dendextend, Polychrome, rmarkdown, simplifyEnrichment, cowplot, flexclust, randomForest, e1071 License: MIT + file LICENSE Archs: x64 MD5sum: 1e3ca280422b736d52ec750c077e08fb NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. 6. It allows applying consensus partitioning in a hierarchical manner. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: RELEASE_3_16 git_last_commit: 3a633ff git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cola_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cola_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cola_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cola_2.4.0.tgz vignettes: vignettes/cola/inst/doc/cola.html vignetteTitles: Use of cola hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: InteractiveComplexHeatmap, simplifyEnrichment dependencyCount: 61 Package: comapr Version: 1.2.0 Depends: R (>= 4.1.0) Imports: methods, ggplot2, reshape2, dplyr, gridExtra, plotly, circlize, rlang, GenomicRanges, IRanges, foreach, BiocParallel, GenomeInfoDb, scales, RColorBrewer, tidyr, S4Vectors, utils, Matrix, grid, stats, SummarizedExperiment, plyr, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), statmod License: MIT + file LICENSE MD5sum: fd5a9990bdf9a7f13f841454f42d4b6c NeedsCompilation: no Title: Crossover analysis and genetic map construction Description: comapr detects crossover intervals for single gametes from their haplotype states sequences and stores the crossovers in GRanges object. The genetic distances can then be calculated via the mapping functions using estimated crossover rates for maker intervals. Visualisation functions for plotting interval-based genetic map or cumulative genetic distances are implemented, which help reveal the variation of crossovers landscapes across the genome and across individuals. biocViews: Software, SingleCell, Visualization, Genetics Author: Ruqian Lyu [aut, cre] () Maintainer: Ruqian Lyu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/comapr git_branch: RELEASE_3_16 git_last_commit: 9c69a83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/comapr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/comapr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/comapr_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/comapr_1.2.0.tgz vignettes: vignettes/comapr/inst/doc/getStarted.html, vignettes/comapr/inst/doc/single-sperm-co-analysis.html vignetteTitles: Get-Started-With-comapr, single-sperm-co-analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/comapr/inst/doc/getStarted.R, vignettes/comapr/inst/doc/single-sperm-co-analysis.R dependencyCount: 164 Package: combi Version: 1.10.0 Depends: R (>= 4.0), DBI Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix, BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: c9e6a26bf674cb1449d5e26bb8c92793 NeedsCompilation: no Title: Compositional omics model based visual integration Description: This explorative ordination method combines quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration are available. The results are shown as interpretable, compositional multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: RELEASE_3_16 git_last_commit: fdf3235 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/combi_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/combi_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/combi_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/combi_1.10.0.tgz vignettes: vignettes/combi/inst/doc/combi.html vignetteTitles: Manual for the combi pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/combi/inst/doc/combi.R dependencyCount: 95 Package: coMET Version: 1.30.0 Depends: R (>= 4.1.0), grid, utils, biomaRt, Gviz, psych Imports: hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics, showtext License: GPL (>= 2) MD5sum: 9fcd9137384d30c5e8c5a33a2cae6348 NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as lon:g as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation, MotifAnnotation Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet [aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut] Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coMET git_branch: RELEASE_3_16 git_last_commit: c75985c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/coMET_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coMET_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coMET_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/coMET_1.30.0.tgz vignettes: vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R dependencyCount: 157 Package: coMethDMR Version: 1.2.0 Depends: R (>= 4.1) Imports: AnnotationHub, BiocParallel, bumphunter, ExperimentHub, GenomicRanges, IRanges, lmerTest, methods, stats, utils Suggests: BiocStyle, corrplot, knitr, rmarkdown, testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: GPL-3 MD5sum: ed667bdf390dc369ce89186f710ce947 NeedsCompilation: no Title: Accurate identification of co-methylated and differentially methylated regions in epigenome-wide association studies Description: coMethDMR identifies genomic regions associated with continuous phenotypes by optimally leverages covariations among CpGs within predefined genomic regions. Instead of testing all CpGs within a genomic region, coMethDMR carries out an additional step that selects co-methylated sub-regions first without using any outcome information. Next, coMethDMR tests association between methylation within the sub-region and continuous phenotype using a random coefficient mixed effects model, which models both variations between CpG sites within the region and differential methylation simultaneously. biocViews: DNAMethylation, Epigenetics, MethylationArray, DifferentialMethylation, GenomeWideAssociation Author: Fernanda Veitzman [cre], Lissette Gomez [aut], Tiago Silva [aut], Ning Lijiao [ctb], Boissel Mathilde [ctb], Lily Wang [aut], Gabriel Odom [aut] Maintainer: Fernanda Veitzman URL: https://github.com/TransBioInfoLab/coMethDMR VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/coMethDMR/issues git_url: https://git.bioconductor.org/packages/coMethDMR git_branch: RELEASE_3_16 git_last_commit: 366ee96 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/coMethDMR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coMethDMR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coMethDMR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/coMethDMR_1.2.0.tgz vignettes: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.html, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.html vignetteTitles: "Introduction to coMethDMR", "coMethDMR with Parallel Computing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coMethDMR/inst/doc/vin1_Introduction_to_coMethDMR_geneBasedPipeline.R, vignettes/coMethDMR/inst/doc/vin2_BiocParallel_for_coMethDMR_geneBasedPipeline.R dependencyCount: 167 Package: compartmap Version: 1.16.0 Depends: R (>= 4.1.0), SummarizedExperiment, RaggedExperiment, BiocSingular, HDF5Array Imports: GenomicRanges, parallel, grid, ggplot2, reshape2, scales, DelayedArray, rtracklayer, DelayedMatrixStats, Matrix, RMTstat Suggests: covr, testthat, knitr, Rcpp, rmarkdown, markdown License: GPL-3 + file LICENSE MD5sum: 18e9415d9c03c94df8268c51f1561532 NeedsCompilation: no Title: Higher-order chromatin domain inference in single cells from scRNA-seq and scATAC-seq Description: Compartmap performs direct inference of higher-order chromatin from scRNA-seq and scATAC-seq. This package implements a James-Stein estimator for computing single-cell level higher-order chromatin domains. Further, we utilize random matrix theory as a method to de-noise correlation matrices to achieve a similar "plaid-like" patterning as observed in Hi-C and scHi-C data. biocViews: Genetics, Epigenetics, ATACSeq, RNASeq, SingleCell Author: Benjamin Johnson [aut, cre], Tim Triche [aut], Hui Shen [aut], Kasper Hansen [aut], Jean-Philippe Fortin [aut] Maintainer: Benjamin Johnson URL: https://github.com/biobenkj/compartmap VignetteBuilder: knitr BugReports: https://github.com/biobenkj/compartmap/issues git_url: https://git.bioconductor.org/packages/compartmap git_branch: RELEASE_3_16 git_last_commit: 146416b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/compartmap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/compartmap_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/compartmap_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/compartmap_1.16.0.tgz vignettes: vignettes/compartmap/inst/doc/compartmap_vignette.html vignetteTitles: Higher-order chromatin inference with compartmap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/compartmap/inst/doc/compartmap_vignette.R dependencyCount: 91 Package: COMPASS Version: 1.36.2 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach, coda LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2, progress License: Artistic-2.0 Archs: x64 MD5sum: 1dc79fa323fc773a82c16858ed23a96a NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: RELEASE_3_16 git_last_commit: 920d377 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/COMPASS_1.36.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/COMPASS_1.36.2.zip mac.binary.ver: bin/macosx/contrib/4.2/COMPASS_1.36.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/COMPASS_1.36.2.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R dependencyCount: 73 Package: compcodeR Version: 1.34.0 Depends: R (>= 4.0), sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, stats, utils, ape, phylolm, matrixStats, grDevices, graphics Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), rmarkdown, phytools, phangorn, testthat, ggtree, tidytree, statmod, covr Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 082c6a4af7c4a62a7ed52f1a494aaf19 NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] (), Paul Bastide [aut] (), Mélina Gallopin [aut] (0000-0002-2431-7825 ) Maintainer: Charlotte Soneson URL: https://github.com/csoneson/compcodeR VignetteBuilder: knitr BugReports: https://github.com/csoneson/compcodeR/issues git_url: https://git.bioconductor.org/packages/compcodeR git_branch: RELEASE_3_16 git_last_commit: 0febb89 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/compcodeR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/compcodeR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/compcodeR_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/compcodeR_1.34.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html, vignettes/compcodeR/inst/doc/phylocompcodeR.html vignetteTitles: compcodeR, phylocompcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R, vignettes/compcodeR/inst/doc/phylocompcodeR.R dependencyCount: 83 Package: compEpiTools Version: 1.32.0 Depends: R (>= 3.5.0), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 9c9724694f3f5c54fe3b133a3381911f NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut, cre] Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: RELEASE_3_16 git_last_commit: a41173d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/compEpiTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/compEpiTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/compEpiTools_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/compEpiTools_1.32.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 164 Package: ComplexHeatmap Version: 2.14.0 Depends: R (>= 3.5.0), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.14), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), png, digest, IRanges, matrixStats, foreach, doParallel, codetools Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff, fastcluster, EnrichedHeatmap, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12), gridGraphics, gplots, rmarkdown, Cairo License: MIT + file LICENSE MD5sum: 3f74a76dbc2cf5f97e7e8503cd30cbec NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: RELEASE_3_16 git_last_commit: 57fcaa0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ComplexHeatmap_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ComplexHeatmap_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ComplexHeatmap_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ComplexHeatmap_2.14.0.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, InteractiveComplexHeatmap, recoup, countToFPKM importsMe: airpart, ASURAT, BiocOncoTK, BioNERO, blacksheepr, BloodGen3Module, CATALYST, celda, CeTF, COCOA, cola, COTAN, cytoKernel, DEComplexDisease, DEGreport, DEP, diffcyt, diffUTR, ELMER, fCCAC, FLAMES, gCrisprTools, GeneTonic, GenomicSuperSignature, gmoviz, GRaNIE, hermes, InterCellar, iSEE, LineagePulse, MatrixQCvis, MesKit, microbiomeMarker, MOMA, monaLisa, muscat, musicatk, MWASTools, PathoStat, PeacoQC, pipeComp, POMA, profileplyr, RLSeq, sechm, segmenter, signifinder, simplifyEnrichment, singleCellTK, sparrow, SPIAT, SPONGE, TBSignatureProfiler, Xeva, YAPSA, armada, autoGO, bulkAnalyseR, conos, IntLIM, metaGE, missoNet, MitoHEAR, MKomics, ogrdbstats, PALMO, pkgndep, rKOMICS, RNAseqQC, RVA, scITD, tidyHeatmap, visxhclust, wilson suggestsMe: artMS, bambu, BindingSiteFinder, BrainSABER, clustifyr, CNVRanger, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, msImpute, pareg, plotgardener, projectR, proteasy, QFeatures, scDblFinder, spiky, TCGAbiolinks, TCGAutils, weitrix, NanoporeRNASeq, CIARA, circlize, eclust, ggpicrust2, ggsector, grandR, i2dash, IOHanalyzer, MOSS, multipanelfigure, scCustomize, SCpubr, singleCellHaystack, spiralize dependencyCount: 28 Package: CompoundDb Version: 1.2.1 Depends: R (>= 4.1), methods, AnnotationFilter, S4Vectors Imports: BiocGenerics, ChemmineR, tibble, jsonlite, dplyr, DBI, dbplyr, RSQLite, Biobase, ProtGenerics, xml2, IRanges, Spectra (>= 1.5.17), MsCoreUtils, MetaboCoreUtils Suggests: knitr, rmarkdown, testthat, BiocStyle (>= 2.5.19), MsBackendMgf License: Artistic-2.0 MD5sum: 796b202426b8dfda5e0fece6810424ce NeedsCompilation: no Title: Creating and Using (Chemical) Compound Annotation Databases Description: CompoundDb provides functionality to create and use (chemical) compound annotation databases from a variety of different sources such as LipidMaps, HMDB, ChEBI or MassBank. The database format allows to store in addition MS/MS spectra along with compound information. The package provides also a backend for Bioconductor's Spectra package and allows thus to match experimetal MS/MS spectra against MS/MS spectra in the database. Databases can be stored in SQLite format and are thus portable. biocViews: MassSpectrometry, Metabolomics, Annotation Author: Jan Stanstrup [aut] (), Johannes Rainer [aut, cre] (), Josep M. Badia [ctb] (), Roger Gine [aut] (), Andrea Vicini [aut] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/CompoundDb VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/CompoundDb/issues git_url: https://git.bioconductor.org/packages/CompoundDb git_branch: RELEASE_3_16 git_last_commit: 119520b git_last_commit_date: 2022-11-14 Date/Publication: 2022-11-23 source.ver: src/contrib/CompoundDb_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/CompoundDb_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/CompoundDb_1.2.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CompoundDb_1.2.1.tgz vignettes: vignettes/CompoundDb/inst/doc/CompoundDb-usage.html, vignettes/CompoundDb/inst/doc/create-compounddb.html vignetteTitles: Usage of Annotation Resources with the CompoundDb Package, Creating CompoundDb annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompoundDb/inst/doc/CompoundDb-usage.R, vignettes/CompoundDb/inst/doc/create-compounddb.R importsMe: MetaboAnnotation dependencyCount: 114 Package: ComPrAn Version: 1.6.0 Imports: data.table, dplyr, forcats, ggplot2, magrittr, purrr, tidyr, rlang, stringr, shiny, DT, RColorBrewer, VennDiagram, rio, scales, shinydashboard, shinyjs, stats, tibble, grid Suggests: testthat (>= 2.1.0), knitr, rmarkdown License: MIT + file LICENSE MD5sum: 9d1c8b4d8a68c42aced257aa76bd1d17 NeedsCompilation: no Title: Complexome Profiling Analysis package Description: This package is for analysis of SILAC labeled complexome profiling data. It uses peptide table in tab-delimited format as an input and produces ready-to-use tables and plots. biocViews: MassSpectrometry, Proteomics, Visualization Author: Rick Scavetta [aut], Petra Palenikova [aut, cre] () Maintainer: Petra Palenikova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComPrAn git_branch: RELEASE_3_16 git_last_commit: b47c5c9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ComPrAn_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ComPrAn_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ComPrAn_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ComPrAn_1.6.0.tgz vignettes: vignettes/ComPrAn/inst/doc/fileFormats.html, vignettes/ComPrAn/inst/doc/proteinWorkflow.html, vignettes/ComPrAn/inst/doc/SILACcomplexomics.html vignetteTitles: fileFormats.html, Protein workflow, SILAC complexomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComPrAn/inst/doc/fileFormats.R, vignettes/ComPrAn/inst/doc/proteinWorkflow.R, vignettes/ComPrAn/inst/doc/SILACcomplexomics.R dependencyCount: 106 Package: conclus Version: 1.5.0 Depends: R (>= 4.1) Imports: org.Hs.eg.db, org.Mm.eg.db, dbscan, fpc, factoextra, Biobase, BiocFileCache, parallel, doParallel, foreach, SummarizedExperiment, biomaRt, AnnotationDbi, methods, dplyr, scran, scater, pheatmap, ggplot2, gridExtra, SingleCellExperiment, stats, utils, scales, grDevices, graphics, Rtsne, GEOquery, clusterProfiler, stringr, tools, rlang Suggests: knitr, rmarkdown, BiocStyle, S4Vectors, matrixStats, dynamicTreeCut, testthat License: GPL-3 MD5sum: c5ac0b9d2c24c49a7b8e59851273c7e2 NeedsCompilation: no Title: ScRNA-seq Workflow CONCLUS - From CONsensus CLUSters To A Meaningful CONCLUSion Description: CONCLUS is a tool for robust clustering and positive marker features selection of single-cell RNA-seq (sc-RNA-seq) datasets. It takes advantage of a consensus clustering approach that greatly simplify sc-RNA-seq data analysis for the user. Of note, CONCLUS does not cover the preprocessing steps of sequencing files obtained following next-generation sequencing. CONCLUS is organized into the following steps: Generation of multiple t-SNE plots with a range of parameters including different selection of genes extracted from PCA. Use the Density-based spatial clustering of applications with noise (DBSCAN) algorithm for idenfication of clusters in each generated t-SNE plot. All DBSCAN results are combined into a cell similarity matrix. The cell similarity matrix is used to define "CONSENSUS" clusters conserved accross the previously defined clustering solutions. Identify marker genes for each concensus cluster. biocViews: Software, Technology, SingleCell, Sequencing, Clustering, ATACSeq, Classification Author: Ilyess Rachedi [cre], Nicolas Descostes [aut], Polina Pavlovich [aut], Christophe Lancrin [aut] Maintainer: Ilyess Rachedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conclus git_branch: master git_last_commit: 354e879 git_last_commit_date: 2022-04-26 Date/Publication: 2022-08-24 source.ver: src/contrib/conclus_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/conclus_1.5.0.zip mac.binary.ver: bin/macosx/contrib/4.2/conclus_1.5.0.tgz vignettes: vignettes/conclus/inst/doc/conclus_vignette.pdf vignetteTitles: conclus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conclus/inst/doc/conclus_vignette.R dependencyCount: 270 Package: condiments Version: 1.6.0 Depends: R (>= 4.0) Imports: slingshot (>= 1.9), mgcv, RANN, stats, SingleCellExperiment, SummarizedExperiment, utils, magrittr, dplyr (>= 1.0), Ecume (>= 0.9.1), methods, pbapply, matrixStats, BiocParallel, TrajectoryUtils, igraph, distinct Suggests: knitr, testthat, rmarkdown, covr, viridis, ggplot2, RColorBrewer, randomForest, tidyr, TSCAN License: MIT + file LICENSE MD5sum: e8ecd4e9abc639ed26daa7f68b5c9833 NeedsCompilation: no Title: Differential Topology, Progression and Differentiation Description: This package encapsulate many functions to conduct a differential topology analysis. It focuses on analyzing an 'omic dataset with multiple conditions. While the package is mostly geared toward scRNASeq, it does not place any restriction on the actual input format. biocViews: RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Hector Roux de Bezieux [aut, cre] (), Koen Van den Berge [aut, ctb], Kelly Street [aut, ctb] Maintainer: Hector Roux de Bezieux URL: https://hectorrdb.github.io/condiments/index.html VignetteBuilder: knitr BugReports: https://github.com/HectorRDB/condiments/issues git_url: https://git.bioconductor.org/packages/condiments git_branch: RELEASE_3_16 git_last_commit: 39a2a18 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/condiments_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/condiments_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/condiments_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/condiments_1.6.0.tgz vignettes: vignettes/condiments/inst/doc/condiments.html, vignettes/condiments/inst/doc/controls.html, vignettes/condiments/inst/doc/examples.html vignetteTitles: The condiments workflow, Using condiments, Generating more examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/condiments/inst/doc/condiments.R, vignettes/condiments/inst/doc/controls.R, vignettes/condiments/inst/doc/examples.R dependencyCount: 174 Package: CONFESS Version: 1.26.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: c833742de6232ed777c5dda94b15ad56 NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: RELEASE_3_16 git_last_commit: d1c2548 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CONFESS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CONFESS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CONFESS_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CONFESS_1.26.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 166 Package: consensus Version: 1.16.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: ed1069b27edb3379a74014375d8ab5c9 NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: RELEASE_3_16 git_last_commit: 12e5b16 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/consensus_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensus_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensus_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/consensus_1.16.0.tgz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 12 Package: ConsensusClusterPlus Version: 1.62.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: 37640754a7b0ce9b833b696163bf896f NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: RELEASE_3_16 git_last_commit: eeab3eb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ConsensusClusterPlus_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ConsensusClusterPlus_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ConsensusClusterPlus_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ConsensusClusterPlus_1.62.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CancerSubtypes, CATALYST, ChromSCape, DEGreport, FlowSOM, PDATK, DeSousa2013, iSubGen, longmixr, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 9 Package: consensusDE Version: 1.16.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: d5a3690f399ddd87c4ab8cafa5c2d5f8 NeedsCompilation: no Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: RELEASE_3_16 git_last_commit: 4b085f5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/consensusDE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensusDE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensusDE_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/consensusDE_1.16.0.tgz vignettes: vignettes/consensusDE/inst/doc/consensusDE.html vignetteTitles: consensusDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R dependencyCount: 147 Package: consensusOV Version: 1.20.0 Depends: R (>= 3.6) Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown License: Artistic-2.0 MD5sum: cb6b145d2525dece177342dab97e584a NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen, Lavanya Kannan, Ludwig Geistlinger, Victor Kofia, Levi Waldron, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues git_url: https://git.bioconductor.org/packages/consensusOV git_branch: RELEASE_3_16 git_last_commit: ae56121 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/consensusOV_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensusOV_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensusOV_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/consensusOV_1.20.0.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R importsMe: signifinder dependencyCount: 146 Package: consensusSeekeR Version: 1.26.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 7e9954a729837824df09ab6d15ba3162 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: RELEASE_3_16 git_last_commit: a6ebb12 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/consensusSeekeR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consensusSeekeR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consensusSeekeR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/consensusSeekeR_1.26.0.tgz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes suggestsMe: EpiCompare dependencyCount: 54 Package: consICA Version: 1.0.0 Depends: R (>= 4.2.0) Imports: fastICA (>= 1.2.1), sm, org.Hs.eg.db, GO.db, stats, SummarizedExperiment, BiocParallel, graph, methods, pheatmap, survival, topGO, graphics, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: MIT + file LICENSE MD5sum: a68f9698514cac4d6f1a152bcfb3f897 NeedsCompilation: no Title: consensus Independent Component Analysis Description: consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Transcriptomics, Classification, FeatureExtraction Author: Petr V. Nazarov [aut, cre] (), Tony Kaoma [aut] (), Maryna Chepeleva [aut] () Maintainer: Petr V. Nazarov VignetteBuilder: knitr BugReports: https://github.com/biomod-lih/consICA/issues git_url: https://git.bioconductor.org/packages/consICA git_branch: RELEASE_3_16 git_last_commit: 665c73c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/consICA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/consICA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/consICA_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/consICA_1.0.0.tgz vignettes: vignettes/consICA/inst/doc/ConsICA.html vignetteTitles: The consICA package: User’s manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consICA/inst/doc/ConsICA.R dependencyCount: 81 Package: CONSTANd Version: 1.6.0 Depends: R (>= 4.1) Suggests: BiocStyle, knitr, rmarkdown, tidyr, ggplot2, gridExtra, magick, Cairo, limma License: file LICENSE MD5sum: d16d1b1eccd7c3359db20df3dc1e8cf5 NeedsCompilation: no Title: Data normalization by matrix raking Description: Normalizes a data matrix `data` by raking (using the RAS method by Bacharach, see references) the Nrows by Ncols matrix such that the row means and column means equal 1. The result is a normalized data matrix `K=RAS`, a product of row mulipliers `R` and column multipliers `S` with the original matrix `A`. Missing information needs to be presented as `NA` values and not as zero values, because CONSTANd is able to ignore missing values when calculating the mean. Using CONSTANd normalization allows for the direct comparison of values between samples within the same and even across different CONSTANd-normalized data matrices. biocViews: MassSpectrometry, Cheminformatics, Normalization, Preprocessing, DifferentialExpression, Genetics, Transcriptomics, Proteomics Author: Joris Van Houtven [aut, trl], Geert Jan Bex [trl], Dirk Valkenborg [aut, cre] Maintainer: Dirk Valkenborg URL: qcquan.net/constand VignetteBuilder: knitr BugReports: https://github.com/PDiracDelta/CONSTANd/issues git_url: https://git.bioconductor.org/packages/CONSTANd git_branch: RELEASE_3_16 git_last_commit: 0f977e3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CONSTANd_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CONSTANd_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CONSTANd_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CONSTANd_1.6.0.tgz vignettes: vignettes/CONSTANd/inst/doc/CONSTANd.html vignetteTitles: CONSTANd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CONSTANd/inst/doc/CONSTANd.R dependencyCount: 0 Package: contiBAIT Version: 1.26.0 Depends: R (>= 3.5.0), BH (>= 1.51.0.3), Rsamtools (>= 1.21) Imports: data.table, grDevices, clue, cluster, gplots, BiocGenerics (>= 0.31.6), S4Vectors, IRanges, GenomicRanges, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, GenomicAlignments, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: 114e2fefb0e81e5d9e88c27efb6eb0ff NeedsCompilation: yes Title: Improves Early Build Genome Assemblies using Strand-Seq Data Description: Using strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/contiBAIT git_branch: RELEASE_3_16 git_last_commit: e8a94bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/contiBAIT_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/contiBAIT_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/contiBAIT_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/contiBAIT_1.26.0.tgz vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf vignetteTitles: flowBi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R dependencyCount: 130 Package: conumee Version: 1.32.0 Depends: R (>= 3.5.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: a7b423e994e2b0438500c0a6d0a4b487 NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: RELEASE_3_16 git_last_commit: edde24c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/conumee_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/conumee_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/conumee_1.32.0.tgz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R dependencyCount: 145 Package: convert Version: 1.74.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 81ae2cee0a80dda4f5cdf080b9f13b81 NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: RELEASE_3_16 git_last_commit: ef81002 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/convert_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/convert_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/convert_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/convert_1.74.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maigesPack, TurboNorm suggestsMe: dyebias, OLIN, dyebiasexamples dependencyCount: 9 Package: copa Version: 1.66.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: x64 MD5sum: 83b2f1f4b5dacf38c4ed90598acfaa4c NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: RELEASE_3_16 git_last_commit: 0e23335 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/copa_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/copa_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/copa_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/copa_1.66.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 6 Package: copynumber Version: 1.38.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 1b7a578d6f0ae8a5c59c65e7d65ea0cb NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen git_url: https://git.bioconductor.org/packages/copynumber git_branch: RELEASE_3_16 git_last_commit: 8ef3095 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/copynumber_1.38.0.tar.gz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R suggestsMe: PureCN dependencyCount: 16 Package: CopyNumberPlots Version: 1.14.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, rmarkdown, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 MD5sum: 580fd73da28dba64e9253aae671b78bb NeedsCompilation: no Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues git_url: https://git.bioconductor.org/packages/CopyNumberPlots git_branch: RELEASE_3_16 git_last_commit: aa4e71a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CopyNumberPlots_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CopyNumberPlots_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CopyNumberPlots_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CopyNumberPlots_1.14.0.tgz vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html vignetteTitles: CopyNumberPlots vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R importsMe: CNVfilteR, CNViz dependencyCount: 156 Package: CopywriteR Version: 2.29.0 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: fe013866bd29c06974f5e7ac3b3beae3 NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: ImmunoOncology, TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Oscar Krijgsman URL: https://github.com/PeeperLab/CopywriteR git_url: https://git.bioconductor.org/packages/CopywriteR git_branch: master git_last_commit: 8fb3df5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-28 source.ver: src/contrib/CopywriteR_2.29.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CopywriteR_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CopywriteR_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CopywriteR_2.30.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R dependencyCount: 56 Package: coRdon Version: 1.16.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: cf6eef6d839305dc61687acb436fe61e NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: RELEASE_3_16 git_last_commit: dfc0b04 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/coRdon_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coRdon_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coRdon_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/coRdon_1.16.0.tgz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R importsMe: vhcub dependencyCount: 56 Package: CoRegNet Version: 1.36.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 0a37dda69c3814d6b53db91d90eab7e9 NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegNet git_branch: RELEASE_3_16 git_last_commit: c610bf5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CoRegNet_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoRegNet_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoRegNet_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CoRegNet_1.36.0.tgz vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html vignetteTitles: Custom Print Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R dependencyCount: 45 Package: CoreGx Version: 2.2.0 Depends: R (>= 4.1), BiocGenerics, SummarizedExperiment Imports: Biobase, S4Vectors, MultiAssayExperiment, MatrixGenerics, piano, BiocParallel, parallel, BumpyMatrix, checkmate, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon, glue, rlang, bench Suggests: pander, markdown, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL (>= 3) MD5sum: ce8eedafc085e5999f8e2471dfe9021f NeedsCompilation: no Title: Classes and Functions to Serve as the Basis for Other 'Gx' Packages Description: A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . biocViews: Software, Pharmacogenomics, Classification, Survival Author: Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: RELEASE_3_16 git_last_commit: 681e43d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CoreGx_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CoreGx_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CoreGx_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CoreGx_2.2.0.tgz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.html vignetteTitles: CoreGx: Class and Function Abstractions, The TreatmentResponseExperiment Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/TreatmentResponseExperiment.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx importsMe: PDATK dependencyCount: 132 Package: Cormotif Version: 1.44.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: f1c775ad7d0d21b09cf313cd4a69aeb0 NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: RELEASE_3_16 git_last_commit: 8338ed7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Cormotif_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Cormotif_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Cormotif_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Cormotif_1.44.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 13 Package: corral Version: 1.8.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, reshape2, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, rmarkdown, scater, testthat License: GPL-2 MD5sum: ba1f3542ef1b8d0440a93425d1c6fb12 NeedsCompilation: no Title: Correspondence Analysis for Single Cell Data Description: Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes additional options, including variations of CA to address overdispersion in count data, as well as the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA. biocViews: BatchEffect, DimensionReduction, GeneExpression, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization Author: Lauren Hsu [aut, cre] (), Aedin Culhane [aut] () Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: RELEASE_3_16 git_last_commit: b74b37a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/corral_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/corral_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/corral_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/corral_1.8.0.tgz vignettes: vignettes/corral/inst/doc/corral_dimred.html, vignettes/corral/inst/doc/corralm_alignment.html vignetteTitles: dim reduction with corral, alignment with corralm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependsOnMe: OSCA.advanced dependencyCount: 76 Package: CORREP Version: 1.64.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 35b2852aff08faa07ffdfc251f57f1bd NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu git_url: https://git.bioconductor.org/packages/CORREP git_branch: RELEASE_3_16 git_last_commit: d9029a6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CORREP_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CORREP_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CORREP_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CORREP_1.64.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R dependencyCount: 9 Package: coseq Version: 1.22.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2, scales, HTSFilter, corrplot, HTSCluster, grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: 981e3921b8c8b0ef7464a76b08260aea NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau [cre, aut] (), Cathy Maugis-Rabusseau [ctb], Antoine Godichon-Baggioni [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: RELEASE_3_16 git_last_commit: a1bc50f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/coseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/coseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/coseq_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/coseq_1.22.0.tgz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependencyCount: 109 Package: cosmiq Version: 1.32.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: a125419fb5edd79a3cb29f247a5b8f82 NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html git_url: https://git.bioconductor.org/packages/cosmiq git_branch: RELEASE_3_16 git_last_commit: 6cae4e8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cosmiq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cosmiq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cosmiq_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cosmiq_1.32.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 95 Package: cosmosR Version: 1.6.0 Depends: R (>= 4.1) Imports: CARNIVAL, dorothea, dplyr, GSEABase, igraph, progress, purrr, rlang, stringr, utils, visNetwork Suggests: testthat, knitr, rmarkdown, piano, ggplot2 License: GPL-3 MD5sum: 89beb9a3a8e0d0bd9c7bf6866e3854e1 NeedsCompilation: no Title: COSMOS (Causal Oriented Search of Multi-Omic Space) Description: COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets. biocViews: CellBiology, Pathways, Network, Proteomics, Metabolomics, Transcriptomics, GeneSignaling Author: Aurélien Dugourd [aut] (), Attila Gabor [cre] (), Katharina Zirngibl [aut] () Maintainer: Attila Gabor URL: https://github.com/saezlab/COSMOSR VignetteBuilder: knitr BugReports: https://github.com/saezlab/COSMOSR/issues git_url: https://git.bioconductor.org/packages/cosmosR git_branch: RELEASE_3_16 git_last_commit: b98af0c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cosmosR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cosmosR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cosmosR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cosmosR_1.6.0.tgz vignettes: vignettes/cosmosR/inst/doc/tutorial.html vignetteTitles: cosmosR tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cosmosR/inst/doc/tutorial.R dependencyCount: 130 Package: COSNet Version: 1.32.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 02935b7d1b0af43a1d320f62abda00af NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: RELEASE_3_16 git_last_commit: bee56a2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/COSNet_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COSNet_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COSNet_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/COSNet_1.32.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: COTAN Version: 1.2.0 Depends: R (>= 4.1) Imports: dplyr, methods, grDevices, Matrix, ggplot2, ggrepel, stats, parallel, tibble, tidyr, irlba, ComplexHeatmap, circlize, grid, scales, utils, rlang, Rfast Suggests: testthat (>= 3.0.0), spelling, knitr, data.table, R.utils, tidyverse, rmarkdown, htmlwidgets, MASS, factoextra, Rtsne, plotly, dendextend, BiocStyle, cowplot License: GPL-3 MD5sum: ee4bcf7168bf9c2660df2a703c5417bf NeedsCompilation: no Title: COexpression Tables ANalysis Description: Statistical and computational method to analyze the co-expression of gene pairs at single cell level. It provides the foundation for single-cell gene interactome analysis. The basic idea is studying the zero UMI counts' distribution instead of focusing on positive counts; this is done with a generalized contingency tables framework. COTAN can effectively assess the correlated or anti-correlated expression of gene pairs. It provides a numerical index related to the correlation and an approximate p-value for the associated independence test. COTAN can also evaluate whether single genes are differentially expressed, scoring them with a newly defined global differentiation index. Moreover, this approach provides ways to plot and cluster genes according to their co-expression pattern with other genes, effectively helping the study of gene interactions and becoming a new tool to identify cell-identity marker genes. biocViews: SystemsBiology, Transcriptomics, GeneExpression, SingleCell Author: Galfrè Silvia Giulia [aut, cre] (), Morandin Francesco [aut] (), Pietrosanto Marco [aut] (), Cremisi Federico [aut] (), Helmer-Citterich Manuela [aut] () Maintainer: Galfrè Silvia Giulia URL: https://github.com/seriph78/COTAN VignetteBuilder: knitr BugReports: https://github.com/seriph78/COTAN/issues git_url: https://git.bioconductor.org/packages/COTAN git_branch: RELEASE_3_16 git_last_commit: 5ed73ec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/COTAN_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/COTAN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/COTAN_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/COTAN_1.2.0.tgz vignettes: vignettes/COTAN/inst/doc/Guided_tutorial.html vignetteTitles: Guided tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COTAN/inst/doc/Guided_tutorial.R dependencyCount: 72 Package: countsimQC Version: 1.16.1 Depends: R (>= 3.5) Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods, ragg Suggests: knitr, testthat License: GPL (>=2) MD5sum: d87b41edc8ee829fb2afb773cdbac8e4 NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/countsimQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/countsimQC/issues git_url: https://git.bioconductor.org/packages/countsimQC git_branch: RELEASE_3_16 git_last_commit: 9278fe5 git_last_commit_date: 2022-12-16 Date/Publication: 2022-12-16 source.ver: src/contrib/countsimQC_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/countsimQC_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/countsimQC_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/countsimQC_1.16.1.tgz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R suggestsMe: muscat dependencyCount: 130 Package: covEB Version: 1.24.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: 47716944fd4baeb049a3a39989a78d91 NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: RELEASE_3_16 git_last_commit: 3a41803 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/covEB_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/covEB_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/covEB_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/covEB_1.24.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 19 Package: CoverageView Version: 1.36.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 50f935d5a62ee30ca9f66d7e932a236d NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: RELEASE_3_16 git_last_commit: 76c28ab git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CoverageView_1.36.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/CoverageView_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CoverageView_1.36.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 46 Package: covRNA Version: 1.24.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 9c4597b6990b7979f62c0f8f165a1db9 NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: RELEASE_3_16 git_last_commit: 20a6848 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/covRNA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/covRNA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/covRNA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/covRNA_1.24.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 62 Package: cpvSNP Version: 1.30.0 Depends: R (>= 3.5.0), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 65344f26be01a2ca4c35bf91e70bc02b NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: RELEASE_3_16 git_last_commit: 76b163d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cpvSNP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cpvSNP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cpvSNP_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cpvSNP_1.30.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 117 Package: cqn Version: 1.44.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: a7756a2cb20d4ff524581a71d9cbd2dd NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: RELEASE_3_16 git_last_commit: 88555e7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cqn_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cqn_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cqn_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cqn_1.44.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R dependsOnMe: KnowSeq importsMe: tweeDEseq dependencyCount: 17 Package: CRImage Version: 1.46.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: ac2eeb59822675b03e90b3bd23b98ad8 NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: RELEASE_3_16 git_last_commit: 1d1da64 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CRImage_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CRImage_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CRImage_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CRImage_1.46.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 67 Package: crisprBase Version: 1.2.0 Depends: utils, methods, R (>= 4.1) Imports: BiocGenerics, Biostrings, GenomicRanges, graphics, IRanges, S4Vectors, stringr Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 25b007b883909ea8f067909054993e38 NeedsCompilation: no Title: Base functions and classes for CRISPR gRNA design Description: Provides S4 classes for general nucleases, CRISPR nucleases, CRISPR nickases, and base editors.Several CRISPR-specific genome arithmetic functions are implemented to help extract genomic coordinates of spacer and protospacer sequences. Commonly-used CRISPR nuclease objects are provided that can be readily used in other packages. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBase VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBase/issues git_url: https://git.bioconductor.org/packages/crisprBase git_branch: RELEASE_3_16 git_last_commit: 0a9c86a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprBase_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprBase_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprBase_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprBase_1.2.0.tgz vignettes: vignettes/crisprBase/inst/doc/crisprBase.html vignetteTitles: Introduction to crisprBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBase/inst/doc/crisprBase.R dependsOnMe: crisprDesign, crisprViz importsMe: crisprBowtie, crisprBwa, crisprVerse dependencyCount: 27 Package: crisprBowtie Version: 1.2.0 Depends: methods Imports: BiocGenerics, Biostrings, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb, GenomicRanges, IRanges, Rbowtie, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: f625ba1a4390ea7dfb3086653d558f44 NeedsCompilation: no Title: Bowtie-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bowtie. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Both DNA- and RNA-targeting nucleases are supported. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBowtie VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBowtie/issues git_url: https://git.bioconductor.org/packages/crisprBowtie git_branch: RELEASE_3_16 git_last_commit: 9f9868b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprBowtie_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprBowtie_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprBowtie_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprBowtie_1.2.0.tgz vignettes: vignettes/crisprBowtie/inst/doc/crisprBowtie.html vignetteTitles: Introduction to crisprBowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBowtie/inst/doc/crisprBowtie.R importsMe: crisprDesign, crisprVerse dependencyCount: 74 Package: crisprBwa Version: 1.2.0 Depends: methods Imports: BiocGenerics, BSgenome, crisprBase (>= 0.99.15), GenomeInfoDb, Rbwa, readr, stats, stringr, utils Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, testthat License: MIT + file LICENSE OS_type: unix MD5sum: 6cd9bd5cf9975bd37ef5bfae438ab91b NeedsCompilation: no Title: BWA-based alignment of CRISPR gRNA spacer sequences Description: Provides a user-friendly interface to map on-targets and off-targets of CRISPR gRNA spacer sequences using bwa. The alignment is fast, and can be performed using either commonly-used or custom CRISPR nucleases. The alignment can work with any reference or custom genomes. Currently not supported on Windows machines. biocViews: CRISPR, FunctionalGenomics, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprBwa VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprBwa/issues git_url: https://git.bioconductor.org/packages/crisprBwa git_branch: RELEASE_3_16 git_last_commit: b5b6442 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprBwa_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/crisprBwa_1.2.0.tgz vignettes: vignettes/crisprBwa/inst/doc/crisprBwa.html vignetteTitles: Introduction to crisprBwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprBwa/inst/doc/crisprBwa.R suggestsMe: crisprDesign dependencyCount: 74 Package: crisprDesign Version: 1.0.0 Depends: R (>= 4.2.0), crisprBase (>= 1.1.3) Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, crisprBowtie (>= 0.99.8), crisprScore (>= 1.1.6), GenomeInfoDb, GenomicFeatures, GenomicRanges (>= 1.38.0), IRanges, Matrix, MatrixGenerics, methods, rtracklayer, S4Vectors, stats, utils, VariantAnnotation Suggests: biomaRt, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocStyle, crisprBwa (>= 0.99.7), knitr, rmarkdown, Rbowtie, Rbwa, RCurl, testthat License: MIT + file LICENSE MD5sum: 21ee312a0ac3825e1eca0d386f32c991 NeedsCompilation: no Title: Comprehensive design of CRISPR gRNAs for nucleases and base editors Description: Provides a comprehensive suite of functions to design and annotate CRISPR guide RNA (gRNAs) sequences. This includes on- and off-target search, on-target efficiency scoring, off-target scoring, full gene and TSS contextual annotations, and SNP annotation (human only). It currently support five types of CRISPR modalities (modes of perturbations): CRISPR knockout, CRISPR activation, CRISPR inhibition, CRISPR base editing, and CRISPR knockdown. All types of CRISPR nucleases are supported, including DNA- and RNA-target nucleases such as Cas9, Cas12a, and Cas13d. All types of base editors are also supported. gRNA design can be performed on reference genomes, transcriptomes, and custom DNA and RNA sequences. Both unpaired and paired gRNA designs are enabled. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprDesign VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprDesign/issues git_url: https://git.bioconductor.org/packages/crisprDesign git_branch: RELEASE_3_16 git_last_commit: cd58577 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprDesign_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprDesign_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprDesign_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprDesign_1.0.0.tgz vignettes: vignettes/crisprDesign/inst/doc/intro.html vignetteTitles: Introduction to crisprDesign hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprDesign/inst/doc/intro.R dependsOnMe: crisprViz importsMe: crisprVerse dependencyCount: 146 Package: crisprScore Version: 1.2.0 Depends: R (>= 4.1), crisprScoreData (>= 1.1.3) Imports: basilisk (>= 1.9.2), basilisk.utils (>= 1.9.1), BiocGenerics, Biostrings, IRanges, methods, randomForest, reticulate, stringr, utils, XVector Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e0f272b62ca271ee8f51193beb2b057f NeedsCompilation: no Title: On-Target and Off-Target Scoring Algorithms for CRISPR gRNAs Description: Provides R wrappers of several on-target and off-target scoring methods for CRISPR guide RNAs (gRNAs). The following nucleases are supported: SpCas9, AsCas12a, enAsCas12a, and RfxCas13d (CasRx). The available on-target cutting efficiency scoring methods are RuleSet1, Azimuth, DeepHF, DeepCpf1, enPAM+GB, and CRISPRscan. Both the CFD and MIT scoring methods are available for off-target specificity prediction. The package also provides a Lindel-derived score to predict the probability of a gRNA to produce indels inducing a frameshift for the Cas9 nuclease. Note that DeepHF, DeepCpf1 and enPAM+GB are not available on Windows machines. biocViews: CRISPR, FunctionalGenomics, FunctionalPrediction Author: Jean-Philippe Fortin [aut, cre, cph], Aaron Lun [aut], Luke Hoberecht [ctb], Pirunthan Perampalam [ctb] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprScore/issues VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprScore git_url: https://git.bioconductor.org/packages/crisprScore git_branch: RELEASE_3_16 git_last_commit: eae9018 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprScore_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprScore_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprScore_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprScore_1.2.0.tgz vignettes: vignettes/crisprScore/inst/doc/crisprScore.html vignetteTitles: crisprScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprScore/inst/doc/crisprScore.R importsMe: crisprDesign, crisprVerse dependencyCount: 109 Package: CRISPRseek Version: 1.38.0 Depends: R (>= 3.5.0), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, hash, methods,reticulate,rhdf5,XVector, DelayedArray, GenomeInfoDb, GenomicRanges, dplyr, keras, mltools Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, lattice, MASS, tensorflow, testthat License: GPL (>= 2) MD5sum: 7861d3f38219224811149b0d2016f3ad NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for the CRISPR editing system including Base Editors and the Prime Editor for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. The package also output indels and their frequencies for Cas9 targeted sites. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu, Paul Scemama, Benjamin R. Holmes, Hervé Pagès, Kai Hu, Hui Mao, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: RELEASE_3_16 git_last_commit: 9f03208 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CRISPRseek_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CRISPRseek_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CRISPRseek_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CRISPRseek_1.38.0.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R dependsOnMe: crisprseekplus importsMe: GUIDEseq, multicrispr dependencyCount: 97 Package: crisprseekplus Version: 1.24.0 Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures, BiocManager, BSgenome, AnnotationDbi, hash Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 5bf5e38c23e46788f2f63f404e24631b NeedsCompilation: no Title: crisprseekplus Description: Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis. biocViews: GeneRegulation, SequenceMatching, Software Author: Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/crisprseekplus VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new git_url: https://git.bioconductor.org/packages/crisprseekplus git_branch: RELEASE_3_16 git_last_commit: 95b4b22 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprseekplus_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprseekplus_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprseekplus_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprseekplus_1.24.0.tgz vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R dependencyCount: 194 Package: CrispRVariants Version: 1.26.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, gdata, GenomicFeatures, knitr, rmarkdown, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: 8935a490224fa7a8832884804c1429db NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: RELEASE_3_16 git_last_commit: f5240e3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CrispRVariants_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CrispRVariants_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CrispRVariants_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CrispRVariants_1.26.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 92 Package: crisprVerse Version: 1.0.0 Depends: R (>= 4.2.0) Imports: BiocManager, cli, crisprBase, crisprBowtie, crisprScore, crisprDesign, crisprViz, rlang, tools, utils Suggests: BiocStyle, knitr, testthat License: MIT + file LICENSE MD5sum: ae05e9e2ea7b5e4ba7a0b8a625ff9ebc NeedsCompilation: no Title: Easily install and load the crisprVerse ecosystem for CRISPR gRNA design Description: The crisprVerse is a modular ecosystem of R packages developed for the design and manipulation of CRISPR guide RNAs (gRNAs). All packages share a common language and design principles. This package is designed to make it easy to install and load the crisprVerse packages in a single step. To learn more about the crisprVerse, visit . biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprVerse VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprVerse/issues git_url: https://git.bioconductor.org/packages/crisprVerse git_branch: RELEASE_3_16 git_last_commit: 25edce4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprVerse_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprVerse_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprVerse_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprVerse_1.0.0.tgz vignettes: vignettes/crisprVerse/inst/doc/crisprVerse.html vignetteTitles: crisprVerse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprVerse/inst/doc/crisprVerse.R dependencyCount: 186 Package: crisprViz Version: 1.0.0 Depends: R (>= 4.2.0), crisprBase (>= 0.99.15), crisprDesign (>= 0.99.77) Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, grDevices, Gviz, IRanges, methods, S4Vectors Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, rtracklayer, testthat, utils License: MIT + file LICENSE MD5sum: d1413128c05af09b0b5651cfbf63bfc2 NeedsCompilation: no Title: Visualization Functions for CRISPR gRNAs Description: Provides functionalities to visualize and contextualize CRISPR guide RNAs (gRNAs) on genomic tracks across nucleases and applications. Works in conjunction with the crisprBase and crisprDesign Bioconductor packages. Plots are produced using the Gviz framework. biocViews: CRISPR, FunctionalGenomics, GeneTarget Author: Jean-Philippe Fortin [aut, cre], Luke Hoberecht [aut] Maintainer: Jean-Philippe Fortin URL: https://github.com/crisprVerse/crisprViz VignetteBuilder: knitr BugReports: https://github.com/crisprVerse/crisprViz/issues git_url: https://git.bioconductor.org/packages/crisprViz git_branch: RELEASE_3_16 git_last_commit: 8295fb6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crisprViz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crisprViz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crisprViz_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crisprViz_1.0.0.tgz vignettes: vignettes/crisprViz/inst/doc/intro.html vignetteTitles: Introduction to crisprViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprViz/inst/doc/intro.R importsMe: crisprVerse dependencyCount: 185 Package: crlmm Version: 1.56.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), snpStats, RUnit License: Artistic-2.0 Archs: x64 MD5sum: a0290f707a9629b463eb4e72e2432891 NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: RELEASE_3_16 git_last_commit: 90f1862 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crlmm_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crlmm_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crlmm_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crlmm_1.56.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R dependsOnMe: MAGAR importsMe: VanillaICE suggestsMe: oligoClasses, hapmap370k dependencyCount: 65 Package: crossmeta Version: 1.24.0 Depends: R (>= 4.0) Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>= 1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0), BiocManager (>= 1.30.4), DT (>= 0.2), DBI (>= 1.0.0), data.table (>= 1.10.4), edgeR, fdrtool (>= 1.2.15), GEOquery (>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0), metaMA (>= 3.1.2), miniUI (>= 0.1.1), methods, oligo (>= 1.38.0), reader(>= 1.0.6), RCurl (>= 1.95.4.11), RSQLite (>= 2.1.1), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0), shinyjs (>= 2.0.0), shinyBS (>= 0.61), shinyWidgets (>= 0.5.3), shinypanel (>= 0.1.0), tibble, XML (>= 3.98.1.17), readxl (>= 1.3.1) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat License: MIT + file LICENSE MD5sum: c1f51276768ffd0643fda7879d1f6951 NeedsCompilation: no Title: Cross Platform Meta-Analysis of Microarray Data Description: Implements cross-platform and cross-species meta-analyses of Affymentrix, Illumina, and Agilent microarray data. This package automates common tasks such as downloading, normalizing, and annotating raw GEO data. The user then selects control and treatment samples in order to perform differential expression analyses for all comparisons. After analysing each contrast seperately, the user can select tissue sources for each contrast and specify any tissue sources that should be grouped for the subsequent meta-analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing, GUI Author: Alex Pickering Maintainer: Alex Pickering URL: https://github.com/alexvpickering/crossmeta SystemRequirements: libxml2: libxml2-dev (deb), libxml2-devel (rpm) libcurl: libcurl4-openssl-dev (deb), libcurl-devel (rpm) openssl: libssl-dev (deb), openssl-devel (rpm), libssl_dev (csw), openssl@1.1 (brew) VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/crossmeta/issues git_url: https://git.bioconductor.org/packages/crossmeta git_branch: RELEASE_3_16 git_last_commit: 6c5c2d8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/crossmeta_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/crossmeta_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/crossmeta_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/crossmeta_1.24.0.tgz vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html vignetteTitles: crossmeta vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R suggestsMe: ccmap dependencyCount: 153 Package: CSAR Version: 1.50.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: x64 MD5sum: 8787c20f9005babb77c30ad68bca7366 NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: RELEASE_3_16 git_last_commit: 3286fe6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CSAR_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CSAR_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CSAR_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CSAR_1.50.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R dependencyCount: 16 Package: csaw Version: 1.32.0 Depends: R (>= 3.5.0), GenomicRanges, SummarizedExperiment Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, methods, S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, metapod, utils LinkingTo: Rhtslib, zlibbioc, Rcpp Suggests: AnnotationDbi, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicFeatures, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 Archs: x64 MD5sum: 53bd00812612d98207c271671a86df28 NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: RELEASE_3_16 git_last_commit: 7ab51d6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/csaw_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/csaw_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/csaw_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/csaw_1.32.0.tgz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R dependsOnMe: csawBook importsMe: diffHic, epigraHMM, extraChIPs, icetea, NADfinder, vulcan suggestsMe: GRaNIE, chipseqDB dependencyCount: 44 Package: csdR Version: 1.4.0 Depends: R (>= 4.1.0) Imports: WGCNA, glue, RhpcBLASctl, matrixStats, Rcpp LinkingTo: Rcpp Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle, magrittr, igraph, dplyr License: GPL-3 Archs: x64 MD5sum: 1d5d1e1e079747f9abd387eea3269c37 NeedsCompilation: yes Title: Differential gene co-expression Description: This package contains functionality to run differential gene co-expression across two different conditions. The algorithm is inspired by Voigt et al. 2017 and finds Conserved, Specific and Differentiated genes (hence the name CSD). This package include efficient and variance calculation by bootstrapping and Welford's algorithm. biocViews: DifferentialExpression, GraphAndNetwork, GeneExpression, Network Author: Jakob Peder Pettersen [aut, cre] () Maintainer: Jakob Peder Pettersen URL: https://almaaslab.github.io/csdR, https://github.com/AlmaasLab/csdR VignetteBuilder: knitr BugReports: https://github.com/AlmaasLab/csdR/issues git_url: https://git.bioconductor.org/packages/csdR git_branch: RELEASE_3_16 git_last_commit: 4e22cbd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/csdR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/csdR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/csdR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/csdR_1.4.0.tgz vignettes: vignettes/csdR/inst/doc/csdR.html vignetteTitles: csdR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csdR/inst/doc/csdR.R dependencyCount: 117 Package: CSSP Version: 1.36.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: x64 MD5sum: 95dc8c5365a6fdccc307e6c6439f2c01 NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo git_url: https://git.bioconductor.org/packages/CSSP git_branch: RELEASE_3_16 git_last_commit: 2468a42 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CSSP_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CSSP_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CSSP_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CSSP_1.36.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R dependencyCount: 4 Package: CSSQ Version: 1.10.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, markdown License: Artistic-2.0 MD5sum: 53db5989eb6ac0ffb3ff83af4bc62ef8 NeedsCompilation: no Title: Chip-seq Signal Quantifier Pipeline Description: This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset. biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization Author: Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut], Yuhong Fan [aut] Maintainer: Fan Lab at Georgia Institute of Technology VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: RELEASE_3_16 git_last_commit: cd559b3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CSSQ_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CSSQ_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CSSQ_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CSSQ_1.10.0.tgz vignettes: vignettes/CSSQ/inst/doc/CSSQ.html vignetteTitles: Introduction to CSSQ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R dependencyCount: 110 Package: ctc Version: 1.72.0 Depends: amap License: GPL-2 MD5sum: c0c8b37aef2d4ac25d88f2f85fad7bd1 NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: RELEASE_3_16 git_last_commit: 0a4b464 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ctc_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ctc_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ctc_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ctc_1.72.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: CTDquerier Version: 2.6.0 Depends: R (>= 4.1) Imports: RCurl, stringr, S4Vectors, stringdist, ggplot2, igraph, utils, grid, gridExtra, methods, stats, BiocFileCache Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ba577b0b47524fe9337fd4e3efd67553 NeedsCompilation: no Title: Package for CTDbase data query, visualization and downstream analysis Description: Package to retrieve and visualize data from the Comparative Toxicogenomics Database (http://ctdbase.org/). The downloaded data is formated as DataFrames for further downstream analyses. biocViews: Software, BiomedicalInformatics, Infrastructure, DataImport, DataRepresentation, GeneSetEnrichment, NetworkEnrichment, Pathways, Network, GO, KEGG Author: Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [cre] Maintainer: Xavier Escribà-Montagut VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/CTDquerier git_branch: RELEASE_3_16 git_last_commit: 97cf629 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CTDquerier_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CTDquerier_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CTDquerier_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CTDquerier_2.6.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 73 Package: cTRAP Version: 1.16.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, binr, cowplot, data.table, dplyr, DT, fastmatch, fgsea, ggplot2, ggrepel, graphics, highcharter, htmltools, httr, limma, methods, parallel, pbapply, purrr, qs, R.utils, readxl, reshape2, rhdf5, rlang, scales, shiny (>= 1.7.0), shinycssloaders, stats, tibble, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling, biomaRt, remotes License: MIT + file LICENSE MD5sum: 8d3559b912d8367ccc625bd9a0e91fa4 NeedsCompilation: no Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/cTRAP, https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues git_url: https://git.bioconductor.org/packages/cTRAP git_branch: RELEASE_3_16 git_last_commit: 2c43ff9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cTRAP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cTRAP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cTRAP_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cTRAP_1.16.0.tgz vignettes: vignettes/cTRAP/inst/doc/cTRAP.html vignetteTitles: cTRAP: identifying candidate causal perturbations from differential gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R dependencyCount: 162 Package: ctsGE Version: 1.24.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: 2cdec15fa80267e18281aee7f4313698 NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: RELEASE_3_16 git_last_commit: c426e56 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ctsGE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ctsGE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ctsGE_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ctsGE_1.24.0.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 72 Package: CTSV Version: 1.0.0 Depends: R (>= 4.2), Imports: stats, pscl, qvalue, BiocParallel, methods, knitr, SpatialExperiment, SummarizedExperiment Suggests: testthat, BiocStyle License: GPL-3 MD5sum: 344992bc0638c5c06fd46ebe6ecf77ac NeedsCompilation: yes Title: Identification of cell-type-specific spatially variable genes accounting for excess zeros Description: The R package CTSV implements the CTSV approach developed by Jinge Yu and Xiangyu Luo that detects cell-type-specific spatially variable genes accounting for excess zeros. CTSV directly models sparse raw count data through a zero-inflated negative binomial regression model, incorporates cell-type proportions, and performs hypothesis testing based on R package pscl. The package outputs p-values and q-values for genes in each cell type, and CTSV is scalable to datasets with tens of thousands of genes measured on hundreds of spots. CTSV can be installed in Windows, Linux, and Mac OS. biocViews: GeneExpression, StatisticalMethod, Regression, Spatial, Genetics Author: Jinge Yu Developer [aut, cre], Xiangyu Luo Developer [aut] Maintainer: Jinge Yu Developer URL: https://github.com/jingeyu/CTSV VignetteBuilder: knitr BugReports: https://github.com/jingeyu/CTSV/issues git_url: https://git.bioconductor.org/packages/CTSV git_branch: RELEASE_3_16 git_last_commit: 91cfe58 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CTSV_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CTSV_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CTSV_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CTSV_1.0.0.tgz vignettes: vignettes/CTSV/inst/doc/CTSV.html vignetteTitles: Basic Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CTSV/inst/doc/CTSV.R dependencyCount: 121 Package: cummeRbund Version: 2.40.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: a1342d9f6f0b921c29f50b5e7260ede7 NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: RELEASE_3_16 git_last_commit: 852ad0a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cummeRbund_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cummeRbund_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cummeRbund_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cummeRbund_2.40.0.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R, vignettes/cummeRbund/inst/doc/cummeRbund-manual.R dependencyCount: 156 Package: customCMPdb Version: 1.8.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 080cd0d0cfdb6834b772f31f3dcf95bd NeedsCompilation: no Title: Customize and Query Compound Annotation Database Description: This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections. biocViews: Software, Cheminformatics,AnnotationHubSoftware Author: Yuzhu Duan [aut, cre], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/customCMPdb/ VignetteBuilder: knitr BugReports: https://github.com/yduan004/customCMPdb/issues git_url: https://git.bioconductor.org/packages/customCMPdb git_branch: RELEASE_3_16 git_last_commit: 7a4d666 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/customCMPdb_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/customCMPdb_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/customCMPdb_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/customCMPdb_1.8.0.tgz vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html vignetteTitles: customCMPdb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R dependencyCount: 118 Package: customProDB Version: 1.38.0 Depends: R (>= 3.5.0), IRanges, AnnotationDbi, biomaRt (>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 63307a7c99f1fffb9a156a761663580a NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: RELEASE_3_16 git_last_commit: cfe95f1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/customProDB_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/customProDB_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/customProDB_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/customProDB_1.38.0.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R dependencyCount: 101 Package: cyanoFilter Version: 1.6.0 Depends: R(>= 4.1.0) Imports: Biobase, flowCore, flowDensity, flowClust, cytometree, ggplot2, GGally, graphics, grDevices, methods, mrfDepth, stats, utils Suggests: magrittr, dplyr, purrr, knitr, stringr, rmarkdown, tidyr License: MIT + file LICENSE MD5sum: eeb7d85f0c12e350bcd2c0040958c440 NeedsCompilation: no Title: Phytoplankton Population Identification using Cell Pigmentation and/or Complexity Description: An approach to filter out and/or identify phytoplankton cells from all particles measured via flow cytometry pigment and cell complexity information. It does this using a sequence of one-dimensional gates on pre-defined channels measuring certain pigmentation and complexity. The package is especially tuned for cyanobacteria, but will work fine for phytoplankton communities where there is at least one cell characteristic that differentiates every phytoplankton in the community. biocViews: FlowCytometry, Clustering, OneChannel Author: Oluwafemi Olusoji [cre, aut], Aerts Marc [ctb], Delaender Frederik [ctb], Neyens Thomas [ctb], Spaak jurg [aut] Maintainer: Oluwafemi Olusoji URL: https://github.com/fomotis/cyanoFilter VignetteBuilder: knitr BugReports: https://github.com/fomotis/cyanoFilter/issues git_url: https://git.bioconductor.org/packages/cyanoFilter git_branch: RELEASE_3_16 git_last_commit: 58c5eae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cyanoFilter_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cyanoFilter_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cyanoFilter_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cyanoFilter_1.6.0.tgz vignettes: vignettes/cyanoFilter/inst/doc/cyanoFilter.html vignetteTitles: cyanoFilter: A Semi-Automated Framework for Identifying Phytplanktons and Cyanobacteria Population in Flow Cytometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cyanoFilter/inst/doc/cyanoFilter.R dependencyCount: 152 Package: cycle Version: 1.52.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: 2fd4015a2a012a16a55ef20c9290265b NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: RELEASE_3_16 git_last_commit: 3699807 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cycle_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cycle_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cycle_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cycle_1.52.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 17 Package: cydar Version: 1.22.0 Depends: SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors LinkingTo: Rcpp Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 Archs: x64 MD5sum: c78c0371107cab0d00786b9dec03d3c2 NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: RELEASE_3_16 git_last_commit: 96c46a2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cydar_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cydar_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cydar_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cydar_1.22.0.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differential abundance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 95 Package: CytoDx Version: 1.18.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 95d2685deac2619ca30eb1713546a419 NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_16 git_last_commit: bfbce43 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CytoDx_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CytoDx_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CytoDx_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CytoDx_1.18.0.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 48 Package: CyTOFpower Version: 1.4.0 Depends: R (>= 4.1) Imports: CytoGLMM, diffcyt, DT, dplyr, ggplot2, magrittr, methods, rlang, stats, shiny, shinyFeedback, shinyjs, shinyMatrix, SummarizedExperiment, tibble, tidyr Suggests: testthat (>= 3.0.0), BiocStyle, knitr License: LGPL-3 MD5sum: 85659747d3b927c73dedf3da73b4131b NeedsCompilation: no Title: Power analysis for CyTOF experiments Description: This package is a tool to predict the power of CyTOF experiments in the context of differential state analyses. The package provides a shiny app with two options to predict the power of an experiment: i. generation of in-sicilico CyTOF data, using users input ii. browsing in a grid of parameters for which the power was already precomputed. biocViews: FlowCytometry, SingleCell, CellBiology, StatisticalMethod, Software Author: Anne-Maud Ferreira [cre, aut] (), Catherine Blish [aut], Susan Holmes [aut] Maintainer: Anne-Maud Ferreira VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CyTOFpower git_branch: RELEASE_3_16 git_last_commit: dec6472 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CyTOFpower_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CyTOFpower_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CyTOFpower_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CyTOFpower_1.4.0.tgz vignettes: vignettes/CyTOFpower/inst/doc/CyTOFpower.html vignetteTitles: Power analysis for CyTOF experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CyTOFpower/inst/doc/CyTOFpower.R dependencyCount: 235 Package: CytoGLMM Version: 1.6.0 Imports: stats, methods, BiocParallel, RColorBrewer, cowplot, doParallel, dplyr, factoextra, flexmix, ggplot2, magrittr, mbest, pheatmap, speedglm, stringr, strucchange, tibble, ggrepel, MASS, logging, Matrix, tidyr, caret, rlang, grDevices Suggests: knitr, rmarkdown, testthat, BiocStyle License: LGPL-3 MD5sum: 6d8317453ff879d25487a41d27fd7ecb NeedsCompilation: no Title: Conditional Differential Analysis for Flow and Mass Cytometry Experiments Description: The CytoGLMM R package implements two multiple regression strategies: A bootstrapped generalized linear model (GLM) and a generalized linear mixed model (GLMM). Most current data analysis tools compare expressions across many computationally discovered cell types. CytoGLMM focuses on just one cell type. Our narrower field of application allows us to define a more specific statistical model with easier to control statistical guarantees. As a result, CytoGLMM finds differential proteins in flow and mass cytometry data while reducing biases arising from marker correlations and safeguarding against false discoveries induced by patient heterogeneity. biocViews: FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, ImmunoOncology, Regression, StatisticalMethod, Software Author: Christof Seiler [aut, cre] () Maintainer: Christof Seiler URL: https://christofseiler.github.io/CytoGLMM, https://github.com/ChristofSeiler/CytoGLMM VignetteBuilder: knitr BugReports: https://github.com/ChristofSeiler/CytoGLMM/issues git_url: https://git.bioconductor.org/packages/CytoGLMM git_branch: RELEASE_3_16 git_last_commit: d23fc47 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CytoGLMM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CytoGLMM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/CytoGLMM_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/CytoGLMM_1.6.0.tgz vignettes: vignettes/CytoGLMM/inst/doc/CytoGLMM.html vignetteTitles: CytoGLMM Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoGLMM/inst/doc/CytoGLMM.R importsMe: CyTOFpower dependencyCount: 182 Package: cytoKernel Version: 1.4.0 Depends: R (>= 4.1) Imports: Rcpp, SummarizedExperiment, utils, methods, ComplexHeatmap, circlize, ashr, data.table, BiocParallel, dplyr, stats, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 Archs: x64 MD5sum: 162ed3a92413fd301c277f3f8b22d9b5 NeedsCompilation: yes Title: Differential expression using kernel-based score test Description: cytoKernel implements a kernel-based score test to identify differentially expressed features in high-dimensional biological experiments. This approach can be applied across many different high-dimensional biological data including gene expression data and dimensionally reduced cytometry-based marker expression data. In this R package, we implement functions that compute the feature-wise p values and their corresponding adjusted p values. Additionally, it also computes the feature-wise shrunk effect sizes and their corresponding shrunken effect size. Further, it calculates the percent of differentially expressed features and plots user-friendly heatmap of the top differentially expressed features on the rows and samples on the columns. biocViews: ImmunoOncology, Proteomics, SingleCell, Software, OneChannel, FlowCytometry, DifferentialExpression, GeneExpression, Clustering Author: Tusharkanti Ghosh [aut, cre], Victor Lui [aut], Pratyaydipta Rudra [aut], Souvik Seal [aut], Thao Vu [aut], Elena Hsieh [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/cytoKernel/issues git_url: https://git.bioconductor.org/packages/cytoKernel git_branch: RELEASE_3_16 git_last_commit: 0aaad3e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cytoKernel_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytoKernel_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cytoKernel_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cytoKernel_1.4.0.tgz vignettes: vignettes/cytoKernel/inst/doc/cytoKernel.html vignetteTitles: The CytoK user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoKernel/inst/doc/cytoKernel.R dependencyCount: 77 Package: cytolib Version: 2.10.1 Depends: R (>= 3.4) Imports: RProtoBufLib LinkingTo: BH(>= 1.75.0.0), RProtoBufLib(>= 2.3.5),Rhdf5lib Suggests: knitr, rmarkdown License: AGPL-3.0-only License_restricts_use: no Archs: x64 MD5sum: 3b158d63c5164d3f97c567325b1a8b2c NeedsCompilation: yes Title: C++ infrastructure for representing and interacting with the gated cytometry data Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: RELEASE_3_16 git_last_commit: b293526 git_last_commit_date: 2023-01-23 Date/Publication: 2023-01-23 source.ver: src/contrib/cytolib_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytolib_2.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cytolib_2.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cytolib_2.10.0.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cytolib/inst/doc/cytolib.R importsMe: CytoML, flowCore, flowWorkspace linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 3 Package: cytomapper Version: 1.10.1 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: SpatialExperiment, S4Vectors, BiocParallel, HDF5Array, DelayedArray, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats, rhdf5, nnls Suggests: BiocStyle, knitr, rmarkdown, markdown, cowplot, testthat, shinytest License: GPL (>= 2) MD5sum: 12b542b73613b6f0389e6be8a7cfd96f NeedsCompilation: no Title: Visualization of highly multiplexed imaging data in R Description: Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultipleComparison, Normalization, DataImport Author: Nils Eling [aut, cre] (), Nicolas Damond [aut] (), Tobias Hoch [ctb] Maintainer: Nils Eling URL: https://github.com/BodenmillerGroup/cytomapper VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytomapper/issues git_url: https://git.bioconductor.org/packages/cytomapper git_branch: RELEASE_3_16 git_last_commit: 73da749 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/cytomapper_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytomapper_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/cytomapper_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cytomapper_1.10.1.tgz vignettes: vignettes/cytomapper/inst/doc/cytomapper_ondisk.html, vignettes/cytomapper/inst/doc/cytomapper.html vignetteTitles: "On disk storage of images", "Visualization of imaging cytometry data in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytomapper/inst/doc/cytomapper_ondisk.R, vignettes/cytomapper/inst/doc/cytomapper.R importsMe: imcRtools, simpleSeg dependencyCount: 155 Package: cytoMEM Version: 1.2.0 Depends: R (>= 4.2.0) Imports: gplots, tools, flowCore, grDevices, stats, utils, matrixStats, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: df6d7a8cd1d1cee932c7f40590d5af18 NeedsCompilation: no Title: Marker Enrichment Modeling (MEM) Description: MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry, mass cytometry, single cell RNA-seq, and spectral flow cytometry using RMSD. biocViews: Proteomics, SystemsBiology, Classification, FlowCytometry, DataRepresentation, DataImport, CellBiology, SingleCell, Clustering Author: Sierra Lima [aut] (), Kirsten Diggins [aut] (), Jonathan Irish [aut, cre] () Maintainer: Jonathan Irish URL: https://github.com/cytolab/cytoMEM VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytoMEM git_branch: RELEASE_3_16 git_last_commit: 3a50f09 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/cytoMEM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/cytoMEM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/cytoMEM_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/cytoMEM_1.2.0.tgz vignettes: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.html vignetteTitles: Intro_to_Marker_Enrichment_Modeling_Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytoMEM/inst/doc/Intro_to_Marker_Enrichment_Modeling_Analysis.R dependencyCount: 23 Package: CytoML Version: 2.10.0 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.3.10), flowCore (>= 1.99.10), flowWorkspace (>= 4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL, Rgraphviz, Biobase, methods, graph, graphics, utils, jsonlite, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, stats, tibble LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib, cytolib, Rhdf5lib, flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, rmarkdown, parallel License: AGPL-3.0-only License_restricts_use: no Archs: x64 MD5sum: 71bcccc39f35834d4781d9bd5c7c9d2c NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, Jake Wagner Maintainer: Mike Jiang URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: RELEASE_3_16 git_last_commit: a073fe2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/CytoML_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/CytoML_2.10.0.zip vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/flowjo_to_gatingset.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/flowjo_to_gatingset.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R suggestsMe: FlowSOM, flowWorkspace, openCyto dependencyCount: 80 Package: dada2 Version: 1.26.0 Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-2 Archs: x64 MD5sum: b633d014f891b3c096c594b976bca494 NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: RELEASE_3_16 git_last_commit: 71af423 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dada2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dada2_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dada2_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dada2_1.26.0.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R importsMe: Rbec suggestsMe: mia dependencyCount: 81 Package: dagLogo Version: 1.36.0 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack, httr Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 5f03fce26f9ca11e3d8121b97b9214e5 NeedsCompilation: no Title: dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: RELEASE_3_16 git_last_commit: 3c7ae92 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dagLogo_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dagLogo_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dagLogo_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dagLogo_1.36.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 157 Package: daMA Version: 1.70.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 0b4f775a14999125edd0cc4fe48893d2 NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: RELEASE_3_16 git_last_commit: 066b476 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/daMA_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/daMA_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/daMA_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/daMA_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.10.1 Depends: R (>= 4.0) Imports: stats, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, readr, SummarizedExperiment, GenomicAlignments, stringr, plyr, VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics, methods, limma, bumphunter, Biostrings, reshape2, cowplot, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 9c63cc715a135654263c69f41966d329 NeedsCompilation: no Title: Finds DAMEs - Differential Allelicly MEthylated regions Description: 'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots. biocViews: DNAMethylation, DifferentialMethylation, Coverage Author: Stephany Orjuela [aut, cre] (), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: RELEASE_3_16 git_last_commit: 731e855 git_last_commit_date: 2022-12-06 Date/Publication: 2022-12-06 source.ver: src/contrib/DAMEfinder_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/DAMEfinder_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/DAMEfinder_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DAMEfinder_1.10.1.tgz vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html vignetteTitles: DAMEfinder Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R dependencyCount: 127 Package: DaMiRseq Version: 2.10.0 Depends: R (>= 3.5.0), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR, plyr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: a69e43d1c439f138eb8fe8588f21403f NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: RELEASE_3_16 git_last_commit: 2bf8822 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DaMiRseq_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DaMiRseq_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DaMiRseq_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DaMiRseq_2.10.0.tgz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS dependencyCount: 255 Package: DAPAR Version: 1.30.6 Depends: R (>= 4.2.0) Imports: Biobase, MSnbase, DAPARdata (>= 1.28.0), utils, highcharter, foreach Suggests: testthat, BiocStyle, AnnotationDbi, clusterProfiler, graph, diptest, cluster, vioplot, visNetwork, vsn, igraph, FactoMineR, factoextra, dendextend, parallel, doParallel, Mfuzz, apcluster, forcats, readxl, openxlsx, multcomp, purrr, tibble, knitr, norm, scales, tidyverse, cp4p, imp4p (>= 1.1),lme4, dplyr, limma, preprocessCore, stringr, tidyr, impute, gplots, grDevices, reshape2, graphics, stats, methods, ggplot2, RColorBrewer, Matrix, org.Sc.sgd.db License: Artistic-2.0 MD5sum: a32553bb8fe92274937703b8a02b5b5b NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: The package DAPAR is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required (see `Prostar` package). biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [aut, cre], Florence Combes [aut], Thomas Burger [aut], Vasile-Cosmin Lazar [ctb], Enora Fremy [ctb], Helene Borges [ctb] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_16 git_last_commit: 3984ff9 git_last_commit_date: 2023-02-23 Date/Publication: 2023-02-23 source.ver: src/contrib/DAPAR_1.30.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/DAPAR_1.30.6.zip mac.binary.ver: bin/macosx/contrib/4.2/DAPAR_1.30.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DAPAR_1.30.6.tgz vignettes: vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar suggestsMe: DAPARdata, mi4p dependencyCount: 122 Package: DART Version: 1.46.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: f6c156909eea46417349a2328ec138a0 NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: RELEASE_3_16 git_last_commit: ff68187 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DART_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DART_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DART_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DART_1.46.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 13 Package: dasper Version: 1.7.0 Depends: R (>= 4.0) Imports: basilisk, BiocFileCache, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, ggpubr, ggrepel, grid, IRanges, magrittr, megadepth, methods, plyranges, readr, reticulate, rtracklayer, S4Vectors, stringr, SummarizedExperiment, tidyr Suggests: AnnotationFilter, BiocStyle, covr, ensembldb, GenomicState, knitr, lifecycle, markdown, recount, RefManageR, rmarkdown, sessioninfo, testthat, tibble License: Artistic-2.0 MD5sum: 03063c741ff942b14988308afa0d8db0 NeedsCompilation: no Title: Detecting abberant splicing events from RNA-sequencing data Description: The aim of dasper is to detect aberrant splicing events from RNA-seq data. dasper will use as input both junction and coverage data from RNA-seq to calculate the deviation of each splicing event in a patient from a set of user-defined controls. dasper uses an unsupervised outlier detection algorithm to score each splicing event in the patient with an outlier score representing the degree to which that splicing event looks abnormal. biocViews: Software, RNASeq, Transcriptomics, AlternativeSplicing, Coverage, Sequencing Author: David Zhang [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: David Zhang URL: https://github.com/dzhang32/dasper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/dasper git_url: https://git.bioconductor.org/packages/dasper git_branch: master git_last_commit: ac4295d git_last_commit_date: 2022-04-26 Date/Publication: 2022-05-01 source.ver: src/contrib/dasper_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dasper_1.8.0.zip vignettes: vignettes/dasper/inst/doc/dasper.html vignetteTitles: Introduction to dasper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dasper/inst/doc/dasper.R importsMe: ODER dependencyCount: 180 Package: dcanr Version: 1.14.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: fa72b8e5f0f844eae6390577a607b88e NeedsCompilation: no Title: Differential co-expression/association network analysis Description: This package implements methods and an evaluation framework to infer differential co-expression/association networks. Various methods are implemented and can be evaluated using simulated datasets. Inference of differential co-expression networks can allow identification of networks that are altered between two conditions (e.g., health and disease). biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: RELEASE_3_16 git_last_commit: ffdded1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dcanr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dcanr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dcanr_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dcanr_1.14.0.tgz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R importsMe: SingscoreAMLMutations dependencyCount: 35 Package: dce Version: 1.6.0 Depends: R (>= 4.1) Imports: stats, methods, assertthat, graph, pcalg, purrr, tidyverse, Matrix, ggraph, tidygraph, ggplot2, rlang, expm, MASS, edgeR, epiNEM, igraph, metap, mnem, naturalsort, ppcor, glm2, graphite, reshape2, dplyr, magrittr, glue, Rgraphviz, harmonicmeanp, org.Hs.eg.db, logger, shadowtext Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, cowplot, ggplotify, dagitty, lmtest, sandwich, devtools, curatedTCGAData, TCGAutils, SummarizedExperiment, RcppParallel, docopt, CARNIVAL License: GPL-3 MD5sum: 2d5262b4d89619cb9749b4a994c6b3aa NeedsCompilation: no Title: Pathway Enrichment Based on Differential Causal Effects Description: Compute differential causal effects (dce) on (biological) networks. Given observational samples from a control experiment and non-control (e.g., cancer) for two genes A and B, we can compute differential causal effects with a (generalized) linear regression. If the causal effect of gene A on gene B in the control samples is different from the causal effect in the non-control samples the dce will differ from zero. We regularize the dce computation by the inclusion of prior network information from pathway databases such as KEGG. biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression, GeneExpression, DifferentialExpression, NetworkEnrichment, Network, KEGG Author: Kim Philipp Jablonski [aut, cre] (), Martin Pirkl [aut] Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/dce VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/dce/issues git_url: https://git.bioconductor.org/packages/dce git_branch: RELEASE_3_16 git_last_commit: c96a8ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dce_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dce_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dce_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dce_1.6.0.tgz vignettes: vignettes/dce/inst/doc/dce.html, vignettes/dce/inst/doc/pathway_databases.html vignetteTitles: Get started, Overview of pathway network databases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dce/inst/doc/dce.R, vignettes/dce/inst/doc/pathway_databases.R dependencyCount: 242 Package: dcGSA Version: 1.26.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 8cc4eb1d6217eac647ee3861091d5fb5 NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: RELEASE_3_16 git_last_commit: a5edad0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dcGSA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dcGSA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dcGSA_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dcGSA_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 18 Package: ddCt Version: 1.54.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: testthat (>= 3.0.0), RUnit License: LGPL-3 MD5sum: dad22ce3a853722f1866046cfc196330 NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: RELEASE_3_16 git_last_commit: e634e75 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ddCt_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ddCt_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ddCt_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ddCt_1.54.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 11 Package: ddPCRclust Version: 1.18.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: 6fee35cd73bf0de1310b501da8366865 NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: RELEASE_3_16 git_last_commit: 8205654 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ddPCRclust_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ddPCRclust_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ddPCRclust_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ddPCRclust_1.18.0.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R dependencyCount: 141 Package: dearseq Version: 1.10.0 Depends: R (>= 3.6.0) Imports: CompQuadForm, dplyr, ggplot2, KernSmooth, magrittr, matrixStats, methods, patchwork, parallel, pbapply, reshape2, rlang, scattermore, stats, statmod, survey, tibble, viridisLite Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: 6e0446710c4c34c6e317fb66bc8e75b4 NeedsCompilation: no Title: Differential Expression Analysis for RNA-seq data through a robust variance component test Description: Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: i) Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604 ; and ii) Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2020) dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, NAR Genomics and Bioinformatics, 2(4):lqaa093. biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression, DNASeq, GeneExpression, Genetics, GeneSetEnrichment, ImmunoOncology, KEGG, Regression, RNASeq, Sequencing, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre], Marine Gauthier [aut], Mélanie Huchon [ctb] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: RELEASE_3_16 git_last_commit: 087f4f4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dearseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dearseq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dearseq_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dearseq_1.10.0.tgz vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html vignetteTitles: dearseqUserguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R importsMe: benchdamic suggestsMe: TcGSA dependencyCount: 58 Package: debCAM Version: 1.16.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR2, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 MD5sum: f35c71fd173ba6e56cf77fafc6f839e2 NeedsCompilation: no Title: Deconvolution by Convex Analysis of Mixtures Description: An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/debCAM/issues git_url: https://git.bioconductor.org/packages/debCAM git_branch: RELEASE_3_16 git_last_commit: f4ee89d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/debCAM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/debCAM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/debCAM_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/debCAM_1.16.0.tgz vignettes: vignettes/debCAM/inst/doc/debcam.html vignetteTitles: debCAM User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/debCAM/inst/doc/debcam.R dependencyCount: 112 Package: debrowser Version: 1.26.3 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview, apeglm, ashr Suggests: testthat, rmarkdown, knitr License: GPL-3 + file LICENSE MD5sum: 9235cc95f042f36c9a66f412b5930f34 NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, rmarkdown BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: RELEASE_3_16 git_last_commit: 4fb9588 git_last_commit_date: 2023-03-12 Date/Publication: 2023-03-13 source.ver: src/contrib/debrowser_1.26.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/debrowser_1.26.3.zip mac.binary.ver: bin/macosx/contrib/4.2/debrowser_1.26.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/debrowser_1.26.3.tgz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 223 Package: DECIPHER Version: 2.26.0 Depends: R (>= 3.5.0), Biostrings (>= 2.59.1), RSQLite (>= 1.1), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 Archs: x64 MD5sum: 242d3228884db231e85fd8a0f7e2bd7d NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology, GenePrediction Author: Erik Wright Maintainer: Erik Wright git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: RELEASE_3_16 git_last_commit: 7de99ec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DECIPHER_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DECIPHER_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DECIPHER_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DECIPHER_2.26.0.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/ClusteringSequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf, vignettes/DECIPHER/inst/doc/FindingGenes.pdf, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.pdf, vignettes/DECIPHER/inst/doc/GrowingTrees.pdf, vignettes/DECIPHER/inst/doc/RepeatRepeat.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, Upsize your clustering with Clusterize, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers That Yield Group-Specific Signatures, Finding Chimeric Sequences, The Magic of Gene Finding, The Double Life of RNA: Uncovering Non-Coding RNAs, Growing phylogenetic trees with TreeLine, Detecting obscure tandem repeats in sequences hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/ClusteringSequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R, vignettes/DECIPHER/inst/doc/FindingGenes.R, vignettes/DECIPHER/inst/doc/FindingNonCodingRNAs.R, vignettes/DECIPHER/inst/doc/GrowingTrees.R, vignettes/DECIPHER/inst/doc/RepeatRepeat.R dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: mia, openPrimeR, scifer, AssessORFData, copyseparator, ensembleTax suggestsMe: MicrobiotaProcess, microbial, pagoo dependencyCount: 35 Package: deco Version: 1.13.0 Depends: R (>= 3.5.0), AnnotationDbi, BiocParallel, SummarizedExperiment, limma Imports: stats, methods, ggplot2, foreign, graphics, BiocStyle, Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4, sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2, gridExtra Suggests: knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens, rmarkdown License: GPL (>=3) MD5sum: 2a6fcb887712cc6591e2800ed5b6f953 NeedsCompilation: no Title: Decomposing Heterogeneous Cohorts using Omic Data Profiling Description: This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels. biocViews: Software, FeatureExtraction, Clustering, MultipleComparison, DifferentialExpression, Transcriptomics, BiomedicalInformatics, Proteomics, Bayesian, GeneExpression, Transcription, Sequencing, Microarray, ExonArray, RNASeq, MicroRNAArray, mRNAMicroarray Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Francisco Jose Campos Laborie URL: https://github.com/fjcamlab/deco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deco git_branch: master git_last_commit: 16b75a5 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/deco_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deco_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deco_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/deco_1.14.0.tgz vignettes: vignettes/deco/inst/doc/DECO.html vignetteTitles: deco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deco/inst/doc/DECO.R dependencyCount: 125 Package: DEComplexDisease Version: 1.18.0 Depends: R (>= 3.3.3) Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment, ComplexHeatmap, grid, parallel, BiocParallel, grDevices, graphics, stats, methods, utils LinkingTo: Rcpp Suggests: knitr License: GPL-3 Archs: x64 MD5sum: 13d50403d7b5b44f1c3f40dd1adca03e NeedsCompilation: yes Title: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis Description: It is designed to find the differential expressed genes (DEGs) for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DECD finds the DEGs shared by as many patients. In this way, DECD describes the DEGs of complex disease in a novel syntax, e.g. a gene list composed of 200 genes are differentially expressed in 30% percent of studied complex disease. Applying the DECD analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures. biocViews: DNASeq, WholeGenome, FunctionalGenomics, DifferentialExpression,GeneExpression, Clustering Author: Guofeng Meng Maintainer: Guofeng Meng VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/DEComplexDisease git_branch: RELEASE_3_16 git_last_commit: a0839b6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-03 source.ver: src/contrib/DEComplexDisease_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEComplexDisease_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEComplexDisease_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEComplexDisease_1.18.0.tgz vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf, vignettes/DEComplexDisease/inst/doc/decd.html vignetteTitles: DEComplexDisease: a R package for DE analysis, DEComplexDisease: a R package for DE analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R dependencyCount: 105 Package: decompTumor2Sig Version: 2.14.0 Depends: R(>= 4.0), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table, GenomeInfoDb, readxl Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 133665fcef3a5b9762f2fc9f9f89d939 NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: RELEASE_3_16 git_last_commit: b172b7a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/decompTumor2Sig_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/decompTumor2Sig_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/decompTumor2Sig_2.14.0.tgz vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R importsMe: musicatk dependencyCount: 123 Package: DeconRNASeq Version: 1.40.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 3ee38ccd0942a5b22c845753ccbe8d19 NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: RELEASE_3_16 git_last_commit: 7c9ec83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DeconRNASeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeconRNASeq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeconRNASeq_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DeconRNASeq_1.40.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R suggestsMe: ADAPTS dependencyCount: 42 Package: decontam Version: 1.18.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: b948a8d78cc9f506a0163ff386efe2b1 NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan [aut, cre], Nicole Marie Davis [aut], Felix G.M. Ernst [ctb] () Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: RELEASE_3_16 git_last_commit: 5a5ef3f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/decontam_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/decontam_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/decontam_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/decontam_1.18.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R importsMe: mia dependencyCount: 41 Package: deconvR Version: 1.4.3 Depends: R (>= 4.1), data.table (>= 1.14.0) Imports: S4Vectors (>= 0.30.0), methylKit (>= 1.18.0), IRanges (>= 2.26.0), GenomicRanges (>= 1.44.0), BiocGenerics (>= 0.38.0), stats, methods, foreach (>= 1.5.1), magrittr (>= 2.0.1), matrixStats (>= 0.61.0), e1071 (>= 1.7.9), quadprog (>= 1.5.8), nnls (>= 1.4), rsq (>= 2.2), MASS, utils, dplyr (>= 1.0.7), tidyr (>= 1.1.3), assertthat, minfi Suggests: testthat (>= 3.0.0), roxygen2 (>= 7.1.2), doParallel (>= 1.0.16), parallel, knitr (>= 1.34), BiocStyle (>= 2.20.2), reshape2 (>= 1.4.4), ggplot2 (>= 3.3.5), rmarkdown, devtools (>= 2.4.2), sessioninfo (>= 1.1.1), covr, granulator, RefManageR License: Artistic-2.0 MD5sum: cf6c2d0a9043360af86246e77307bcbc NeedsCompilation: no Title: Simulation and Deconvolution of Omic Profiles Description: This package provides a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. Users are given the option to create or extend a reference atlas and,also simulate the desired size of the bulk signature profile of the reference cell types.The package includes the cell-type-specific methylation atlas and, Illumina Epic B5 probe ids that can be used in deconvolution. Additionally,we included BSmeth2Probe, to make mapping WGBS data to their probe IDs easier. biocViews: DNAMethylation, Regression, GeneExpression, RNASeq, SingleCell, StatisticalMethod, Transcriptomics Author: Irem B. Gündüz [aut, cre] (), Veronika Ebenal [aut] (), Altuna Akalin [aut] () Maintainer: Irem B. Gündüz URL: https://github.com/BIMSBbioinfo/deconvR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/deconvR git_url: https://git.bioconductor.org/packages/deconvR git_branch: RELEASE_3_16 git_last_commit: 8ccbdb9 git_last_commit_date: 2022-12-03 Date/Publication: 2022-12-05 source.ver: src/contrib/deconvR_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/deconvR_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.2/deconvR_1.4.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/deconvR_1.4.5.tgz vignettes: vignettes/deconvR/inst/doc/deconvRVignette.html vignetteTitles: deconvRVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deconvR/inst/doc/deconvRVignette.R dependencyCount: 189 Package: decoupleR Version: 2.4.0 Depends: R (>= 4.0) Imports: broom, dplyr, magrittr, Matrix, purrr, rlang, stats, stringr, tibble, tidyr, tidyselect, withr Suggests: glmnet (>= 4.1.0), GSVA, viper, fgsea (>= 1.15.4), AUCell, SummarizedExperiment, rpart, ranger, BiocStyle, covr, knitr, pkgdown, RefManageR, rmarkdown, roxygen2, sessioninfo, pheatmap, testthat, OmnipathR, Seurat, ggplot2, ggrepel, patchwork License: GPL-3 + file LICENSE MD5sum: 5bcdf1db35f539ea6e7e32ed40e3d434 NeedsCompilation: no Title: decoupleR: Ensemble of computational methods to infer biological activities from omics data Description: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor package containing different statistical methods to extract these signatures within a unified framework. decoupleR allows the user to flexibly test any method with any resource. It incorporates methods that take into account the sign and weight of network interactions. decoupleR can be used with any omic, as long as its features can be linked to a biological process based on prior knowledge. For example, in transcriptomics gene sets regulated by a transcription factor, or in phospho-proteomics phosphosites that are targeted by a kinase. biocViews: DifferentialExpression, FunctionalGenomics, GeneExpression, GeneRegulation, Network, Software, StatisticalMethod, Transcription, Author: Pau Badia-i-Mompel [aut, cre] (), Jesús Vélez-Santiago [aut] (), Jana Braunger [aut] (), Celina Geiss [aut] (), Daniel Dimitrov [aut] (), Sophia Müller-Dott [aut] (), Petr Taus [aut] (), Aurélien Dugourd [aut] (), Christian H. Holland [aut] (), Ricardo O. Ramirez Flores [aut] (), Julio Saez-Rodriguez [aut] () Maintainer: Pau Badia-i-Mompel URL: https://saezlab.github.io/decoupleR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/decoupleR/issues git_url: https://git.bioconductor.org/packages/decoupleR git_branch: RELEASE_3_16 git_last_commit: 2847935 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/decoupleR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/decoupleR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/decoupleR_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/decoupleR_2.4.0.tgz vignettes: vignettes/decoupleR/inst/doc/decoupleR.html, vignettes/decoupleR/inst/doc/pw_bk.html, vignettes/decoupleR/inst/doc/pw_sc.html, vignettes/decoupleR/inst/doc/tf_bk.html, vignettes/decoupleR/inst/doc/tf_sc.html vignetteTitles: Introduction, Pathway activity inference in bulk RNA-seq, Pathway activity activity inference from scRNA-seq, Transcription factor activity inference in bulk RNA-seq, Transcription factor activity inference from scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/decoupleR/inst/doc/decoupleR.R, vignettes/decoupleR/inst/doc/pw_bk.R, vignettes/decoupleR/inst/doc/pw_sc.R, vignettes/decoupleR/inst/doc/tf_bk.R, vignettes/decoupleR/inst/doc/tf_sc.R importsMe: progeny dependencyCount: 33 Package: DeepBlueR Version: 1.24.1 Depends: R (>= 3.3), XML, RCurl Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods, rjson, utils, R.utils, foreach, withr, rtracklayer, GenomeInfoDb, settings, filehash Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr, RColorBrewer, matrixStats License: GPL (>=2.0) MD5sum: a21ff5b6e2fb3c87eab955680dcd19e5 NeedsCompilation: no Title: DeepBlueR Description: Accessing the DeepBlue Epigenetics Data Server through R. biocViews: DataImport, DataRepresentation, ThirdPartyClient, GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation, Epigenetics, Annotation, Preprocessing, ImmunoOncology Author: Felipe Albrecht, Markus List Maintainer: Felipe Albrecht , Markus List , Quirin Manz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepBlueR git_branch: RELEASE_3_16 git_last_commit: d404c5b git_last_commit_date: 2023-01-10 Date/Publication: 2023-01-10 source.ver: src/contrib/DeepBlueR_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeepBlueR_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/DeepBlueR_1.24.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DeepBlueR_1.24.1.tgz vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html vignetteTitles: The DeepBlue epigenomic data server - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R dependencyCount: 95 Package: DeepPINCS Version: 1.6.0 Depends: keras, R (>= 4.1) Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers, webchem, purrr, ttgsea, PRROC, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: cc81273ece84492a613d35dff09b5673 NeedsCompilation: no Title: Protein Interactions and Networks with Compounds based on Sequences using Deep Learning Description: The identification of novel compound-protein interaction (CPI) is important in drug discovery. Revealing unknown compound-protein interactions is useful to design a new drug for a target protein by screening candidate compounds. The accurate CPI prediction assists in effective drug discovery process. To identify potential CPI effectively, prediction methods based on machine learning and deep learning have been developed. Data for sequences are provided as discrete symbolic data. In the data, compounds are represented as SMILES (simplified molecular-input line-entry system) strings and proteins are sequences in which the characters are amino acids. The outcome is defined as a variable that indicates how strong two molecules interact with each other or whether there is an interaction between them. In this package, a deep-learning based model that takes only sequence information of both compounds and proteins as input and the outcome as output is used to predict CPI. The model is implemented by using compound and protein encoders with useful features. The CPI model also supports other modeling tasks, including protein-protein interaction (PPI), chemical-chemical interaction (CCI), or single compounds and proteins. Although the model is designed for proteins, DNA and RNA can be used if they are represented as sequences. biocViews: Software, Network, GraphAndNetwork, NeuralNetwork Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepPINCS git_branch: RELEASE_3_16 git_last_commit: 873bd03 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DeepPINCS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeepPINCS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeepPINCS_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DeepPINCS_1.6.0.tgz vignettes: vignettes/DeepPINCS/inst/doc/DeepPINCS.html vignetteTitles: DeepPINCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepPINCS/inst/doc/DeepPINCS.R importsMe: GenProSeq, VAExprs dependencyCount: 158 Package: deepSNV Version: 1.44.0 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.27.6), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 5083e5404197de4c3d29ca070d32809a NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], John Marshall [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: RELEASE_3_16 git_last_commit: a36f965 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/deepSNV_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deepSNV_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deepSNV_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/deepSNV_1.44.0.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R importsMe: mitoClone2 suggestsMe: GenomicFiles dependencyCount: 100 Package: DEFormats Version: 1.26.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: b61d2716b59d6a167c20c5ba5687f131 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: RELEASE_3_16 git_last_commit: 4c9b97c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEFormats_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEFormats_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEFormats_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEFormats_1.26.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: ideal dependencyCount: 96 Package: DegNorm Version: 1.8.2 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, parallel, foreach, S4Vectors, doParallel, Rsamtools (>= 1.31.2), GenomicAlignments, heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges, plyr, plotly, utils,viridis LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges Suggests: knitr,rmarkdown,formatR License: LGPL (>= 3) Archs: x64 MD5sum: 8e642cec5f28196ef3ecf8a8dd76580d NeedsCompilation: yes Title: DegNorm: degradation normalization for RNA-seq data Description: This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage, DifferentialExpression, BatchEffect,Software,Sequencing, ImmunoOncology, QualityControl, DataImport Author: Bin Xiong and Ji-Ping Wang Maintainer: Ji-Ping Wang VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues git_url: https://git.bioconductor.org/packages/DegNorm git_branch: RELEASE_3_16 git_last_commit: cfa5bdd git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/DegNorm_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/DegNorm_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/DegNorm_1.8.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DegNorm_1.8.2.tgz vignettes: vignettes/DegNorm/inst/doc/DegNorm.html vignetteTitles: DegNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R dependencyCount: 156 Package: DEGraph Version: 1.50.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: f046aae8779fc9953668e819a0bce2f8 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: RELEASE_3_16 git_last_commit: 0878c60 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEGraph_1.50.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 66 Package: DEGreport Version: 1.34.0 Depends: R (>= 4.0.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, magrittr, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: 56d4240c09a51da9ca4832b8db37d61e NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: RELEASE_3_16 git_last_commit: 4b3a6ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEGreport_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEGreport_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEGreport_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEGreport_1.34.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R importsMe: isomiRs dependencyCount: 130 Package: DEGseq Version: 1.52.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: x64 MD5sum: 3634ebf8b632faa6072bc269fa987e5a NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang and Xi Wang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_16 git_last_commit: 3839a0c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEGseq_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEGseq_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEGseq_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEGseq_1.52.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 42 Package: DelayedArray Version: 0.24.0 Depends: R (>= 4.0.0), methods, stats4, Matrix, BiocGenerics (>= 0.43.4), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2), IRanges (>= 2.17.3) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, lobstr, DelayedMatrixStats, knitr, rmarkdown, BiocStyle, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 0a80b8a189e2db3c44fda356c12b3a7c NeedsCompilation: yes Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès , with contributions from Peter Hickey and Aaron Lun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/DelayedArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedArray/issues git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: RELEASE_3_16 git_last_commit: 68ee3d0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DelayedArray_0.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedArray_0.23.2.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedArray_0.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DelayedArray_0.24.0.tgz vignettes: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.pdf, vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html vignetteTitles: Working with large arrays in R, DelayedArray / HDF5Array update, Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R, vignettes/DelayedArray/inst/doc/03-DelayedArray_HDF5Array_update.R dependsOnMe: DelayedDataFrame, DelayedMatrixStats, DelayedRandomArray, GDSArray, HDF5Array, rhdf5client, SCArray, singleCellTK, TileDBArray, VCFArray, restfulSEData importsMe: AUCell, batchelor, beachmat, bigPint, BiocSingular, bsseq, CAGEr, celaref, celda, Cepo, ChromSCape, clusterExperiment, compartmap, CRISPRseek, cytomapper, DelayedTensor, DEScan2, DropletUtils, ELMER, EWCE, flowWorkspace, FRASER, GenomicScores, glmGamPoi, GSVA, hipathia, LoomExperiment, Macarron, mbkmeans, MethReg, methrix, methylSig, mia, miaViz, minfi, MOFA2, MuData, MultiAssayExperiment, mumosa, NetActivity, netSmooth, NewWave, NxtIRFcore, orthogene, PCAtools, ResidualMatrix, RTCGAToolbox, ScaledMatrix, scater, scDblFinder, scMerge, scmeth, scPCA, scran, scry, scuttle, signatureSearch, SingleCellExperiment, SingleR, SpliceWiz, SummarizedExperiment, transformGamPoi, TSCAN, VariantExperiment, velociraptor, weitrix, xcore, zellkonverter, celldex, imcdatasets, ebvcube, scDiffCom suggestsMe: BiocGenerics, ChIPpeakAnno, CNVgears, gwascat, hermes, iSEE, MAST, ProteoDisco, S4Vectors, satuRn, SPOTlight, SQLDataFrame, TrajectoryUtils, digitalDLSorteR dependencyCount: 14 Package: DelayedDataFrame Version: 1.14.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, SeqArray, GDSArray License: GPL-3 MD5sum: 5da143c0d548a58d283b60c389024581 NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: RELEASE_3_16 git_last_commit: 811fc38 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DelayedDataFrame_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedDataFrame_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedDataFrame_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DelayedDataFrame_1.14.0.tgz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R importsMe: VariantExperiment dependencyCount: 15 Package: DelayedMatrixStats Version: 1.20.0 Depends: MatrixGenerics (>= 1.5.3), DelayedArray (>= 0.17.6) Imports: methods, matrixStats (>= 0.60.0), sparseMatrixStats, Matrix (>= 1.5-0), S4Vectors (>= 0.17.5), IRanges (>= 2.25.10) Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem, HDF5Array License: MIT + file LICENSE MD5sum: eccc9b5201ff734463db9b0e453c11a0 NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey [aut, cre], Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: RELEASE_3_16 git_last_commit: 1ed1425 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DelayedMatrixStats_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedMatrixStats_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedMatrixStats_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DelayedMatrixStats_1.20.0.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: AUCell, batchelor, biscuiteer, bsseq, CAGEr, Cepo, compartmap, dmrseq, DropletUtils, FRASER, glmGamPoi, GSVA, methrix, methylSig, mia, minfi, mumosa, NetActivity, NxtIRFcore, PCAtools, recountmethylation, SCArray, scater, scMerge, scran, scuttle, singleCellTK, SingleR, sparrow, SpliceWiz, weitrix, celldex suggestsMe: DelayedArray, EWCE, MatrixGenerics, mbkmeans, scPCA, slingshot, TrajectoryUtils, digitalDLSorteR dependencyCount: 17 Package: DelayedRandomArray Version: 1.6.0 Depends: DelayedArray Imports: methods, dqrng, Rcpp LinkingTo: dqrng, BH, Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, Matrix License: GPL-3 Archs: x64 MD5sum: 1d7f165ed8ce1db3bb2352add74c3fdc NeedsCompilation: yes Title: Delayed Arrays of Random Values Description: Implements a DelayedArray of random values where the realization of the sampled values is delayed until they are needed. Reproducible sampling within any subarray is achieved by chunking where each chunk is initialized with a different random seed and stream. The usual distributions in the stats package are supported, along with scalar, vector and arrays for the parameters. biocViews: DataRepresentation Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/DelayedRandomArray SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/DelayedRandomArray/issues git_url: https://git.bioconductor.org/packages/DelayedRandomArray git_branch: RELEASE_3_16 git_last_commit: 3de3ebc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DelayedRandomArray_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedRandomArray_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedRandomArray_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DelayedRandomArray_1.6.0.tgz vignettes: vignettes/DelayedRandomArray/inst/doc/userguide.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedRandomArray/inst/doc/userguide.R importsMe: DelayedTensor dependencyCount: 19 Package: DelayedTensor Version: 1.4.0 Depends: R (>= 4.1.0) Imports: methods, utils, DelayedArray, HDF5Array, BiocSingular, rTensor, DelayedRandomArray, irlba, Matrix, einsum, Suggests: markdown, rmarkdown, BiocStyle, knitr, testthat, magrittr, dplyr, reticulate License: Artistic-2.0 MD5sum: 59b12779b3e41f042a33bee198c3ff0a NeedsCompilation: no Title: R package for sparse and out-of-core arithmetic and decomposition of Tensor Description: DelayedTensor operates Tensor arithmetic directly on DelayedArray object. DelayedTensor provides some generic function related to Tensor arithmetic/decompotision and dispatches it on the DelayedArray class. DelayedTensor also suppors Tensor contraction by einsum function, which is inspired by numpy einsum. biocViews: Software, Infrastructure, DataRepresentation, DimensionReduction Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/DelayedTensor/issues git_url: https://git.bioconductor.org/packages/DelayedTensor git_branch: RELEASE_3_16 git_last_commit: ceff25c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DelayedTensor_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DelayedTensor_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DelayedTensor_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DelayedTensor_1.4.0.tgz vignettes: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.html, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.html vignetteTitles: DelayedTensor, TensorArithmetic, TensorDecomposition, Einsum hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedTensor/inst/doc/DelayedTensor_1.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_2.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_3.R, vignettes/DelayedTensor/inst/doc/DelayedTensor_4.R dependencyCount: 43 Package: deltaCaptureC Version: 1.12.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2, tictoc Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: b77a952031c7d4ffde1e86a0e16956d0 NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: RELEASE_3_16 git_last_commit: 6902612 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/deltaCaptureC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deltaCaptureC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deltaCaptureC_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/deltaCaptureC_1.12.0.tgz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 92 Package: deltaGseg Version: 1.38.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: b74b63f8492cde2a21a963276bb90726 NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: RELEASE_3_16 git_last_commit: 687e465 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/deltaGseg_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/deltaGseg_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/deltaGseg_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/deltaGseg_1.38.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 55 Package: DeMAND Version: 1.28.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 7693da687af4608dc1a0db9b701c6abf NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: RELEASE_3_16 git_last_commit: 1e71633 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DeMAND_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeMAND_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeMAND_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DeMAND_1.28.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.14.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc, rmarkdown, DSS, dendextend, psych, sva Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 Archs: x64 MD5sum: 01d3dac06f864ac67ed2f890ee077452 NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Shuai Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_16 git_last_commit: 8da5af8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DeMixT_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DeMixT_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DeMixT_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DeMixT_1.14.0.tgz vignettes: vignettes/DeMixT/inst/doc/demixt.html vignetteTitles: DeMixT.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/demixt.R dependencyCount: 145 Package: demuxmix Version: 1.0.0 Depends: R (>= 4.0.0) Imports: stats, MASS, Matrix, ggplot2, gridExtra, methods Suggests: BiocStyle, cowplot, DropletUtils, knitr, reshape2, rmarkdown, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: df0284f78ff8d585e18a6d0e9410ef30 NeedsCompilation: no Title: Demultiplexing oligo-barcoded scRNA-seq data using regression mixture models Description: A package for demultiplexing single-cell sequencing experiments of pooled cells labeled with barcode oligonucleotides. The package implements methods to fit regression mixture models for a probabilistic classification of cells, including multiplet detection. Demultiplexing error rates can be estimated, and methods for quality control are provided. biocViews: SingleCell, Sequencing, Preprocessing, Classification, Regression Author: Hans-Ulrich Klein [aut, cre] () Maintainer: Hans-Ulrich Klein URL: https://github.com/huklein/demuxmix VignetteBuilder: knitr BugReports: https://github.com/huklein/demuxmix/issues git_url: https://git.bioconductor.org/packages/demuxmix git_branch: RELEASE_3_16 git_last_commit: e4a91de git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/demuxmix_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/demuxmix_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/demuxmix_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/demuxmix_1.0.0.tgz vignettes: vignettes/demuxmix/inst/doc/demuxmix.html vignetteTitles: Demultiplexing cells with demuxmix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/demuxmix/inst/doc/demuxmix.R dependencyCount: 36 Package: densvis Version: 1.8.3 Imports: Rcpp, basilisk, assertthat, reticulate, irlba LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, Rtsne, uwot, testthat License: MIT + file LICENSE Archs: x64 MD5sum: beebc59f0f70b475f1703a72bed7c695 NeedsCompilation: yes Title: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction Description: Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/densvis VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues git_url: https://git.bioconductor.org/packages/densvis git_branch: RELEASE_3_16 git_last_commit: 463637d git_last_commit_date: 2023-01-25 Date/Publication: 2023-01-25 source.ver: src/contrib/densvis_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/densvis_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.2/densvis_1.8.3.tgz vignettes: vignettes/densvis/inst/doc/densvis.html vignetteTitles: Introduction to densvis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/densvis/inst/doc/densvis.R dependsOnMe: OSCA.advanced dependencyCount: 25 Package: DEP Version: 1.20.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 MD5sum: 6f0dd158dd403aa298b3c13fb4573b89 NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEP git_branch: RELEASE_3_16 git_last_commit: 6dc7356 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEP_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEP_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEP_1.20.0.tgz vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R suggestsMe: proDA, RforProteomics dependencyCount: 155 Package: DepecheR Version: 1.14.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1) LinkingTo: Rcpp, RcppEigen Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 6e69a75fc559cdaf7d8e7bd2216edb92 NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre], Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: RELEASE_3_16 git_last_commit: 8162d11 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DepecheR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DepecheR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DepecheR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DepecheR_1.14.0.tgz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html vignetteTitles: Example of a cytometry data analysis with DepecheR, Using the groupProbPlot plot function for single-cell probability display hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R suggestsMe: flowSpecs dependencyCount: 82 Package: DepInfeR Version: 1.2.0 Depends: R (>= 4.2.0) Imports: matrixStats, glmnet, stats, BiocParallel Suggests: testthat (>= 3.0.0), knitr, rmarkdown, dplyr, tidyr, tibble, ggplot2, missForest, pheatmap, RColorBrewer, ggrepel, ggbeeswarm License: GPL-3 MD5sum: c2dd33e24fdd4f41912edfae52d1577f NeedsCompilation: no Title: Inferring tumor-specific cancer dependencies through integrating ex-vivo drug response assays and drug-protein profiling Description: DepInfeR integrates two experimentally accessible input data matrices: the drug sensitivity profiles of cancer cell lines or primary tumors ex-vivo (X), and the drug affinities of a set of proteins (Y), to infer a matrix of molecular protein dependencies of the cancers (ß). DepInfeR deconvolutes the protein inhibition effect on the viability phenotype by using regularized multivariate linear regression. It assigns a “dependence coefficient” to each protein and each sample, and therefore could be used to gain a causal and accurate understanding of functional consequences of genomic aberrations in a heterogeneous disease, as well as to guide the choice of pharmacological intervention for a specific cancer type, sub-type, or an individual patient. For more information, please read out preprint on bioRxiv: https://doi.org/10.1101/2022.01.11.475864. biocViews: Software, Regression, Pharmacogenetics, Pharmacogenomics, FunctionalGenomics Author: Junyan Lu [aut, cre] (), Alina Batzilla [aut] Maintainer: Junyan Lu VignetteBuilder: knitr BugReports: https://github.com/Huber-group-EMBL/DepInfeR/issues git_url: https://git.bioconductor.org/packages/DepInfeR git_branch: RELEASE_3_16 git_last_commit: 3047fb7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DepInfeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DepInfeR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DepInfeR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DepInfeR_1.2.0.tgz vignettes: vignettes/DepInfeR/inst/doc/vignette.html vignetteTitles: DepInfeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DepInfeR/inst/doc/vignette.R dependencyCount: 27 Package: DEqMS Version: 1.16.0 Depends: R(>= 3.5),graphics,stats,ggplot2,matrixStats,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,markdown,plyr,reshape2,farms,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: 9764c93e44b7bff35cac5ab2077f6967 NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: RELEASE_3_16 git_last_commit: 26c7cd0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEqMS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEqMS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEqMS_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEqMS_1.16.0.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R dependencyCount: 37 Package: derfinder Version: 1.32.0 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>= 0.23.19), stats, utils Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: faa523304229ab81eb54b497f6a1688d NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology, Coverage Author: Leonardo Collado-Torres [aut, cre] (), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: RELEASE_3_16 git_last_commit: d837971 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/derfinder_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/derfinder_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/derfinder_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/derfinder_1.32.0.tgz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: brainflowprobes, derfinderPlot, ODER, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 153 Package: derfinderHelper Version: 1.32.0 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), RefManageR, rmarkdown (>= 0.3.3), testthat, covr License: Artistic-2.0 MD5sum: bba4cb39cd85594b1c413fe93b74cfaf NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. This package is particularly useful when using BiocParallel and it helps reduce the time spent loading the full derfinder package when running the F-statistics calculation in parallel. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: RELEASE_3_16 git_last_commit: d38b592 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/derfinderHelper_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/derfinderHelper_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/derfinderHelper_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/derfinderHelper_1.32.0.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 12 Package: derfinderPlot Version: 1.32.0 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: c0213295480ab890e7cce412f4a3a5ec NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. This helps separate the graphical dependencies required for making these plots from the core functionality of derfinder. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot git_url: https://git.bioconductor.org/packages/derfinderPlot git_branch: RELEASE_3_16 git_last_commit: 9245bc5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/derfinderPlot_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/derfinderPlot_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/derfinderPlot_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/derfinderPlot_1.32.0.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R importsMe: brainflowprobes, recountWorkflow suggestsMe: derfinder, regionReport, GenomicState dependencyCount: 168 Package: DEScan2 Version: 1.18.2 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 Archs: x64 MD5sum: d8c674a2e22ca2b4b3f7043769b2ff41 NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: RELEASE_3_16 git_last_commit: e0e40a5 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/DEScan2_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEScan2_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/DEScan2_1.18.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEScan2_1.18.2.tgz vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R dependencyCount: 127 Package: DESeq2 Version: 1.38.3 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, matrixStats, methods, stats4, locfit, geneplotter, ggplot2, Rcpp (>= 0.11.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10), glmGamPoi, BiocManager License: LGPL (>= 3) Archs: x64 MD5sum: 32379516b8531b9b0f8c908815b529e5 NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb], RADIANT EU FP7 [fnd], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/mikelove/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: RELEASE_3_16 git_last_commit: c470955 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/DESeq2_1.38.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/DESeq2_1.38.3.zip mac.binary.ver: bin/macosx/contrib/4.2/DESeq2_1.38.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DESeq2_1.38.3.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEWSeq, DEXSeq, metaseqR2, octad, rgsepd, SeqGSEA, TCC, tRanslatome, rnaseqDTU, rnaseqGene, Anaconda, Brundle, DRomics, ordinalbayes importsMe: Anaquin, animalcules, APAlyzer, benchdamic, BRGenomics, CeTF, circRNAprofiler, consensusDE, coseq, countsimQC, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, easier, EBSEA, eegc, ERSSA, GDCRNATools, GeneTonic, Glimma, GRaNIE, hermes, HTSFilter, icetea, ideal, INSPEcT, IntEREst, isomiRs, kissDE, microbiomeExplorer, microbiomeMarker, MLSeq, multiSight, muscat, NBAMSeq, NetActivity, ORFik, OUTRIDER, PathoStat, pcaExplorer, phantasus, POMA, proActiv, RegEnrich, regionReport, ReportingTools, RiboDiPA, Rmmquant, RNASeqR, scBFA, scGPS, SEtools, singleCellTK, SNPhood, srnadiff, systemPipeTools, TBSignatureProfiler, TEKRABber, UMI4Cats, vidger, vulcan, BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper, ExpHunterSuite, recountWorkflow, autoGO, bulkAnalyseR, cinaR, ExpGenetic, ggpicrust2, HeritSeq, HTSSIP, limorhyde2, MetaLonDA, microbial, RNAseqQC, SIPmg, sRNAGenetic, wilson suggestsMe: aggregateBioVar, apeglm, bambu, biobroom, BiocGenerics, BioCor, BiocSet, BioNERO, CAGEr, compcodeR, dearseq, derfinder, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, EWCE, fishpond, gage, GenomicAlignments, GenomicRanges, glmGamPoi, HiCDCPlus, IHW, InteractiveComplexHeatmap, miRmine, NxtIRFcore, OPWeight, PCAtools, phyloseq, progeny, recount, RUVSeq, scran, sparrow, spatialHeatmap, SpliceWiz, subSeq, SummarizedBenchmark, systemPipeR, systemPipeShiny, TFEA.ChIP, tidybulk, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, curatedAdipoChIP, curatedAdipoRNA, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, bakR, cellpypes, conos, FateID, glmmSeq, grandR, lfc, metacoder, metaRNASeq, RaceID, seqgendiff, Seurat, SQMtools dependencyCount: 90 Package: DEsingle Version: 1.18.1 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 3c5cc66ebebb721c89d63f01a0c4785c NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: RELEASE_3_16 git_last_commit: 7e5f975 git_last_commit_date: 2023-01-10 Date/Publication: 2023-01-10 source.ver: src/contrib/DEsingle_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEsingle_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/DEsingle_1.18.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEsingle_1.18.1.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 39 Package: destiny Version: 3.12.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, grid, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: knitr, rmarkdown, igraph, testthat, FNN, tidyverse, gridExtra, cowplot, conflicted, viridis, rgl, scRNAseq, org.Mm.eg.db, scran, repr Enhances: rgl, SingleCellExperiment License: GPL-3 Archs: x64 MD5sum: 2f7c3e52c683d657357fa82fe26fecc9 NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (), Laleh Haghverdi [ctb], Maren Büttner [ctb] (), Fabian Theis [ctb] (), Carsten Marr [ctb] (), Florian Büttner [ctb] () Maintainer: Philipp Angerer URL: https://theislab.github.io/destiny/, https://github.com/theislab/destiny/, https://www.helmholtz-muenchen.de/icb/destiny, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: RELEASE_3_16 git_last_commit: bfeaf9e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/destiny_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/destiny_3.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/destiny_3.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/destiny_3.12.0.tgz vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.html, vignettes/destiny/inst/doc/Diffusion-Maps.html, vignettes/destiny/inst/doc/DPT.html, vignettes/destiny/inst/doc/Gene-Relevance.html, vignettes/destiny/inst/doc/Global-Sigma.html, vignettes/destiny/inst/doc/tidyverse.html vignetteTitles: Reproduce the Diffusion Map vignette with the supplied data(), destiny main vignette: Start here!, destiny 2.0 brought the Diffusion Pseudo Time (DPT) class, detecting relevant genes with destiny 3, The effects of a global vs. local kernel, tidyverse and ggplot integration with destiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/destiny/inst/doc/Diffusion-Map-recap.R, vignettes/destiny/inst/doc/Diffusion-Maps.R, vignettes/destiny/inst/doc/DPT.R, vignettes/destiny/inst/doc/Gene-Relevance.R, vignettes/destiny/inst/doc/Global-Sigma.R, vignettes/destiny/inst/doc/tidyverse.R importsMe: phemd suggestsMe: CelliD, CellTrails, monocle dependencyCount: 124 Package: DEsubs Version: 1.24.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL-3 MD5sum: d20fca4a4429eb812e661ddbd780a502 NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: RELEASE_3_16 git_last_commit: 4ff00fc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEsubs_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEsubs_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEsubs_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEsubs_1.24.0.tgz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R dependencyCount: 128 Package: DEWSeq Version: 1.12.0 Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: 7eef7adc0d6eed7e109664578f0a7c53 NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics team Hentze [aut, cre] Maintainer: bioinformatics team Hentze URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: RELEASE_3_16 git_last_commit: e324b51 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEWSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEWSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEWSeq_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEWSeq_1.12.0.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 95 Package: DExMA Version: 1.6.0 Depends: R (>= 4.1), DExMAdata Imports: Biobase, GEOquery, impute, limma, pheatmap, plyr, scales, snpStats, sva, swamp, stats, methods, utils, bnstruct, RColorBrewer, grDevices Suggests: BiocStyle, qpdf, BiocGenerics, RUnit License: GPL-2 MD5sum: 8463b6f8020f8b47f21d2b8d0519b70a NeedsCompilation: no Title: Differential Expression Meta-Analysis Description: performing all the steps of gene expression meta-analysis considering the possible existence of missing genes. It provides the necessary functions to be able to perform the different methods of gene expression meta-analysis. In addition, it contains functions to apply quality controls, download GEO datasets and show graphical representations of the results. biocViews: DifferentialExpression, GeneExpression, StatisticalMethod, QualityControl Author: Juan Antonio Villatoro-García [aut, cre], Pedro Carmona-Sáez [aut] Maintainer: Juan Antonio Villatoro-García git_url: https://git.bioconductor.org/packages/DExMA git_branch: RELEASE_3_16 git_last_commit: e54fbab git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DExMA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DExMA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DExMA_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DExMA_1.6.0.tgz vignettes: vignettes/DExMA/inst/doc/DExMA.pdf vignetteTitles: Differential Expression Meta-Analysis with DExMA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DExMA/inst/doc/DExMA.R dependencyCount: 120 Package: DEXSeq Version: 1.44.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) MD5sum: b18982d6f7bd08e3302900292b9f674c NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: RELEASE_3_16 git_last_commit: 9660d73 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DEXSeq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DEXSeq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DEXSeq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DEXSeq_1.44.0.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html vignetteTitles: Inferring differential exon usage in RNA-Seq data with the DEXSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R dependsOnMe: IsoformSwitchAnalyzeR, pasilla, rnaseqDTU importsMe: diffUTR, IntEREst suggestsMe: bambu, GenomicRanges, satuRn, stageR, subSeq, BioPlex dependencyCount: 114 Package: DFP Version: 1.56.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: d6b76195d7950f5e9575d7595d85cb77 NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: RELEASE_3_16 git_last_commit: ab6a5ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DFP_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DFP_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DFP_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DFP_1.56.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 6 Package: DIAlignR Version: 2.6.0 Depends: methods, stats, R (>= 4.0) Imports: zoo (>= 1.8-3), data.table, magrittr, dplyr, tidyr, rlang, mzR (>= 2.18), signal, bit64, reticulate, ggplot2, RSQLite, DBI, ape, phangorn, pracma, RMSNumpress, Rcpp LinkingTo: Rcpp, RcppEigen Suggests: knitr, akima, lattice, scales, gridExtra, latticeExtra, rmarkdown, BiocStyle, BiocParallel, testthat (>= 2.1.0) License: GPL-3 Archs: x64 MD5sum: 64c32812c3ee3739d1224ded108e6413 NeedsCompilation: yes Title: Dynamic Programming Based Alignment of MS2 Chromatograms Description: To obtain unbiased proteome coverage from a biological sample, mass-spectrometer is operated in Data Independent Acquisition (DIA) mode. Alignment of these DIA runs establishes consistency and less missing values in complete data-matrix. This package implements dynamic programming with affine gap penalty based approach for pair-wise alignment of analytes. A hybrid approach of global alignment (through MS2 features) and local alignment (with MS2 chromatograms) is implemented in this tool. biocViews: MassSpectrometry, Metabolomics, Proteomics, Alignment, Software Author: Shubham Gupta [aut, cre] (), Hannes Rost [aut] (), Justin Sing [aut] Maintainer: Shubham Gupta SystemRequirements: C++14 VignetteBuilder: knitr BugReports: https://github.com/shubham1637/DIAlignR/issues git_url: https://git.bioconductor.org/packages/DIAlignR git_branch: RELEASE_3_16 git_last_commit: 96343cb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DIAlignR_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DIAlignR_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DIAlignR_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DIAlignR_2.6.0.tgz vignettes: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.html vignetteTitles: MS2 chromatograms based alignment of targeted mass-spectrometry runs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.R dependencyCount: 80 Package: DiffBind Version: 3.8.4 Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 2.13.1), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.99.1), Rcpp Suggests: BiocStyle, testthat, xtable Enhances: rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb, profileplyr, rtracklayer, grid License: Artistic-2.0 Archs: x64 MD5sum: 537d2734861f3cfde272740f49686ac8 NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq,ATACSeq, DNaseSeq, MethylSeq, RIPSeq, DifferentialPeakCalling, DifferentialMethylation, GeneRegulation, HistoneModification, PeakDetection, BiomedicalInformatics, CellBiology, MultipleComparison, Normalization, ReportWriting, Epigenetics, FunctionalGenomics Author: Rory Stark [aut, cre], Gord Brown [aut] Maintainer: Rory Stark URL: https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/DiffBind SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/DiffBind git_branch: RELEASE_3_16 git_last_commit: 846ee10 git_last_commit_date: 2023-01-12 Date/Publication: 2023-01-12 source.ver: src/contrib/DiffBind_3.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/DiffBind_3.8.4.zip mac.binary.ver: bin/macosx/contrib/4.2/DiffBind_3.8.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DiffBind_3.8.4.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC, vulcan, Brundle dependencyCount: 154 Package: diffcoexp Version: 1.18.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) MD5sum: 78d716ed9922ad37828872406c5d393a NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: RELEASE_3_16 git_last_commit: af33fef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diffcoexp_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffcoexp_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffcoexp_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diffcoexp_1.18.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R importsMe: ExpHunterSuite, easyDifferentialGeneCoexpression dependencyCount: 127 Package: diffcyt Version: 1.18.0 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE MD5sum: 24de43c96ac3fdecd877ca028e0c5fc6 NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: RELEASE_3_16 git_last_commit: 35290a0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diffcyt_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffcyt_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffcyt_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diffcyt_1.18.0.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R dependsOnMe: censcyt, cytofWorkflow importsMe: CyTOFpower, treekoR suggestsMe: CATALYST dependencyCount: 146 Package: DifferentialRegulation Version: 1.2.2 Depends: R (>= 4.2.0) Imports: methods, Rcpp, doRNG, MASS, data.table, doParallel, parallel, foreach, stats, BANDITS, Matrix, SingleCellExperiment, SummarizedExperiment, ggplot2 LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: a60791bd49cfc2f26f54d6de3bd6a988 NeedsCompilation: yes Title: Differentially regulated genes from scRNA-seq data Description: DifferentialRegulation is a method for detecting differentially regulated genes between two groups of samples (e.g., healthy vs. disease, or treated vs. untreated samples), by targeting differences in the balance of spliced and unspliced mRNA abundances, obtained from single-cell RNA-sequencing (scRNA-seq) data. DifferentialRegulation accounts for the sample-to-sample variability, and embeds multiple samples in a Bayesian hierarchical model. In particular, when reads are compatible with multiple genes or multiple splicing versions of a gene (unspliced spliced or ambiguous), the method allocates these multi-mapping reads to the gene of origin and their splicing version. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques (Metropolis-within-Gibbs). biocViews: DifferentialSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, GeneTarget Author: Simone Tiberi [aut, cre] () Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/DifferentialRegulation SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/DifferentialRegulation/issues git_url: https://git.bioconductor.org/packages/DifferentialRegulation git_branch: RELEASE_3_16 git_last_commit: 0ae1b53 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/DifferentialRegulation_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/DifferentialRegulation_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/DifferentialRegulation_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DifferentialRegulation_1.2.2.tgz vignettes: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.html vignetteTitles: DifferentialRegulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DifferentialRegulation/inst/doc/DifferentialRegulation.R dependencyCount: 84 Package: diffGeneAnalysis Version: 1.80.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 9320cdacf623ed2c00860bfc7cecb4ec NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: RELEASE_3_16 git_last_commit: 2793355 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diffGeneAnalysis_1.80.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffGeneAnalysis_1.80.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffGeneAnalysis_1.80.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diffGeneAnalysis_1.80.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.30.0 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), zlibbioc, Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 Archs: x64 MD5sum: b9b86e15e59038edb1ab1c879aa8dab6 NeedsCompilation: yes Title: Differential Analyis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: RELEASE_3_16 git_last_commit: c42accb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diffHic_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffHic_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffHic_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diffHic_1.30.0.tgz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 57 Package: DiffLogo Version: 2.22.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: e2367dc8dff7b85a5d1456f86081294b NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: RELEASE_3_16 git_last_commit: d3ae281 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DiffLogo_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DiffLogo_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DiffLogo_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DiffLogo_2.22.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffuStats Version: 1.18.0 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils License: GPL-3 Archs: x64 MD5sum: 99deeec6c2c90480bb2a6bce8e4b059a NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics, Transcriptomics, Proteomics, Genetics, GenomeWideAssociation, Normalization Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: RELEASE_3_16 git_last_commit: 2909fc2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diffuStats_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffuStats_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffuStats_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diffuStats_1.18.0.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 49 Package: diffUTR Version: 1.6.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, limma, edgeR, DEXSeq, GenomicRanges, Rsubread, ggplot2, rtracklayer, ComplexHeatmap, ggrepel, stringi, methods, stats, GenomeInfoDb, dplyr, matrixStats, IRanges, ensembldb, viridisLite Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: d389e262756575825cb3fd1e87f7da23 NeedsCompilation: no Title: diffUTR: Streamlining differential exon and 3' UTR usage Description: The diffUTR package provides a uniform interface and plotting functions for limma/edgeR/DEXSeq -powered differential bin/exon usage. It includes in addition an improved version of the limma::diffSplice method. Most importantly, diffUTR further extends the application of these frameworks to differential UTR usage analysis using poly-A site databases. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] (), Stefan Gerber [aut] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/ETHZ-INS/diffUTR git_url: https://git.bioconductor.org/packages/diffUTR git_branch: RELEASE_3_16 git_last_commit: a27bd74 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diffUTR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diffUTR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diffUTR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diffUTR_1.6.0.tgz vignettes: vignettes/diffUTR/inst/doc/diffSplice2.html, vignettes/diffUTR/inst/doc/diffUTR.html vignetteTitles: diffUTR_diffSplice2, 1_diffUTR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffUTR/inst/doc/diffSplice2.R, vignettes/diffUTR/inst/doc/diffUTR.R dependencyCount: 140 Package: diggit Version: 1.30.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: e22c732b2ce65822d5030e772df924f4 NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: RELEASE_3_16 git_last_commit: 3f95e15 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/diggit_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/diggit_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/diggit_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/diggit_1.30.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 100 Package: Dino Version: 1.4.0 Depends: R (>= 4.0.0) Imports: BiocParallel, BiocSingular, SummarizedExperiment, SingleCellExperiment, S4Vectors, Matrix, Seurat, matrixStats, parallel, scran, grDevices, stats, methods Suggests: testthat (>= 2.1.0), knitr, rmarkdown, BiocStyle, devtools, ggplot2, gridExtra, ggpubr, grid, magick, hexbin License: GPL-3 MD5sum: 66671d63510b7ea7ef08ce31f006c4b0 NeedsCompilation: no Title: Normalization of Single-Cell mRNA Sequencing Data Description: Dino normalizes single-cell, mRNA sequencing data to correct for technical variation, particularly sequencing depth, prior to downstream analysis. The approach produces a matrix of corrected expression for which the dependency between sequencing depth and the full distribution of normalized expression; many existing methods aim to remove only the dependency between sequencing depth and the mean of the normalized expression. This is particuarly useful in the context of highly sparse datasets such as those produced by 10X genomics and other uninque molecular identifier (UMI) based microfluidics protocols for which the depth-dependent proportion of zeros in the raw expression data can otherwise present a challenge. biocViews: Software, Normalization, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Regression, CellBasedAssays Author: Jared Brown [aut, cre] (), Christina Kendziorski [ctb] Maintainer: Jared Brown URL: https://github.com/JBrownBiostat/Dino VignetteBuilder: knitr BugReports: https://github.com/JBrownBiostat/Dino/issues git_url: https://git.bioconductor.org/packages/Dino git_branch: RELEASE_3_16 git_last_commit: d36593c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Dino_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Dino_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Dino_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Dino_1.4.0.tgz vignettes: vignettes/Dino/inst/doc/Dino.html vignetteTitles: Normalization by distributional resampling of high throughput single-cell RNA-sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Dino/inst/doc/Dino.R dependencyCount: 187 Package: dir.expiry Version: 1.6.0 Imports: utils, filelock Suggests: rmarkdown, knitr, testthat, BiocStyle License: GPL-3 MD5sum: 8b0a798316a94942d17161f2ca4b6e99 NeedsCompilation: no Title: Managing Expiration for Cache Directories Description: Implements an expiration system for access to versioned directories. Directories that have not been accessed by a registered function within a certain time frame are deleted. This aims to reduce disk usage by eliminating obsolete caches generated by old versions of packages. biocViews: Software, Infrastructure Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dir.expiry git_branch: RELEASE_3_16 git_last_commit: 67b0702 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dir.expiry_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dir.expiry_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dir.expiry_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dir.expiry_1.6.0.tgz vignettes: vignettes/dir.expiry/inst/doc/userguide.html vignetteTitles: Managing directory expiration hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dir.expiry/inst/doc/userguide.R importsMe: basilisk, basilisk.utils, rebook dependencyCount: 2 Package: Director Version: 1.24.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: 0d4cae3747a23c79ea450c290edf75c3 NeedsCompilation: no Title: A dynamic visualization tool of multi-level data Description: Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data. biocViews: Visualization Author: Katherine Icay [aut, cre] Maintainer: Katherine Icay URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues git_url: https://git.bioconductor.org/packages/Director git_branch: RELEASE_3_16 git_last_commit: 8332a98 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Director_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Director_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Director_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Director_1.24.0.tgz vignettes: vignettes/Director/inst/doc/vignette.pdf vignetteTitles: Using Director hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Director/inst/doc/vignette.R dependencyCount: 8 Package: DirichletMultinomial Version: 1.40.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: x64 MD5sum: 2aef6ee52884858b31172b63d0930cfe NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: RELEASE_3_16 git_last_commit: 200176f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DirichletMultinomial_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DirichletMultinomial_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DirichletMultinomial_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DirichletMultinomial_1.40.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: mia, miaViz, TFBSTools suggestsMe: MicrobiotaProcess dependencyCount: 8 Package: discordant Version: 1.22.0 Depends: R (>= 4.1.0) Imports: Rcpp, Biobase, stats, biwt, gtools, MASS, tools, dplyr, methods, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 6df3ea65dc1532d8541e81e6c7bf55fd NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is an R package that identifies pairs of features that correlate differently between phenotypic groups, with application to -omics data sets. Discordant uses a mixture model that “bins” molecular feature pairs based on their type of coexpression or coabbundance. Algorithm is explained further in "Differential Correlation for Sequencing Data"" (Siska et al. 2016). biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [aut], McGrath Max [aut, cre], Katerina Kechris [aut, cph, ths] Maintainer: McGrath Max URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_16 git_last_commit: 2cf63ca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/discordant_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/discordant_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/discordant_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/discordant_1.22.0.tgz vignettes: vignettes/discordant/inst/doc/Using_discordant.html vignetteTitles: The discordant R Package: A Novel Approach to Differential Correlation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Using_discordant.R dependencyCount: 30 Package: DiscoRhythm Version: 1.14.0 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 MD5sum: 1b7c9a6697e18dd9a924a26647265153 NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: RELEASE_3_16 git_last_commit: 9cd5a0d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DiscoRhythm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DiscoRhythm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DiscoRhythm_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DiscoRhythm_1.14.0.tgz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 158 Package: distinct Version: 1.10.2 Depends: R (>= 4.0) Imports: Rcpp, Rfast, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo, Rfast Suggests: knitr, rmarkdown, testthat, UpSetR License: GPL (>= 3) Archs: x64 MD5sum: 41db57ea276847d4181d4601d0efed49 NeedsCompilation: yes Title: distinct: a method for differential analyses via hierarchical permutation tests Description: distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group. biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, FlowCytometry, GeneTarget Author: Simone Tiberi [aut, cre]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: RELEASE_3_16 git_last_commit: b6b514e git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/distinct_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/distinct_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/distinct_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/distinct_1.10.2.tgz vignettes: vignettes/distinct/inst/doc/distinct.html vignetteTitles: distinct: a method for differential analyses via hierarchical permutation tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/distinct/inst/doc/distinct.R importsMe: condiments suggestsMe: spatialHeatmap dependencyCount: 108 Package: dittoSeq Version: 1.10.0 Depends: ggplot2 Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2, pheatmap, grDevices, ggrepel, ggridges, stats, utils, SummarizedExperiment, SingleCellExperiment, S4Vectors Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, edgeR, ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq, ggrastr (>= 0.2.0), ComplexHeatmap, bluster, scater, scran License: MIT + file LICENSE MD5sum: edf7aed33d56c9592a8ae6c09b43acdb NeedsCompilation: no Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization Description: A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression, Transcriptomics, DataImport Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb] Maintainer: Daniel Bunis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: RELEASE_3_16 git_last_commit: eaa7948 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dittoSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dittoSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dittoSeq_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dittoSeq_1.10.0.tgz vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R importsMe: SPIAT suggestsMe: escape, tidySingleCellExperiment, magmaR, scCustomize dependencyCount: 63 Package: divergence Version: 1.14.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: b27d40897e1210656095c96a388f8f45 NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: RELEASE_3_16 git_last_commit: 6ed83c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/divergence_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/divergence_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/divergence_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/divergence_1.14.0.tgz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 25 Package: dks Version: 1.44.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: d93ca0629aabccf5f42958777ba341c9 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: RELEASE_3_16 git_last_commit: 96c0528 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dks_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dks_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dks_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dks_1.44.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.12.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 4c76de9be414900aadbead2a0f42c306 NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: RELEASE_3_16 git_last_commit: 1c24503 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DMCFB_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMCFB_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMCFB_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DMCFB_1.12.0.tgz vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 108 Package: DMCHMM Version: 1.20.0 Depends: R (>= 4.1.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: b9834c4cf269d8dcb74790e4e8dbf13e NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. DNA methylation studies have enabled researchers to understand methylation patterns and their regulatory roles in biological processes and disease. However, only a limited number of statistical approaches have been developed to provide formal quantitative analysis. Specifically, a few available methods do identify differentially methylated CpG (DMC) sites or regions (DMR), but they suffer from limitations that arise mostly due to challenges inherent in bisulfite sequencing data. These challenges include: (1) that read-depths vary considerably among genomic positions and are often low; (2) both methylation and autocorrelation patterns change as regions change; and (3) CpG sites are distributed unevenly. Furthermore, there are several methodological limitations: almost none of these tools is capable of comparing multiple groups and/or working with missing values, and only a few allow continuous or multiple covariates. The last of these is of great interest among researchers, as the goal is often to find which regions of the genome are associated with several exposures and traits. To tackle these issues, we have developed an efficient DMC identification method based on Hidden Markov Models (HMMs) called “DMCHMM” which is a three-step approach (model selection, prediction, testing) aiming to address the aforementioned drawbacks. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: RELEASE_3_16 git_last_commit: 336dac0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DMCHMM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMCHMM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMCHMM_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DMCHMM_1.20.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: DMCHMM: Differentially Methylated CpG using Hidden Markov Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 56 Package: DMRcaller Version: 1.30.0 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10) Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 5b51c7d79dafa39c1d841eaa21e144be NeedsCompilation: no Title: Differentially Methylated Regions caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: RELEASE_3_16 git_last_commit: 5215cdb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DMRcaller_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRcaller_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRcaller_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DMRcaller_1.30.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 30 Package: DMRcate Version: 2.12.0 Depends: R (>= 4.0.0) Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS, minfi, missMethyl, GenomicRanges, plyr, Gviz, IRanges, stats, utils, S4Vectors, methods, graphics, SummarizedExperiment Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, FlowSorted.Blood.EPIC, tissueTreg, DMRcatedata License: file LICENSE MD5sum: fcc418be3d20a5d98ada6656124573fe NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: RELEASE_3_16 git_last_commit: 560dd50 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DMRcate_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRcate_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRcate_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DMRcate_2.12.0.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 224 Package: DMRforPairs Version: 1.34.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 3ad83c7ea0408bda0133d22d96c190ca NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ git_url: https://git.bioconductor.org/packages/DMRforPairs git_branch: RELEASE_3_16 git_last_commit: 31b89f6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DMRforPairs_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRforPairs_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRforPairs_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DMRforPairs_1.34.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R dependencyCount: 154 Package: DMRScan Version: 1.20.0 Depends: R (>= 3.6.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb, methods, mvtnorm, stats, parallel Suggests: knitr, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: 7b65d7c47a659b314a0491bd008e8d34 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: RELEASE_3_16 git_last_commit: b1ff7fb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DMRScan_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DMRScan_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DMRScan_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DMRScan_1.20.0.tgz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html vignetteTitles: DMR Scan Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R dependencyCount: 25 Package: dmrseq Version: 1.18.1 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, GenomeInfoDb, splines Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: c62f42c90821a15765f5ff88424f55a7 NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (), Rafael Irizarry [aut] (), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: RELEASE_3_16 git_last_commit: ea7a1c9 git_last_commit_date: 2023-03-29 Date/Publication: 2023-03-31 source.ver: src/contrib/dmrseq_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/dmrseq_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/dmrseq_1.18.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dmrseq_1.18.1.tgz vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R importsMe: biscuiteer dependencyCount: 171 Package: DNABarcodeCompatibility Version: 1.14.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 1027a9cb369deb4a38828f51fc97632e NeedsCompilation: no Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (), Jacques Boutet de Monvel [aut] (), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] () Maintainer: Céline Trébeau VignetteBuilder: knitr BugReports: https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: RELEASE_3_16 git_last_commit: cdb8ccb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DNABarcodeCompatibility_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNABarcodeCompatibility_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNABarcodeCompatibility_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DNABarcodeCompatibility_1.14.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 35 Package: DNABarcodes Version: 1.28.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: x64 MD5sum: 7a7fb3d4f03a9eaa574a81d96e3e256a NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: RELEASE_3_16 git_last_commit: 4232ade git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DNABarcodes_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNABarcodes_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNABarcodes_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DNABarcodes_1.28.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R importsMe: DNABarcodeCompatibility dependencyCount: 11 Package: DNAcopy Version: 1.72.3 License: GPL (>= 2) Archs: x64 MD5sum: aa91303bfd470ab11bf34b06259d7288 NeedsCompilation: yes Title: DNA copy number data analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: RELEASE_3_16 git_last_commit: 29b7915 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/DNAcopy_1.72.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNAcopy_1.72.3.zip mac.binary.ver: bin/macosx/contrib/4.2/DNAcopy_1.72.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DNAcopy_1.72.3.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, maftools, MDTS, MEDIPS, MethCP, MinimumDistance, QDNAseq, Repitools, SCOPE, snapCGH, jointseg, PSCBS suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, nullranges, sesame, ACNE, aroma.cn, aroma.core, bcp, calmate dependencyCount: 0 Package: DNAfusion Version: 1.0.0 Depends: R (>= 4.2.0) Imports: bamsignals, GenomicRanges, IRanges, Rsamtools, GenomicAlignments, BiocBaseUtils, S4Vectors Suggests: knitr, rmarkdown, testthat, sessioninfo, BiocStyle, License: GPL-3 MD5sum: 2a902d457521f1ebf8b2ca36163dd2de NeedsCompilation: no Title: Identification of gene fusions using paired-end sequencing Description: Paired-end sequencing of cfDNA generated BAM files can be used as input to discover EML4-ALK variants. This package was developed using position deduplicated BAM files generated with the AVENIO Oncology Analysis Software. These files are made using the AVENIO ctDNA surveillance kit and Illumina Nextseq 500 sequencing. This is a targeted hybridization NGS approach and includes ALK-specific but not EML4-specific probes. biocViews: TargetedResequencing, Genetics, GeneFusionDetection, Sequencing Author: Christoffer Trier Maansson [aut, cre] (), Emma Roger Andersen [ctb, rev], Maiken Parm Ulhoi [dtc], Peter Meldgaard [dtc], Boe Sandahl Sorensen [rev, fnd] Maintainer: Christoffer Trier Maansson URL: https://github.com/CTrierMaansson/DNAfusion VignetteBuilder: knitr BugReports: https://github.com/CTrierMaansson/DNAfusion/issues git_url: https://git.bioconductor.org/packages/DNAfusion git_branch: RELEASE_3_16 git_last_commit: 163dcf2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DNAfusion_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNAfusion_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNAfusion_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DNAfusion_1.0.0.tgz vignettes: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.html vignetteTitles: Introduction to DNAfusion hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAfusion/inst/doc/Introduction_to_DNAfusion.R dependencyCount: 43 Package: DNAshapeR Version: 1.26.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 Archs: x64 MD5sum: 44687dd0152a4bfeb6d072ede8a9a217 NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: RELEASE_3_16 git_last_commit: f0f4eb1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DNAshapeR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DNAshapeR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DNAshapeR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DNAshapeR_1.26.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 56 Package: DominoEffect Version: 1.18.0 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: b1da1141e9d2bd2f763c6297bce3f6e7 NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: RELEASE_3_16 git_last_commit: 36be327 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DominoEffect_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DominoEffect_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DominoEffect_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DominoEffect_1.18.0.tgz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 99 Package: doppelgangR Version: 1.26.0 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat License: GPL (>=2.0) MD5sum: 959dd9827864db89ef05d4acd526ad48 NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: RELEASE_3_16 git_last_commit: c899a8b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/doppelgangR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/doppelgangR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/doppelgangR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/doppelgangR_1.26.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 79 Package: Doscheda Version: 1.20.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: feed2c10c258627119453f847e763272 NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: RELEASE_3_16 git_last_commit: aa90674 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Doscheda_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Doscheda_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Doscheda_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Doscheda_1.20.0.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 158 Package: DOSE Version: 3.24.2 Depends: R (>= 3.5.0) Imports: AnnotationDbi, HDO.db, BiocParallel, fgsea, ggplot2, GOSemSim (>= 2.23.1), methods, qvalue, reshape2, stats, utils Suggests: prettydoc, clusterProfiler, gson (>= 0.0.5), knitr, rmarkdown, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: 391363330b411ea31dd262e04df50906 NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb], Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: RELEASE_3_16 git_last_commit: 2d3de40 git_last_commit_date: 2022-11-20 Date/Publication: 2022-11-21 source.ver: src/contrib/DOSE_3.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/DOSE_3.24.2.zip mac.binary.ver: bin/macosx/contrib/4.2/DOSE_3.24.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DOSE_3.24.2.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html vignetteTitles: DOSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: bioCancer, clusterProfiler, debrowser, eegc, enrichplot, GDCRNATools, MAGeCKFlute, meshes, miRspongeR, MoonlightR, pareg, Pigengene, ReactomePA, RegEnrich, RNASeqR, scTensor, signatureSearch, ExpHunterSuite, GseaVis, immcp suggestsMe: cola, GOSemSim, GRaNIE, rrvgo, scGPS, simplifyEnrichment dependencyCount: 91 Package: doseR Version: 1.14.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: ec35228d655a89d682235837377a65dd NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: RELEASE_3_16 git_last_commit: 28a024c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/doseR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/doseR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/doseR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/doseR_1.14.0.tgz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 71 Package: dpeak Version: 1.10.0 Depends: R (>= 4.0.0), methods, stats, utils, graphics, Rcpp Imports: MASS, IRanges, BSgenome, grDevices, parallel LinkingTo: Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805 License: GPL (>= 2) Archs: x64 MD5sum: 5c0ae9cedec6e3cda0b4b772542d7bcc NeedsCompilation: yes Title: dPeak (Deconvolution of Peaks in ChIP-seq Analysis) Description: dPeak is a statistical framework for the high resolution identification of protein-DNA interaction sites using PET and SET ChIP-Seq and ChIP-exo data. It provides computationally efficient and user friendly interface to process ChIP-seq and ChIP-exo data, implement exploratory analysis, fit dPeak model, and export list of predicted binding sites for downstream analysis. biocViews: ChIPSeq, Genetics, Sequencing, Software, Transcription Author: Dongjun Chung, Carter Allen Maintainer: Dongjun Chung SystemRequirements: GNU make, meme, fimo BugReports: https://github.com/dongjunchung/dpeak/issues git_url: https://git.bioconductor.org/packages/dpeak git_branch: RELEASE_3_16 git_last_commit: 602b8c4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dpeak_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dpeak_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dpeak_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dpeak_1.10.0.tgz vignettes: vignettes/dpeak/inst/doc/dpeak-example.pdf vignetteTitles: dPeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dpeak/inst/doc/dpeak-example.R dependencyCount: 49 Package: drawProteins Version: 1.18.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 0f7c383d5df708ec3512eea75b68bb51 NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: RELEASE_3_16 git_last_commit: a2360c2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/drawProteins_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/drawProteins_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/drawProteins_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/drawProteins_1.18.0.tgz vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using extract_transcripts in drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R importsMe: factR dependencyCount: 61 Package: DRIMSeq Version: 1.26.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: b0e7a537d5ba56b9ab59319bd2be27a9 NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: RELEASE_3_16 git_last_commit: 69cf942 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DRIMSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DRIMSeq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DRIMSeq_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DRIMSeq_1.26.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R dependsOnMe: rnaseqDTU importsMe: BANDITS, IsoformSwitchAnalyzeR dependencyCount: 65 Package: DriverNet Version: 1.38.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 24bd5a77d291fdb8b80c5f803f029723 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: RELEASE_3_16 git_last_commit: e030831 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DriverNet_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DriverNet_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DriverNet_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DriverNet_1.38.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: DropletUtils Version: 1.18.1 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiocParallel, DelayedArray, DelayedMatrixStats, HDF5Array, rhdf5, edgeR, R.utils, dqrng, beachmat, scuttle LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng, scuttle Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite, DropletTestFiles License: GPL-3 Archs: x64 MD5sum: 8b6dd84250172d53cfbb6636141d878a NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut], Jonathan Griffiths [ctb, cre], Davis McCarthy [ctb], Dongze He [ctb], Rob Patro [ctb] Maintainer: Jonathan Griffiths SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_16 git_last_commit: ff37775 git_last_commit_date: 2022-11-22 Date/Publication: 2022-11-22 source.ver: src/contrib/DropletUtils_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/DropletUtils_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/DropletUtils_1.18.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DropletUtils_1.18.1.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R dependsOnMe: OSCA.advanced, OSCA.intro, OSCA.multisample, OSCA.workflows importsMe: FLAMES, scCB2, scPipe, singleCellTK, Spaniel, SpatialExperiment suggestsMe: demuxmix, mumosa, Nebulosa, SpatialFeatureExperiment, DropletTestFiles, muscData, scCustomize, SoupX dependencyCount: 53 Package: drugTargetInteractions Version: 1.6.0 Depends: methods, R (>= 4.1) Imports: utils, RSQLite, UniProt.ws, biomaRt,ensembldb, BiocFileCache,dplyr,rappdirs, AnnotationFilter, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, ggplot2, reshape2, DT, EnsDb.Hsapiens.v86 License: Artistic-2.0 MD5sum: eb80ddd832c35d54f7ef7043992ebe86 NeedsCompilation: no Title: Drug-Target Interactions Description: Provides utilities for identifying drug-target interactions for sets of small molecule or gene/protein identifiers. The required drug-target interaction information is obained from a local SQLite instance of the ChEMBL database. ChEMBL has been chosen for this purpose, because it provides one of the most comprehensive and best annotatated knowledge resources for drug-target information available in the public domain. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, Proteomics, Metabolomics Author: Thomas Girke [cre, aut] Maintainer: Thomas Girke URL: https://github.com/girke-lab/drugTargetInteractions VignetteBuilder: knitr BugReports: https://github.com/girke-lab/drugTargetInteractions git_url: https://git.bioconductor.org/packages/drugTargetInteractions git_branch: RELEASE_3_16 git_last_commit: e975994 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/drugTargetInteractions_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/drugTargetInteractions_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/drugTargetInteractions_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/drugTargetInteractions_1.6.0.tgz vignettes: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.html vignetteTitles: Drug-Target Interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/drugTargetInteractions/inst/doc/drugTargetInteractions.R dependencyCount: 104 Package: DrugVsDisease Version: 2.40.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: babca2ebe58102cc744de3a80f95d137 NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: RELEASE_3_16 git_last_commit: 0bbcf6a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DrugVsDisease_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DrugVsDisease_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DrugVsDisease_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DrugVsDisease_2.40.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 127 Package: DSS Version: 2.46.0 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel, bsseq, parallel Imports: utils, graphics, stats, splines Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: x64 MD5sum: cfddb2dffe867e04864458103ddc4c4a NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu, Hao Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: RELEASE_3_16 git_last_commit: debfbac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DSS_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DSS_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DSS_2.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DSS_2.46.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.html vignetteTitles: The DSS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R dependsOnMe: DeMixT importsMe: borealis, DMRcate, kissDE, metaseqR2, MethCP, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 77 Package: dStruct Version: 1.4.0 Depends: R (>= 4.1) Imports: zoo, ggplot2, purrr, reshape2, parallel, IRanges, S4Vectors, rlang, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 4f42240b3ddd43ddedb8e69401ebd174 NeedsCompilation: no Title: Identifying differentially reactive regions from RNA structurome profiling data Description: dStruct identifies differentially reactive regions from RNA structurome profiling data. dStruct is compatible with a broad range of structurome profiling technologies, e.g., SHAPE-MaP, DMS-MaPseq, Structure-Seq, SHAPE-Seq, etc. See Choudhary et al, Genome Biology, 2019 for the underlying method. biocViews: StatisticalMethod, StructuralPrediction, Sequencing, Software Author: Krishna Choudhary [aut, cre] (), Sharon Aviran [aut] () Maintainer: Krishna Choudhary URL: https://github.com/dataMaster-Kris/dStruct VignetteBuilder: knitr BugReports: https://github.com/dataMaster-Kris/dStruct/issues git_url: https://git.bioconductor.org/packages/dStruct git_branch: RELEASE_3_16 git_last_commit: d240736 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dStruct_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dStruct_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dStruct_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dStruct_1.4.0.tgz vignettes: vignettes/dStruct/inst/doc/dStruct.html vignetteTitles: Differential RNA structurome analysis using `dStruct` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dStruct/inst/doc/dStruct.R dependencyCount: 48 Package: DTA Version: 2.44.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 5d3d9632d873e5f491d36556611da276 NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: RELEASE_3_16 git_last_commit: 26564cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DTA_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DTA_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DTA_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DTA_2.44.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R dependencyCount: 5 Package: Dune Version: 1.10.0 Depends: R (>= 3.6) Imports: BiocParallel, SummarizedExperiment, utils, ggplot2, dplyr, tidyr, RColorBrewer, magrittr, gganimate, purrr, aricode Suggests: knitr, rmarkdown, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 2b6f8892515c68f0858b9ad9ed793bd3 NeedsCompilation: no Title: Improving replicability in single-cell RNA-Seq cell type discovery Description: Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability. biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, cre] (), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: RELEASE_3_16 git_last_commit: b23531c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Dune_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Dune_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Dune_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Dune_1.10.0.tgz vignettes: vignettes/Dune/inst/doc/Dune.html vignetteTitles: Dune Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Dune/inst/doc/Dune.R dependencyCount: 77 Package: dupRadar Version: 1.28.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1), KernSmooth Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: ba6eac4a78d4ce96708daba439896483 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein URL: https://www.bioconductor.org/packages/dupRadar, https://ssayols.github.io/dupRadar/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: RELEASE_3_16 git_last_commit: 4e8be81 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dupRadar_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/dupRadar_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dupRadar_1.28.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 10 Package: dyebias Version: 1.58.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: ebed952e1115f00b803eea21caf15aeb NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: RELEASE_3_16 git_last_commit: 9f40c43 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/dyebias_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/dyebias_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/dyebias_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/dyebias_1.58.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R suggestsMe: dyebiasexamples dependencyCount: 9 Package: DynDoc Version: 1.76.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: f7d5391c485c725b42c217246de82443 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: RELEASE_3_16 git_last_commit: 98230b2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/DynDoc_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/DynDoc_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/DynDoc_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/DynDoc_1.76.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: easier Version: 1.4.0 Depends: R (>= 4.2.0) Imports: progeny, easierData, dorothea (>= 1.6.0), quantiseqr, ROCR, grDevices, stats, graphics, ggplot2, ggpubr, DESeq2, utils, dplyr, matrixStats, rlang, BiocParallel, reshape2, rstatix, ggrepel, coin Suggests: knitr, rmarkdown, BiocStyle, testthat, SummarizedExperiment License: MIT + file LICENSE MD5sum: 27caa6b42f2c3e59e83c02f0707ab2e0 NeedsCompilation: no Title: Estimate Systems Immune Response from RNA-seq data Description: This package provides a workflow for the use of EaSIeR tool, developed to assess patients' likelihood to respond to ICB therapies providing just the patients' RNA-seq data as input. We integrate RNA-seq data with different types of prior knowledge to extract quantitative descriptors of the tumor microenvironment from several points of view, including composition of the immune repertoire, and activity of intra- and extra-cellular communications. Then, we use multi-task machine learning trained in TCGA data to identify how these descriptors can simultaneously predict several state-of-the-art hallmarks of anti-cancer immune response. In this way we derive cancer-specific models and identify cancer-specific systems biomarkers of immune response. These biomarkers have been experimentally validated in the literature and the performance of EaSIeR predictions has been validated using independent datasets form four different cancer types with patients treated with anti-PD1 or anti-PDL1 therapy. biocViews: GeneExpression, Software, Transcription, SystemsBiology, Pathways, GeneSetEnrichment, ImmunoOncology, Epigenetics, Classification, BiomedicalInformatics, Regression, ExperimentHubSoftware Author: Oscar Lapuente-Santana [aut, cre] (), Federico Marini [aut] (), Arsenij Ustjanzew [aut] (), Francesca Finotello [aut] (), Federica Eduati [aut] () Maintainer: Oscar Lapuente-Santana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easier git_branch: RELEASE_3_16 git_last_commit: 20ef4de git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/easier_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/easier_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/easier_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/easier_1.4.0.tgz vignettes: vignettes/easier/inst/doc/easier_user_manual.html vignetteTitles: easier User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/easier/inst/doc/easier_user_manual.R dependencyCount: 205 Package: EasyCellType Version: 1.0.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, ggplot2, magrittr, rlang, stats, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, testthat (>= 3.0.0), Seurat, BiocManager, devtools License: Artistic-2.0 MD5sum: 647eb1bfd87c25f865e266a17328cd66 NeedsCompilation: no Title: Annotate cell types for scRNA-seq data Description: We developed EasyCellType which can automatically examine the input marker lists obtained from existing software such as Seurat over the cell markerdatabases. Two quantification approaches to annotate cell types are provided: Gene set enrichment analysis (GSEA) and a modified versio of Fisher's exact test. The function presents annotation recommendations in graphical outcomes: bar plots for each cluster showing candidate cell types, as well as a dot plot summarizing the top 5 significant annotations for each cluster. biocViews: SingleCell, Software, GeneExpression, GeneSetEnrichment Author: Ruoxing Li [aut, cre, ctb], Ziyi Li [ctb] Maintainer: Ruoxing Li VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EasyCellType git_branch: RELEASE_3_16 git_last_commit: d6e16b6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EasyCellType_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EasyCellType_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EasyCellType_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EasyCellType_1.0.0.tgz vignettes: vignettes/EasyCellType/inst/doc/my-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyCellType/inst/doc/my-vignette.R dependencyCount: 132 Package: easyreporting Version: 1.10.0 Depends: R (>= 3.5.0) Imports: rmarkdown, methods, tools, shiny, rlang Suggests: distill, BiocStyle, knitr, readxl, edgeR, limma, EDASeq, statmod License: Artistic-2.0 MD5sum: 5e2a0fca97c00157d1a6f06367cbccd1 NeedsCompilation: no Title: Helps creating report for improving Reproducible Computational Research Description: An S4 class for facilitating the automated creation of rmarkdown files inside other packages/software even without knowing rmarkdown language. Best if implemented in functions as "recursive" style programming. biocViews: ReportWriting Author: Dario Righelli [cre, aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: RELEASE_3_16 git_last_commit: 4036b93 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/easyreporting_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/easyreporting_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/easyreporting_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/easyreporting_1.10.0.tgz vignettes: vignettes/easyreporting/inst/doc/bio_usage.html, vignettes/easyreporting/inst/doc/standard_usage.html vignetteTitles: bio_usage.html, standard_usage.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R, vignettes/easyreporting/inst/doc/standard_usage.R dependencyCount: 47 Package: easyRNASeq Version: 2.34.0 Imports: Biobase (>= 2.50.0), BiocFileCache (>= 1.14.0), BiocGenerics (>= 0.36.0), BiocParallel (>= 1.24.1), biomaRt (>= 2.46.0), Biostrings (>= 2.58.0), edgeR (>= 3.32.0), GenomeInfoDb (>= 1.26.0), genomeIntervals (>= 1.46.0), GenomicAlignments (>= 1.26.0), GenomicRanges (>= 1.42.0), SummarizedExperiment (>= 1.20.0), graphics, IRanges (>= 2.24.0), LSD (>= 4.1-0), locfit, methods, parallel, rappdirs (>= 0.3.1), Rsamtools (>= 2.6.0), S4Vectors (>= 0.28.0), ShortRead (>= 1.48.0), utils Suggests: BiocStyle (>= 2.18.0), BSgenome (>= 1.58.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.32) License: Artistic-2.0 MD5sum: 9cbf39467a416d27084288899332bb0b NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: RELEASE_3_16 git_last_commit: 37ce549 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/easyRNASeq_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/easyRNASeq_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/easyRNASeq_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/easyRNASeq_2.34.0.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf, vignettes/easyRNASeq/inst/doc/simpleRNASeq.html vignetteTitles: R / Bioconductor for High Throughput Sequence Analysis, geneNetworkR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R importsMe: msgbsR dependencyCount: 106 Package: EBarrays Version: 2.62.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: x64 MD5sum: 37e657ffd76a71420a26ce30a2f162e8 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: RELEASE_3_16 git_last_commit: 1d200d5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EBarrays_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBarrays_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBarrays_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EBarrays_2.62.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 10 Package: EBcoexpress Version: 1.42.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: x64 MD5sum: e43fedf0e0c05dc3ea92c68cd5218570 NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: RELEASE_3_16 git_last_commit: 8e2fb30 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EBcoexpress_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBcoexpress_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBcoexpress_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EBcoexpress_1.42.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R suggestsMe: dcanr dependencyCount: 14 Package: EBImage Version: 4.40.1 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL Archs: x64 MD5sum: 0c1430b129a70769ac1d62d6f4cffbf8 NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: RELEASE_3_16 git_last_commit: f2b0b41 git_last_commit_date: 2023-04-09 Date/Publication: 2023-04-10 source.ver: src/contrib/EBImage_4.40.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBImage_4.40.1.zip mac.binary.ver: bin/macosx/contrib/4.2/EBImage_4.40.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EBImage_4.40.1.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: Cardinal, CRImage, cytomapper, flowcatchR, imageHTS, DonaPLLP2013, furrowSeg, MerfishData, GiNA, nucim importsMe: bnbc, flowCHIC, heatmaps, imcRtools, simpleSeg, synapsis, yamss, BioImageDbs, bioimagetools, GoogleImage2Array, LFApp, LOMAR, RockFab, SAFARI, trackter suggestsMe: HilbertVis, DmelSGI, aroma.core, cooltools, ExpImage, glow, graphx, ijtiff, juicr, lidR, metagear, pliman, ProFound, rcaiman, SIPmg dependencyCount: 49 Package: EBSEA Version: 1.26.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 7055b7242a27748eafee7c00087f5c29 NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: RELEASE_3_16 git_last_commit: 69a6064 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EBSEA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBSEA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBSEA_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EBSEA_1.26.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.html vignetteTitles: EBSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 92 Package: EBSeq Version: 1.38.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 3dcbccf3dab22b82b2535991266b3f02 NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_16 git_last_commit: df5ae80 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EBSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBSeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBSeq_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EBSeq_1.38.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: EBSeqHMM, Oscope importsMe: DEsubs, scDD suggestsMe: compcodeR dependencyCount: 47 Package: EBSeqHMM Version: 1.32.0 Depends: EBSeq License: Artistic-2.0 MD5sum: f91eb234118df0f141ecf9e6c80dc7cf NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeqHMM git_branch: RELEASE_3_16 git_last_commit: 92e51ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EBSeqHMM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EBSeqHMM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EBSeqHMM_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EBSeqHMM_1.32.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R dependencyCount: 48 Package: ecolitk Version: 1.70.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: b90afffa63497e4f37db7757beb5667c NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: RELEASE_3_16 git_last_commit: 5fe0e9e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ecolitk_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ecolitk_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ecolitk_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ecolitk_1.70.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 6 Package: EDASeq Version: 2.32.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat, DESeq2, rmarkdown License: Artistic-2.0 MD5sum: 948506096325abacab298979c0e9e958 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: RELEASE_3_16 git_last_commit: 06df5a3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EDASeq_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EDASeq_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EDASeq_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EDASeq_2.32.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: RUVSeq importsMe: consensusDE, DaMiRseq, metaseqR2, octad, ribosomeProfilingQC suggestsMe: awst, bigPint, DEScan2, easyreporting, GRaNIE, HTSFilter, TCGAbiolinks dependencyCount: 111 Package: edge Version: 2.30.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: x64 MD5sum: d1d99a3666fc3f05f226e739e7fb8760 NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: RELEASE_3_16 git_last_commit: a76c36a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/edge_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/edge_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/edge_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/edge_2.30.0.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 117 Package: edgeR Version: 3.40.2 Depends: R (>= 3.6.0), limma (>= 3.41.5) Imports: methods, graphics, stats, utils, locfit, Rcpp LinkingTo: Rcpp Suggests: jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi, SummarizedExperiment, org.Hs.eg.db License: GPL (>=2) Archs: x64 MD5sum: c6cd5f896a43e7c9b896e61a4e2a9f5f NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison, BiomedicalInformatics, CellBiology, FunctionalGenomics, Epigenetics, Genetics, ImmunoOncology, SystemsBiology, Transcriptomics Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson URL: http://bioinf.wehi.edu.au/edgeR, https://bioconductor.org/packages/edgeR SystemRequirements: C++11 git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_16 git_last_commit: ddb1cb9 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/edgeR_3.40.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/edgeR_3.40.2.zip mac.binary.ver: bin/macosx/contrib/4.2/edgeR_3.40.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/edgeR_3.40.2.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, IntEREst, methylMnM, miloR, octad, RNASeqR, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, csawBook, OSCA.advanced, OSCA.multisample, OSCA.workflows, babel, BALLI, BioInsight importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, autonomics, AWFisher, baySeq, beer, benchdamic, BioQC, censcyt, ChromSCape, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, crossmeta, csaw, DaMiRseq, dce, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, diffcyt, diffHic, diffUTR, DMRcate, doseR, DRIMSeq, DropletUtils, easyRNASeq, eegc, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, extraChIPs, GDCRNATools, Glimma, GSEABenchmarkeR, hermes, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR2, microbiomeMarker, MIGSA, MLSeq, moanin, Motif2Site, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PhIPData, ppcseq, PROPER, psichomics, RCM, regsplice, Repitools, ROSeq, scCB2, scde, scone, scran, ScreenR, SEtools, SIMD, SingleCellSignalR, singscore, sparrow, spatialHeatmap, splatter, SPsimSeq, srnadiff, sSNAPPY, standR, STATegRa, sva, TBSignatureProfiler, TCseq, tradeSeq, treekoR, tweeDEseq, vidger, xcore, yarn, zinbwave, emtdata, spatialLIBD, ExpHunterSuite, recountWorkflow, SingscoreAMLMutations, aIc, bulkAnalyseR, CAMML, CIDER, cinaR, ggpicrust2, HTSCluster, MetaLonDA, microbial, myTAI, QuasiSeq, RVA, scITD, SCRIP, scRNAtools, SPUTNIK, ssizeRNA, TSGS suggestsMe: ABSSeq, bigPint, biobroom, ClassifyR, cqn, cydar, dcanr, dearseq, DEScan2, dittoSeq, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEu, missMethyl, multiMiR, recount, regionReport, ribosomeProfilingQC, satuRn, SeqGate, signifinder, SpliceWiz, stageR, subSeq, SummarizedBenchmark, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zenith, zFPKM, leeBamViews, CAGEWorkflow, chipseqDB, DGEobj, DGEobj.utils, DiPALM, glmmSeq, Platypus, seqgendiff, SIBERG dependencyCount: 10 Package: eds Version: 1.0.0 Depends: Matrix Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, tximportData, testthat (>= 3.0.0) License: GPL-2 Archs: x64 MD5sum: 85c6bd1320b354b1227103894175f6b9 NeedsCompilation: yes Title: eds: Low-level reader for Alevin EDS format Description: This packages provides a single function, readEDS. This is a low-level utility for reading in Alevin EDS format into R. This function is not designed for end-users but instead the package is predominantly for simplifying package dependency graph for other Bioconductor packages. biocViews: Sequencing, RNASeq, GeneExpression, SingleCell Author: Avi Srivastava [aut, cre], Michael Love [aut, ctb] Maintainer: Avi Srivastava URL: https://github.com/mikelove/eds SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eds git_branch: RELEASE_3_16 git_last_commit: 916b910 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/eds_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eds_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eds_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/eds_1.0.0.tgz vignettes: vignettes/eds/inst/doc/eds.html vignetteTitles: eds: Low-level reader function for Alevin EDS format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eds/inst/doc/eds.R suggestsMe: tximport dependencyCount: 9 Package: eegc Version: 1.24.0 Depends: R (>= 3.4.0) Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR, DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db, org.Mm.eg.db, limma, DOSE, AnnotationDbi Suggests: knitr License: GPL-2 MD5sum: a01830ae30002de8ea33d9306518e007 NeedsCompilation: no Title: Engineering Evaluation by Gene Categorization (eegc) Description: This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells. biocViews: ImmunoOncology, Microarray, Sequencing, RNASeq, DifferentialExpression, GeneRegulation, GeneSetEnrichment, GeneExpression, GeneTarget Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei Maintainer: Xiaoyuan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eegc git_branch: RELEASE_3_16 git_last_commit: 5ff5737 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/eegc_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eegc_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eegc_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/eegc_1.24.0.tgz vignettes: vignettes/eegc/inst/doc/eegc.pdf vignetteTitles: Engineering Evaluation by Gene Categorization (eegc) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eegc/inst/doc/eegc.R dependencyCount: 157 Package: EGAD Version: 1.26.0 Depends: R(>= 3.5) Imports: gplots, Biobase, GEOquery, limma, impute, RColorBrewer, zoo, igraph, plyr, MASS, RCurl, methods Suggests: knitr, testthat, rmarkdown, markdown License: GPL-2 MD5sum: 1269c359e66dbd7a6528ebe3fc414d25 NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/EGAD git_branch: RELEASE_3_16 git_last_commit: f4695cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EGAD_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EGAD_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EGAD_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EGAD_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 63 Package: EGSEA Version: 1.26.0 Depends: R (>= 3.5), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: ec031d870897e310bebdfc7f5109b73c NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: RELEASE_3_16 git_last_commit: c3e5f41 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EGSEA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EGSEA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EGSEA_1.26.0.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R dependsOnMe: EGSEA123 suggestsMe: tidybulk, EGSEAdata dependencyCount: 188 Package: eiR Version: 1.38.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap,rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 4fff518f3b66ce1d8b47b75702e53a00 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: RELEASE_3_16 git_last_commit: 96ba2db git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/eiR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eiR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eiR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/eiR_1.38.0.tgz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR: Accelerated Similarity Searching of Small Molecules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 81 Package: eisaR Version: 1.10.0 Depends: R (>= 4.1) Imports: graphics, stats, GenomicRanges, S4Vectors, IRanges, limma, edgeR, methods, SummarizedExperiment, BiocGenerics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Rhisat2, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb, AnnotationDbi, GenomicFeatures, rtracklayer License: GPL-3 MD5sum: b92d1bf994bf18cd43607b386db578a5 NeedsCompilation: no Title: Exon-Intron Split Analysis (EISA) in R Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R. biocViews: Transcription, GeneExpression, GeneRegulation, FunctionalGenomics, Transcriptomics, Regression, RNASeq Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger [aut], Charlotte Soneson [aut] Maintainer: Michael Stadler URL: https://github.com/fmicompbio/eisaR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/eisaR/issues git_url: https://git.bioconductor.org/packages/eisaR git_branch: RELEASE_3_16 git_last_commit: 7b66d74 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/eisaR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eisaR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eisaR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/eisaR_1.10.0.tgz vignettes: vignettes/eisaR/inst/doc/eisaR.html, vignettes/eisaR/inst/doc/rna-velocity.html vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA), Generating reference files for spliced and unspliced abundance estimation with alignment-free methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisaR/inst/doc/eisaR.R, vignettes/eisaR/inst/doc/rna-velocity.R dependencyCount: 29 Package: ELMER Version: 2.22.0 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, GenomeInfoDb, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.23.7), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer, DelayedArray Suggests: BiocStyle, AnnotationHub, ExperimentHub, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: 40c2d94b6db36145eb8de725ab0ad16b NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: RELEASE_3_16 git_last_commit: a3d5369 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ELMER_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ELMER_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ELMER_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ELMER_2.22.0.tgz vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R importsMe: TCGAbiolinksGUI dependencyCount: 218 Package: EMDomics Version: 2.28.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: c7785eaa643af4a1846b2fb9c1345497 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: RELEASE_3_16 git_last_commit: aa8060e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EMDomics_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EMDomics_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EMDomics_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EMDomics_2.28.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 49 Package: EmpiricalBrownsMethod Version: 1.26.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d0796abd0422744d6a42ed1fc7150552 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: RELEASE_3_16 git_last_commit: 3c7a887 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EmpiricalBrownsMethod_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EmpiricalBrownsMethod_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EmpiricalBrownsMethod_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EmpiricalBrownsMethod_1.26.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependsOnMe: poolVIM importsMe: EBSEA dependencyCount: 0 Package: EnhancedVolcano Version: 1.16.0 Depends: ggplot2, ggrepel Imports: methods Suggests: ggalt, ggrastr, RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr, rmarkdown License: GPL-3 MD5sum: 54bfc2e0610cc542ac705867353e6113 NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes [ctb], Benjamin Ostendorf [ctb], Andrea Grioni [ctb], Myles Lewis [aut] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: RELEASE_3_16 git_last_commit: cc67675 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EnhancedVolcano_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnhancedVolcano_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EnhancedVolcano_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EnhancedVolcano_1.16.0.tgz vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html vignetteTitles: Publication-ready volcano plots with enhanced colouring and labeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R importsMe: ExpHunterSuite dependencyCount: 37 Package: enhancerHomologSearch Version: 1.4.2 Depends: R (>= 4.1.0), methods Imports: BiocGenerics, Biostrings, BSgenome, BiocParallel, BiocFileCache, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, motifmatchr, Matrix, rtracklayer, Rcpp, S4Vectors, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Mm.eg.db, MotifDb, testthat, TFBSTools License: GPL (>= 2) Archs: x64 MD5sum: c766049e598a26492da9470fd86439d7 NeedsCompilation: yes Title: Identification of putative mammalian orthologs to given enhancer Description: Get ENCODE data of enhancer region via H3K4me1 peaks and search homolog regions for given sequences. The candidates of enhancer homolog regions can be filtered by distance to target TSS. The top candidates from human and mouse will be aligned to each other and then exported as multiple alignments with given enhancer. biocViews: Sequencing, GeneRegulation, Alignment Author: Jianhong Ou [aut, cre] (), Valentina Cigliola [dtc], Kenneth Poss [fnd] Maintainer: Jianhong Ou URL: https://jianhong.github.io/enhancerHomologSearch VignetteBuilder: knitr BugReports: https://github.com/jianhong/enhancerHomologSearch/issues git_url: https://git.bioconductor.org/packages/enhancerHomologSearch git_branch: RELEASE_3_16 git_last_commit: c30e1e0 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/enhancerHomologSearch_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/enhancerHomologSearch_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/enhancerHomologSearch_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/enhancerHomologSearch_1.4.2.tgz vignettes: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.html vignetteTitles: enhancerHomologSearch Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enhancerHomologSearch/inst/doc/enhancerHomologSearch.R dependencyCount: 131 Package: ENmix Version: 1.34.02 Depends: R (>= 3.5.0), parallel, doParallel, foreach, SummarizedExperiment, stats Imports: grDevices,graphics,preprocessCore,matrixStats,methods,utils, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 97ffa721d92d8c52739f56c7eec8fb44 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre,aut], Liang Niu [aut] , Jack Taylor [ctb] Maintainer: Zongli Xu URL: https://github.com/Bioconductor/ENmix VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ENmix/issues git_url: https://git.bioconductor.org/packages/ENmix git_branch: RELEASE_3_16 git_last_commit: faf0823 git_last_commit_date: 2023-03-07 Date/Publication: 2023-03-08 source.ver: src/contrib/ENmix_1.34.02.tar.gz win.binary.ver: bin/windows/contrib/4.2/ENmix_1.34.02.zip mac.binary.ver: bin/macosx/contrib/4.2/ENmix_1.34.02.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ENmix_1.34.02.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.html vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 179 Package: EnrichedHeatmap Version: 1.28.1 Depends: R (>= 3.6.0), methods, grid, ComplexHeatmap (>= 2.11.0), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, rmarkdown, genefilter, RColorBrewer License: MIT + file LICENSE MD5sum: f52dcfa3183fea87ce02bd7dff0ad8ae NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: RELEASE_3_16 git_last_commit: 668edeb git_last_commit_date: 2023-03-19 Date/Publication: 2023-03-20 source.ver: src/contrib/EnrichedHeatmap_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnrichedHeatmap_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.2/EnrichedHeatmap_1.28.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EnrichedHeatmap_1.28.1.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive Associations in Roadmap dataset, 3. Compare row ordering methods, 2. Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R importsMe: extraChIPs, profileplyr suggestsMe: ComplexHeatmap, epistack, InteractiveComplexHeatmap dependencyCount: 40 Package: EnrichmentBrowser Version: 2.28.2 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR, graphite, hwriter, limma, methods, pathview, safe Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools, airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 116eb27a4c41ceb5949e0bb6c9957c01 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Mirko Signorelli [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: RELEASE_3_16 git_last_commit: 0db911d git_last_commit_date: 2023-03-19 Date/Publication: 2023-03-20 source.ver: src/contrib/EnrichmentBrowser_2.28.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/EnrichmentBrowser_2.28.2.zip mac.binary.ver: bin/macosx/contrib/4.2/EnrichmentBrowser_2.28.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EnrichmentBrowser_2.28.2.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR, zenith suggestsMe: GenomicSuperSignature dependencyCount: 91 Package: enrichplot Version: 1.18.4 Depends: R (>= 3.5.0) Imports: aplot (>= 0.1.4), DOSE (>= 3.16.0), ggnewscale, ggplot2, ggraph, graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, rlang, stats, utils, scatterpie, shadowtext, GOSemSim, magrittr, ggtree, yulab.utils (>= 0.0.4) Suggests: clusterProfiler, dplyr, europepmc, ggupset, knitr, rmarkdown, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggrepel (>= 0.9.0), ggstar, treeio, scales, tidytree, ggtreeExtra, tidydr License: Artistic-2.0 MD5sum: f8ad4bab77c4354bf8aca57692df2c1b NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. It is mainly designed to work with the 'clusterProfiler' package suite. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] (), Erqiang Hu [ctb] (), Chun-Hui Gao [ctb] () Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: RELEASE_3_16 git_last_commit: d85003b git_last_commit_date: 2023-04-02 Date/Publication: 2023-04-03 source.ver: src/contrib/enrichplot_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/enrichplot_1.18.4.zip mac.binary.ver: bin/macosx/contrib/4.2/enrichplot_1.18.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/enrichplot_1.18.4.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ChIPseeker, clusterProfiler, debrowser, MAGeCKFlute, meshes, MicrobiomeProfiler, multiSight, ReactomePA, ExpHunterSuite suggestsMe: methylGSA, pareg, SCpubr dependencyCount: 125 Package: enrichTF Version: 1.14.0 Depends: pipeFrame Imports: BSgenome, rtracklayer, motifmatchr, TFBSTools, R.utils, methods, JASPAR2018, GenomeInfoDb, GenomicRanges, IRanges, BiocGenerics, S4Vectors, utils, parallel, stats, ggpubr, heatmap3, ggplot2, clusterProfiler, rmarkdown, grDevices, magrittr Suggests: knitr, testthat, webshot License: GPL-3 MD5sum: e8e6f8eecf3fdec5b9e6e29a1022647e NeedsCompilation: no Title: Transcription Factors Enrichment Analysis Description: As transcription factors (TFs) play a crucial role in regulating the transcription process through binding on the genome alone or in a combinatorial manner, TF enrichment analysis is an efficient and important procedure to locate the candidate functional TFs from a set of experimentally defined regulatory regions. While it is commonly accepted that structurally related TFs may have similar binding preference to sequences (i.e. motifs) and one TF may have multiple motifs, TF enrichment analysis is much more challenging than motif enrichment analysis. Here we present a R package for TF enrichment analysis which combine motif enrichment with the PECA model. biocViews: Software, GeneTarget, MotifAnnotation, GraphAndNetwork, Transcription Author: Zheng Wei, Zhana Duren, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/enrichTF VignetteBuilder: knitr BugReports: https://github.com/wzthu/enrichTF/issues git_url: https://git.bioconductor.org/packages/enrichTF git_branch: RELEASE_3_16 git_last_commit: 7aa6e22 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/enrichTF_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/enrichTF_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/enrichTF_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/enrichTF_1.14.0.tgz vignettes: vignettes/enrichTF/inst/doc/enrichTF.html vignetteTitles: An Introduction to enrichTF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichTF/inst/doc/enrichTF.R dependencyCount: 221 Package: ensembldb Version: 2.22.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.49.6), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.47.9), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), magrittr, rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: 59d5563c58f14888b07507513e12fdf3 NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto and Christian Weichenberger. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: RELEASE_3_16 git_last_commit: 4dda178 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ensembldb_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ensembldb_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ensembldb_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ensembldb_2.22.0.tgz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE, diffUTR, epimutacions, epivizrData, ggbio, Gviz, metagene, proteasy, RITAN, scanMiRApp, signifinder, singleCellTK, TVTB, tximeta, GenomicDistributionsData, scRNAseq, crosstalkr, MOCHA, RNAseqQC, utr.annotation suggestsMe: alpine, CNVRanger, dasper, eisaR, EpiTxDb, fishpond, GenomicFeatures, ldblock, multicrispr, nullranges, satuRn, wiggleplotr dependencyCount: 99 Package: ensemblVEP Version: 1.40.0 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: c637c93267812afc223745b033ea95c5 NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API. biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain and Lori Shepherd Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 105) and the Perl modules DBI and DBD::mysql must be installed. See the package README and Ensembl installation instructions: http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer git_url: https://git.bioconductor.org/packages/ensemblVEP git_branch: RELEASE_3_16 git_last_commit: 1c081b3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-12-09 source.ver: src/contrib/ensemblVEP_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ensemblVEP_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ensemblVEP_1.40.0.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf vignetteTitles: ensemblVEP, PreV90EnsemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R importsMe: MMAPPR2, TVTB dependencyCount: 98 Package: epialleleR Version: 1.6.1 Depends: R (>= 4.1) Imports: stats, methods, utils, GenomicRanges, BiocGenerics, GenomeInfoDb, SummarizedExperiment, VariantAnnotation, stringi, data.table, Rcpp LinkingTo: Rcpp, BH, Rhtslib, zlibbioc Suggests: RUnit, knitr, rmarkdown, ggplot2, ggstance, gridExtra License: Artistic-2.0 MD5sum: 4f1ada3aab291db95f429375b4885d66 NeedsCompilation: yes Title: Fast, Epiallele-Aware Methylation Reporter Description: Epialleles are specific DNA methylation patterns that are mitotically and/or meiotically inherited. This package calls hypermethylated epiallele frequencies at the level of genomic regions or individual cytosines in next-generation sequencing data using binary alignment map (BAM) files as an input. Other functionality includes extracting methylation patterns, computing the empirical cumulative distribution function for per-read beta values, and testing the significance of the association between epiallele methylation status and base frequencies at particular genomic positions (SNPs). biocViews: DNAMethylation, Epigenetics, MethylSeq Author: Oleksii Nikolaienko [aut, cre] () Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/epialleleR SystemRequirements: C++17, GNU make VignetteBuilder: knitr BugReports: https://github.com/BBCG/epialleleR/issues git_url: https://git.bioconductor.org/packages/epialleleR git_branch: RELEASE_3_16 git_last_commit: 8bc723f git_last_commit_date: 2023-02-19 Date/Publication: 2023-02-19 source.ver: src/contrib/epialleleR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/epialleleR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/epialleleR_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epialleleR_1.6.1.tgz vignettes: vignettes/epialleleR/inst/doc/epialleleR.html vignetteTitles: epialleleR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epialleleR/inst/doc/epialleleR.R dependencyCount: 100 Package: EpiCompare Version: 1.2.0 Depends: R (>= 4.1.0) Imports: AnnotationHub, BRGenomics, ChIPseeker, data.table, genomation, GenomicRanges, IRanges, GenomeInfoDb, ggplot2, htmltools, methods, plotly, reshape2, rmarkdown, rtracklayer, stats, stringr, utils, BiocGenerics Suggests: badger, BiocFileCache, BiocParallel, parallel, BiocStyle, clusterProfiler, GenomicAlignments, grDevices, htmlwidgets, knitr, org.Hs.eg.db, testthat (>= 3.0.0), tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, UpSetR, plyranges, scales, Matrix, consensusSeekeR License: GPL-3 Archs: x64 MD5sum: 0dc68e72644aa43c811046b2ed3ae5ec NeedsCompilation: no Title: Comparison, Benchmarking & QC of Epigenomic Datasets Description: EpiCompare is used to compare and analyse epigenetic datasets for quality control and benchmarking purposes. The package outputs an HTML report consisting of three sections: (1. General metrics) Metrics on peaks (percentage of blacklisted and non-standard peaks, and peak widths) and fragments (duplication rate) of samples, (2. Peak overlap) Percentage and statistical significance of overlapping and non-overlapping peaks. Also includes upset plot and (3. Functional annotation) functional annotation (ChromHMM, ChIPseeker and enrichment analysis) of peaks. Also includes peak enrichment around TSS. biocViews: Epigenetics, Genetics, QualityControl, ChIPSeq, MultipleComparison, FunctionalGenomics, ATACSeq, DNaseSeq Author: Sera Choi [aut, cre] (), Brian Schilder [aut] (), Leyla Abbasova [aut], Alan Murphy [aut] (), Nathan Skene [aut] () Maintainer: Sera Choi URL: https://github.com/neurogenomics/EpiCompare VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/EpiCompare/issues git_url: https://git.bioconductor.org/packages/EpiCompare git_branch: RELEASE_3_16 git_last_commit: c1e27ec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EpiCompare_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EpiCompare_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EpiCompare_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EpiCompare_1.2.0.tgz vignettes: vignettes/EpiCompare/inst/doc/docker.html, vignettes/EpiCompare/inst/doc/EpiCompare.html vignetteTitles: docker, EpiCompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiCompare/inst/doc/docker.R, vignettes/EpiCompare/inst/doc/EpiCompare.R dependencyCount: 201 Package: epidecodeR Version: 1.6.0 Depends: R (>= 3.1.0) Imports: EnvStats, ggplot2, rtracklayer, GenomicRanges, IRanges, rstatix, ggpubr, methods, stats, utils, dplyr Suggests: knitr, rmarkdown License: GPL-3 MD5sum: ba98fc51a7f5aed3da968fa40faca816 NeedsCompilation: no Title: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation Description: epidecodeR is a package capable of analysing impact of degree of DNA/RNA epigenetic chemical modifications on dysregulation of genes or proteins. This package integrates chemical modification data generated from a host of epigenomic or epitranscriptomic techniques such as ChIP-seq, ATAC-seq, m6A-seq, etc. and dysregulated gene lists in the form of differential gene expression, ribosome occupancy or differential protein translation and identify impact of dysregulation of genes caused due to varying degrees of chemical modifications associated with the genes. epidecodeR generates cumulative distribution function (CDF) plots showing shifts in trend of overall log2FC between genes divided into groups based on the degree of modification associated with the genes. The tool also tests for significance of difference in log2FC between groups of genes. biocViews: DifferentialExpression, GeneRegulation, HistoneModification, FunctionalPrediction, Transcription, GeneExpression, Epitranscriptomics, Epigenetics, FunctionalGenomics, SystemsBiology, Transcriptomics, ChipOnChip Author: Kandarp Joshi [aut, cre], Dan Ohtan Wang [aut] Maintainer: Kandarp Joshi URL: https://github.com/kandarpRJ/epidecodeR, https://epidecoder.shinyapps.io/shinyapp VignetteBuilder: knitr BugReports: https://github.com/kandarpRJ/epidecodeR/issues git_url: https://git.bioconductor.org/packages/epidecodeR git_branch: RELEASE_3_16 git_last_commit: fd482c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epidecodeR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epidecodeR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epidecodeR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epidecodeR_1.6.0.tgz vignettes: vignettes/epidecodeR/inst/doc/epidecodeR.html vignetteTitles: epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epidecodeR/inst/doc/epidecodeR.R dependencyCount: 126 Package: EpiDISH Version: 2.14.1 Depends: R (>= 4.1) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr, Matrix Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: 4d5ee3cc189547a6aa761363ef0632c2 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of whole blood, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre] Maintainer: Shijie C. Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: RELEASE_3_16 git_last_commit: 074cedf git_last_commit_date: 2022-11-09 Date/Publication: 2022-11-09 source.ver: src/contrib/EpiDISH_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/EpiDISH_2.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/EpiDISH_2.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EpiDISH_2.14.1.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST suggestsMe: planet dependencyCount: 26 Package: epigenomix Version: 1.38.0 Depends: R (>= 3.5.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 7a886d5f5da9650c322dcf82f697648f NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: RELEASE_3_16 git_last_commit: 3022714 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epigenomix_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epigenomix_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epigenomix_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epigenomix_1.38.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R dependencyCount: 103 Package: epigraHMM Version: 1.6.4 Depends: R (>= 3.5.0) Imports: Rcpp, magrittr, data.table, SummarizedExperiment, methods, GenomeInfoDb, GenomicRanges, rtracklayer, IRanges, Rsamtools, bamsignals, csaw, S4Vectors, limma, stats, Rhdf5lib, rhdf5, Matrix, MASS, scales, ggpubr, ggplot2, GreyListChIP, pheatmap, grDevices LinkingTo: Rcpp, RcppArmadillo, Rhdf5lib Suggests: testthat, knitr, rmarkdown, BiocStyle, BSgenome.Rnorvegicus.UCSC.rn4, gcapc, chromstaRData License: MIT + file LICENSE MD5sum: 59f9ba44ea3db58427058f12b52f2a94 NeedsCompilation: yes Title: Epigenomic R-based analysis with hidden Markov models Description: epigraHMM provides a set of tools for the analysis of epigenomic data based on hidden Markov Models. It contains two separate peak callers, one for consensus peaks from biological or technical replicates, and one for differential peaks from multi-replicate multi-condition experiments. In differential peak calling, epigraHMM provides window-specific posterior probabilities associated with every possible combinatorial pattern of read enrichment across conditions. biocViews: ChIPSeq, ATACSeq, DNaseSeq, HiddenMarkovModel, Epigenetics Author: Pedro Baldoni [aut, cre] Maintainer: Pedro Baldoni SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epigraHMM git_branch: RELEASE_3_16 git_last_commit: 5055b06 git_last_commit_date: 2023-03-22 Date/Publication: 2023-03-24 source.ver: src/contrib/epigraHMM_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/epigraHMM_1.6.4.zip mac.binary.ver: bin/macosx/contrib/4.2/epigraHMM_1.6.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epigraHMM_1.6.4.tgz vignettes: vignettes/epigraHMM/inst/doc/epigraHMM.html vignetteTitles: Consensus and Differential Peak Calling With epigraHMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epigraHMM/inst/doc/epigraHMM.R dependencyCount: 138 Package: epihet Version: 1.13.0 Depends: R(>= 3.6), GenomicRanges, IRanges, S4Vectors, ggplot2, foreach, Rtsne, igraph Imports: data.table, doParallel, EntropyExplorer, graphics, stats, grDevices, pheatmap, utils, qvalue, WGCNA, ReactomePA Suggests: knitr, clusterProfiler, ggfortify, org.Hs.eg.db, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 26b1e6d8404649e2cafdbfbff19a2805 NeedsCompilation: no Title: Determining Epigenetic Heterogeneity from Bisulfite Sequencing Data Description: epihet is an R-package that calculates the epigenetic heterogeneity between cancer cells and/or normal cells. The functions establish a pipeline that take in bisulfite sequencing data from multiple samples and use the data to track similarities and differences in epipolymorphism,proportion of discordantly methylated sequencing reads (PDR),and Shannon entropy values at epialleles that are shared between the samples.epihet can be used to perform analysis on the data by creating pheatmaps, box plots, PCA plots, and t-SNE plots. MA plots can also be created by calculating the differential heterogeneity of the samples. And we construct co-epihet network and perform network analysis. biocViews: DNAMethylation, Epigenetics, MethylSeq, Sequencing, Software Author: Xiaowen Chen [aut, cre], Haitham Ashoor [aut], Ryan Musich [aut], Mingsheng Zhang [aut], Jiahui Wang [aut], Sheng Li [aut] Maintainer: Xiaowen Chen URL: https://github.com/TheJacksonLaboratory/epihet VignetteBuilder: knitr BugReports: https://github.com/TheJacksonLaboratory/epihet/issues git_url: https://git.bioconductor.org/packages/epihet git_branch: master git_last_commit: 4e1b422 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/epihet_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epihet_1.13.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epihet_1.13.0.tgz vignettes: vignettes/epihet/inst/doc/epihet.pdf vignetteTitles: epihet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epihet/inst/doc/epihet.R dependencyCount: 172 Package: EpiMix Version: 1.0.1 Depends: R (>= 4.2.0), EpiMix.data (>= 0.99.2) Imports: AnnotationHub, AnnotationDbi, Biobase, biomaRt, data.table, doParallel, doSNOW, downloader, dplyr, ELMER.data, ExperimentHub, foreach, GenomeInfoDb, GenomicFeatures, GenomicRanges, GEOquery, ggplot2, graphics, grDevices, impute, IRanges, limma, methods, parallel, plyr, progress, R.matlab, RColorBrewer, RCurl, rlang, RPMM, S4Vectors, stats, SummarizedExperiment, tibble, tidyr, utils Suggests: BiocStyle, clusterProfiler, karyoploteR, knitr, org.Hs.eg.db, regioneR, Seurat, survival, survminer, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics License: GPL-3 MD5sum: 0efebd22c87fa4a4f29707c8e613d2f7 NeedsCompilation: no Title: EpiMix: an integrative tool for the population-level analysis of DNA methylation Description: EpiMix is a comprehensive tool for the integrative analysis of high-throughput DNA methylation data and gene expression data. EpiMix enables automated data downloading (from TCGA or GEO), preprocessing, methylation modeling, interactive visualization and functional annotation.To identify hypo- or hypermethylated CpG sites across physiological or pathological conditions, EpiMix uses a beta mixture modeling to identify the methylation states of each CpG probe and compares the methylation of the experimental group to the control group.The output from EpiMix is the functional DNA methylation that is predictive of gene expression. EpiMix incorporates specialized algorithms to identify functional DNA methylation at various genetic elements, including proximal cis-regulatory elements of protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. biocViews: Software, Epigenetics, Preprocessing, DNAMethylation, GeneExpression, DifferentialMethylation Author: Yuanning Zheng [aut, cre], John Jun [aut], Olivier Gevaert [aut] Maintainer: Yuanning Zheng VignetteBuilder: knitr BugReports: https://github.com/gevaertlab/EpiMix/issues git_url: https://git.bioconductor.org/packages/EpiMix git_branch: RELEASE_3_16 git_last_commit: 6a1aecd git_last_commit_date: 2023-02-14 Date/Publication: 2023-02-14 source.ver: src/contrib/EpiMix_1.0.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/EpiMix_1.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EpiMix_1.0.1.tgz vignettes: vignettes/EpiMix/inst/doc/Methylation_Modeling.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiMix/inst/doc/Methylation_Modeling.R dependencyCount: 163 Package: epimutacions Version: 1.2.0 Depends: R (>= 4.2.0), epimutacionsData Imports: minfi, bumphunter, isotree, robustbase, ggplot2, GenomicRanges, GenomicFeatures, IRanges, SummarizedExperiment, stats, matrixStats, BiocGenerics, S4Vectors, utils, biomaRt, BiocParallel, GenomeInfoDb, Homo.sapiens, purrr, tibble, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, rtracklayer, AnnotationDbi, AnnotationHub, ExperimentHub, reshape2, grid, ensembldb, gridExtra, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, ggrepel Suggests: testthat, knitr, rmarkdown, BiocStyle, a4Base, kableExtra, methods, grDevices License: MIT + file LICENSE MD5sum: 51f420dc23c3131fc644f4462bd99793 NeedsCompilation: yes Title: Robust outlier identification for DNA methylation data Description: The package includes some statistical outlier detection methods for epimutations detection in DNA methylation data. The methods included in the package are MANOVA, Multivariate linear models, isolation forest, robust mahalanobis distance, quantile and beta. The methods compare a case sample with a suspected disease against a reference panel (composed of healthy individuals) to identify epimutations in the given case sample. It also contains functions to annotate and visualize the identified epimutations. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (), Juan R. Gonzalez [aut] (), Carlos Ruiz-Arenas [aut] (), Carles Hernandez-Ferrer [aut] (), Leire Abarrategui [aut] () Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/epimutacions VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/epimutacions/issues git_url: https://git.bioconductor.org/packages/epimutacions git_branch: RELEASE_3_16 git_last_commit: b5bb97c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epimutacions_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epimutacions_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epimutacions_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epimutacions_1.2.0.tgz vignettes: vignettes/epimutacions/inst/doc/epimutacions.html vignetteTitles: Detection of epimutations with state of the art methods in methylation data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epimutacions/inst/doc/epimutacions.R dependencyCount: 223 Package: epiNEM Version: 1.22.0 Depends: R (>= 4.1) Imports: BoolNet, e1071, gtools, stats, igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph, mnem, latex2exp Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db License: GPL-3 Archs: x64 MD5sum: a9264cbf5edec4ad27ecdf189366fcd3 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. It is tailored towards large scale double knock-out screens. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/epiNEM/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/epiNEM/issues git_url: https://git.bioconductor.org/packages/epiNEM git_branch: RELEASE_3_16 git_last_commit: 718cb64 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epiNEM_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epiNEM_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epiNEM_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epiNEM_1.22.0.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.html vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R importsMe: bnem, dce, nempi suggestsMe: mnem dependencyCount: 108 Package: epistack Version: 1.4.0 Depends: R (>= 4.1) Imports: GenomicRanges, SummarizedExperiment, BiocGenerics, S4Vectors, IRanges, viridisLite, graphics, plotrix, grDevices, stats, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, EnrichedHeatmap, biomaRt, rtracklayer, covr, vdiffr, magick License: MIT + file LICENSE MD5sum: e5d8ace460efb0bbb035794209180bf3 NeedsCompilation: no Title: Heatmaps of Stack Profiles from Epigenetic Signals Description: The epistack package main objective is the visualizations of stacks of genomic tracks (such as, but not restricted to, ChIP-seq, ATAC-seq, DNA methyation or genomic conservation data) centered at genomic regions of interest. biocViews: RNASeq, Preprocessing, ChIPSeq, GeneExpression Author: SACI Safia [aut], DEVAILLY Guillaume [cre] Maintainer: DEVAILLY Guillaume VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epistack git_branch: RELEASE_3_16 git_last_commit: 6e62201 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epistack_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epistack_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epistack_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epistack_1.4.0.tgz vignettes: vignettes/epistack/inst/doc/using_epistack.html vignetteTitles: Using epistack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epistack/inst/doc/using_epistack.R dependencyCount: 27 Package: epistasisGA Version: 1.0.2 Depends: R (>= 4.0) Imports: BiocParallel, data.table, matrixStats, stats, survival, igraph, batchtools, qgraph, grDevices, parallel, Rcpp (>= 0.11.0), ggplot2, grid, graphics, utils LinkingTo: Rcpp, RcppArmadillo Suggests: Matrix, BiocStyle, knitr, rmarkdown, magrittr, kableExtra, testthat (>= 3.0.0) License: GPL-3 MD5sum: 1222ff801ba141fddd152970c6623aff NeedsCompilation: yes Title: An R package to identify multi-snp effects in nuclear family studies using the GADGETS method Description: This package runs the GADGETS method to identify epistatic effects in nuclear family studies. It also provides functions for permutation-based inference and graphical visualization of the results. biocViews: Genetics, SNP, GeneticVariability Author: Michael Nodzenski [aut, cre], Juno Krahn [ctb] Maintainer: Michael Nodzenski URL: https://github.com/mnodzenski/epistasisGA VignetteBuilder: knitr BugReports: https://github.com/mnodzenski/epistasisGA/issues git_url: https://git.bioconductor.org/packages/epistasisGA git_branch: RELEASE_3_16 git_last_commit: 89c2598 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/epistasisGA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/epistasisGA_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/epistasisGA_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epistasisGA_1.0.2.tgz vignettes: vignettes/epistasisGA/inst/doc/basic_usage.html vignetteTitles: GADGETS Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epistasisGA/inst/doc/basic_usage.R dependencyCount: 114 Package: EpiTxDb Version: 1.10.0 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, GenomicFeatures, GenomicRanges, GenomeInfoDb, BiocGenerics, BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest, AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 Archs: x64 MD5sum: 4461aeaecb08c8025776e81737d5f5c3 NeedsCompilation: no Title: Storing and accessing epitranscriptomic information using the AnnotationDbi interface Description: EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with. biocViews: Software, Epitranscriptomics Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/EpiTxDb VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/EpiTxDb/issues git_url: https://git.bioconductor.org/packages/EpiTxDb git_branch: RELEASE_3_16 git_last_commit: 2ced971 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EpiTxDb_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EpiTxDb_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EpiTxDb_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EpiTxDb_1.10.0.tgz vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html, vignettes/EpiTxDb/inst/doc/EpiTxDb.html vignetteTitles: EpiTxDb-creation, EpiTxDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R, vignettes/EpiTxDb/inst/doc/EpiTxDb.R dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3 dependencyCount: 114 Package: epivizr Version: 2.28.0 Depends: R (>= 3.5.0), methods Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, minfi, rmarkdown License: Artistic-2.0 MD5sum: 4e64b3fe2ee9c4ba3c11a80ca868634e NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: RELEASE_3_16 git_last_commit: b6baa40 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epivizr_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizr_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizr_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epivizr_2.28.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone, scTreeViz importsMe: metavizr dependencyCount: 117 Package: epivizrChart Version: 1.20.0 Depends: R (>= 3.5.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 MD5sum: 505b4fb5b026abd9754c631f58c62f9c NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: RELEASE_3_16 git_last_commit: 0ededb8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epivizrChart_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrChart_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrChart_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epivizrChart_1.20.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 113 Package: epivizrData Version: 1.26.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI, matrixStats License: MIT + file LICENSE MD5sum: c208f72e3255905f13d292b740f5a70e NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: RELEASE_3_16 git_last_commit: 44f48d3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epivizrData_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrData_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrData_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epivizrData_1.26.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: epivizrData Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, metavizr, scTreeViz dependencyCount: 109 Package: epivizrServer Version: 1.26.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: b396fa1c9286bece2671aeb8827d154c NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: RELEASE_3_16 git_last_commit: ae6460e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epivizrServer_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrServer_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrServer_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epivizrServer_1.26.0.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, metavizr, scTreeViz dependencyCount: 13 Package: epivizrStandalone Version: 1.26.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: b4f345553fbeb8376970f8d3aad418d0 NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: RELEASE_3_16 git_last_commit: 538d8d7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/epivizrStandalone_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/epivizrStandalone_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/epivizrStandalone_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/epivizrStandalone_1.26.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr suggestsMe: scTreeViz dependencyCount: 119 Package: erccdashboard Version: 1.32.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: ba26cc07920c686e3010e7bbed975d3e NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: https://github.com/munrosa/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/munrosa/erccdashboard/issues git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: RELEASE_3_16 git_last_commit: e374442 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/erccdashboard_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/erccdashboard_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/erccdashboard_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/erccdashboard_1.32.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 52 Package: erma Version: 1.14.0 Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2) Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb, Biobase, shiny, BiocParallel, IRanges, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel License: Artistic-2.0 MD5sum: 5811fca20ad2fce297e69eb2bfe15776 NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erma git_branch: RELEASE_3_16 git_last_commit: b240e98 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/erma_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/erma_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/erma_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/erma_1.14.0.tgz vignettes: vignettes/erma/inst/doc/erma.html vignetteTitles: ermaInteractive hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R dependencyCount: 137 Package: ERSSA Version: 1.16.0 Depends: R (>= 4.0.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 6af7e20eb2f76d86d1af1b2eb03721e7 NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: RELEASE_3_16 git_last_commit: cb8f4ab git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ERSSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ERSSA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ERSSA_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ERSSA_1.16.0.tgz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 94 Package: esATAC Version: 1.20.0 Depends: R (>= 4.0.0), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, GenomicAlignments, GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat, webshot License: GPL-3 | file LICENSE MD5sum: 0665935ec59a7dc0d13a89a2a7aee2b4 NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: RELEASE_3_16 git_last_commit: 6c9fe81 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/esATAC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/esATAC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/esATAC_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/esATAC_1.20.0.tgz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 208 Package: escape Version: 1.8.0 Depends: R (>= 4.1) Imports: grDevices, dplyr, ggplot2, GSEABase, GSVA, SingleCellExperiment, ggridges, msigdbr, stats, BiocParallel, Matrix, UCell, broom, reshape2, patchwork, MatrixGenerics, utils, rlang, stringr, data.table, SummarizedExperiment, methods Suggests: Seurat, SeuratObject, knitr, rmarkdown, markdown, BiocStyle, testthat, dittoSeq (>= 1.1.2) License: GPL-2 Archs: x64 MD5sum: fddfcbb4f063e9d8db08427bdba86222 NeedsCompilation: no Title: Easy single cell analysis platform for enrichment Description: A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells. biocViews: Software, SingleCell, Classification, Annotation, GeneSetEnrichment, Sequencing, GeneSignaling, Pathways Author: Nick Borcherding [aut, cre], Jared Andrews [aut] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/escape git_branch: RELEASE_3_16 git_last_commit: 3cb0ccd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/escape_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/escape_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/escape_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/escape_1.8.0.tgz vignettes: vignettes/escape/inst/doc/vignette.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/escape/inst/doc/vignette.R suggestsMe: Cepo dependencyCount: 122 Package: esetVis Version: 1.24.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: c7fb116c6d1b27829af8d7e4db990211 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: RELEASE_3_16 git_last_commit: 188114a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/esetVis_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/esetVis_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/esetVis_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/esetVis_1.24.0.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 58 Package: eudysbiome Version: 1.28.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 9170c603e9591dcb733041fe8d592bb7 NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: RELEASE_3_16 git_last_commit: 27a1602 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/eudysbiome_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/eudysbiome_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/eudysbiome_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/eudysbiome_1.28.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 36 Package: evaluomeR Version: 1.14.0 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: 8abc37b5da285307f8f50debec7ee2ec NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: RELEASE_3_16 git_last_commit: d549bf3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/evaluomeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/evaluomeR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/evaluomeR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/evaluomeR_1.14.0.tgz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 122 Package: EventPointer Version: 3.6.0 Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, Biostrings, glmnet, abind, iterators, lpSolve, poibin, speedglm, tximport, fgsea Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: 7fb6b64c68081a77a4a83b502227aac7 NeedsCompilation: yes Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero [aut], Juan A. Ferrer-Bonsoms [aut, cre], Pablo Sacristan [aut], Ander Muniategui [aut], Fernando Carazo [aut], Ander Aramburu [aut], Angel Rubio [aut] Maintainer: Juan A. Ferrer-Bonsoms VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: RELEASE_3_16 git_last_commit: a3a397b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EventPointer_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EventPointer_3.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EventPointer_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EventPointer_3.6.0.tgz vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R dependencyCount: 158 Package: EWCE Version: 1.6.0 Depends: R (>= 4.2), RNOmni (>= 1.0) Imports: stats, utils, methods, ewceData, dplyr, ggplot2, reshape2, limma, stringr, HGNChelper, Matrix, parallel, SingleCellExperiment, SummarizedExperiment, DelayedArray, BiocParallel, orthogene (>= 0.99.8), data.table Suggests: remotes, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), readxl, memoise, markdown, sctransform, DESeq2, MAST, DelayedMatrixStats, cowplot, ggdendro, grDevices, grid, gridExtra, scales, magick, badger License: GPL-3 Archs: x64 MD5sum: 9c76425eadd916e87b3c8de9b1ab7edf NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics, SingleCell Author: Alan Murphy [cre] (), Brian Schilder [aut] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/NathanSkene/EWCE VignetteBuilder: knitr BugReports: https://github.com/NathanSkene/EWCE/issues git_url: https://git.bioconductor.org/packages/EWCE git_branch: RELEASE_3_16 git_last_commit: e37a8f2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/EWCE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/EWCE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EWCE_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EWCE_1.6.0.tgz vignettes: vignettes/EWCE/inst/doc/EWCE.html, vignettes/EWCE/inst/doc/extended.html vignetteTitles: Getting started, Extended examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R, vignettes/EWCE/inst/doc/extended.R dependencyCount: 202 Package: ExCluster Version: 1.16.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 0d23af311ba618053d8b5d5df320f72f NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: RELEASE_3_16 git_last_commit: 11c15d0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExCluster_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ExCluster_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExCluster_1.16.0.tgz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 47 Package: ExiMiR Version: 2.40.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: c08d694578905c778ccb49e9c2c7d181 NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: RELEASE_3_16 git_last_commit: 70d7270 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExiMiR_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExiMiR_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExiMiR_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExiMiR_2.40.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 13 Package: exomeCopy Version: 1.44.0 Depends: R (>= 3.5.0), IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) MD5sum: 6a07931c4b2acae6d052add5468327b0 NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love git_url: https://git.bioconductor.org/packages/exomeCopy git_branch: RELEASE_3_16 git_last_commit: 2dd6598 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/exomeCopy_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/exomeCopy_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/exomeCopy_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/exomeCopy_1.44.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: cn.mops, CNVPanelizer, contiBAIT dependencyCount: 31 Package: exomePeak2 Version: 1.10.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: Rsamtools,GenomicAlignments,GenomicRanges,GenomicFeatures,DESeq2,ggplot2,mclust,BSgenome,Biostrings,GenomeInfoDb,BiocParallel,IRanges,S4Vectors,rtracklayer,methods,stats,utils,BiocGenerics,magrittr,speedglm,splines Suggests: knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: c96e499f822a2522298910a4666d4b0e NeedsCompilation: no Title: Peak Calling and differential analysis for MeRIP-Seq Description: exomePeak2 provides peak detection and differential methylation for Methylated RNA Immunoprecipitation Sequencing (MeRIP-Seq) data. MeRIP-Seq is a commonly applied sequencing assay that measures the location and abundance of RNA modification sites under specific cellular conditions. In practice, the technique is sensitive to PCR amplification biases commonly found in NGS data. In addition, the efficiency of immunoprecipitation often varies between different IP samples. exomePeak2 can perform peak calling and differential analysis independent of GC content bias and IP efficiency changes. biocViews: Sequencing, MethylSeq, RNASeq, Coverage, DifferentialMethylation, DifferentialPeakCalling, PeakDetection Author: Zhen Wei [aut, cre] Maintainer: Zhen Wei VignetteBuilder: knitr BugReports: https://github.com/ZW-xjtlu/exomePeak2/issues git_url: https://git.bioconductor.org/packages/exomePeak2 git_branch: RELEASE_3_16 git_last_commit: ca31d66 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/exomePeak2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/exomePeak2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/exomePeak2_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/exomePeak2_1.10.0.tgz vignettes: vignettes/exomePeak2/inst/doc/Vignette_V_2.00.html vignetteTitles: The exomePeak2 user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak2/inst/doc/Vignette_V_2.00.R dependencyCount: 120 Package: ExperimentHub Version: 2.6.0 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 3.3.6), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, curl, rappdirs Suggests: knitr, BiocStyle, rmarkdown, HubPub Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: cc5c46814f9be117919fabc695236536 NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: RELEASE_3_16 git_last_commit: 557ba29 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExperimentHub_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExperimentHub_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExperimentHub_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExperimentHub_2.6.0.tgz vignettes: vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, iSEEhub, LRcell, octad, SeqSQC, alpineData, BeadSorted.Saliva.EPIC, benchmarkfdrData2019, biscuiteerData, bodymapRat, CellMapperData, clustifyrdatahub, crisprScoreData, curatedAdipoChIP, DMRcatedata, EpiMix.data, ewceData, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HiContactsData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, muscData, NanoporeRNASeq, NestLink, nullrangesData, ObMiTi, octad.db, restfulSEData, RNAmodR.Data, SCATEData, scpdata, sesameData, SimBenchData, spatialDmelxsim, STexampleData, tartare, TENxVisiumData, VectraPolarisData, WeberDivechaLCdata importsMe: BloodGen3Module, coMethDMR, DMRcate, EpiMix, epimutacions, ExperimentHubData, GSEABenchmarkeR, m6Aboost, MACSr, MethReg, methylclock, PhyloProfile, restfulSE, signatureSearch, singleCellTK, adductData, BioImageDbs, celldex, chipseqDBData, CLLmethylation, curatedMetagenomicData, curatedTBData, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, easierData, emtdata, FieldEffectCrc, GenomicDistributionsData, HarmonizedTCGAData, HCAData, HMP16SData, HMP2Data, imcdatasets, LRcellTypeMarkers, MerfishData, methylclockData, MethylSeqData, microbiomeDataSets, MouseGastrulationData, MouseThymusAgeing, msigdb, NxtIRFdata, PhyloProfileData, preciseTADhub, pwrEWAS.data, RLHub, scRNAseq, SFEData, signatureSearchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialLIBD, TabulaMurisData, TabulaMurisSenisData, TENxBrainData, TENxBUSData, TENxPBMCData, tuberculosis, xcoredata suggestsMe: ANF, AnnotationHub, bambu, celaref, CellMapper, ELMER, genomicInstability, HDF5Array, metavizr, missMethyl, MsBackendRawFileReader, muscat, nullranges, quantiseqr, rawrr, recountmethylation, SingleMoleculeFootprinting, SPOTlight, standR, TCGAbiolinks, TENxIO, Voyager, xcore, BioPlex, celarefData, curatedAdipoArray, epimutacionsData, GSE103322, GSE13015, GSE159526, GSE62944, tissueTreg dependencyCount: 95 Package: ExperimentHubData Version: 1.24.0 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.21.3) Imports: methods, ExperimentHub, BiocManager, DBI, httr, curl Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown, HubPub License: Artistic-2.0 MD5sum: 2f65f3ee496fdb31fc16ec7df76b1d51 NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: RELEASE_3_16 git_last_commit: 276f420 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExperimentHubData_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExperimentHubData_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExperimentHubData_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExperimentHubData_1.24.0.tgz vignettes: vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RNAmodR.Data importsMe: methylclockData suggestsMe: HubPub, MerfishData dependencyCount: 136 Package: ExperimentSubset Version: 1.8.0 Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment, SpatialExperiment, TreeSummarizedExperiment Imports: methods, Matrix, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran, scater, scds, TENxPBMCData, airway License: MIT + file LICENSE MD5sum: dbdbc776b2ec7e46b0a8b4ec3d008aec NeedsCompilation: no Title: Manages subsets of data with Bioconductor Experiment objects Description: Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset. biocViews: Infrastructure, Software, DataImport, DataRepresentation Author: Irzam Sarfraz [aut, cre] (), Muhammad Asif [aut, ths] (), Joshua D. Campbell [aut] () Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: RELEASE_3_16 git_last_commit: e9fa864 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExperimentSubset_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExperimentSubset_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExperimentSubset_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExperimentSubset_1.8.0.tgz vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html vignetteTitles: An introduction to ExperimentSubset class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R dependencyCount: 107 Package: ExploreModelMatrix Version: 1.10.0 Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr, magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales, tibble, MASS, limma, S4Vectors, shinyjs Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle License: MIT + file LICENSE MD5sum: 84a19578e29e250ad50b26a54395590b NeedsCompilation: no Title: Graphical Exploration of Design Matrices Description: Given a sample data table and a design formula, ExploreModelMatrix generates an interactive application for exploration of the resulting design matrix. This can be helpful for interpreting model coefficients and constructing appropriate contrasts in (generalized) linear models. Static visualizations can also be generated. biocViews: ExperimentalDesign, Regression, DifferentialExpression Author: Charlotte Soneson [aut, cre] (), Federico Marini [aut] (), Michael Love [aut] (), Florian Geier [aut] (), Michael Stadler [aut] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/ExploreModelMatrix VignetteBuilder: knitr BugReports: https://github.com/csoneson/ExploreModelMatrix/issues git_url: https://git.bioconductor.org/packages/ExploreModelMatrix git_branch: RELEASE_3_16 git_last_commit: e71d8a4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExploreModelMatrix_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExploreModelMatrix_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExploreModelMatrix_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExploreModelMatrix_1.10.0.tgz vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R dependencyCount: 87 Package: ExpressionAtlas Version: 1.26.0 Depends: R (>= 4.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2, RCurl, jsonlite, BiocStyle Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 273ee1d201e69a395000f7e5a03bcc86 NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays [aut] (), Pedro Madrigal [cre] () Maintainer: Pedro Madrigal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_16 git_last_commit: f57a05e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ExpressionAtlas_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ExpressionAtlas_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ExpressionAtlas_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ExpressionAtlas_1.26.0.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: spatialHeatmap dependencyCount: 67 Package: extraChIPs Version: 1.2.4 Depends: BiocParallel, R (>= 4.2.0), GenomicRanges, ggplot2 (>= 3.4.0), SummarizedExperiment, tibble Imports: BiocIO, broom, ComplexUpset, csaw, dplyr, edgeR, EnrichedHeatmap, forcats, GenomeInfoDb, GenomicInteractions, ggforce, ggrepel, ggside, glue, grDevices, grid, Gviz, InteractionSet, IRanges, limma, methods, patchwork, RColorBrewer, rlang, Rsamtools, rtracklayer, S4Vectors, scales, stats, stringr, tidyr, tidyselect, utils, vctrs, VennDiagram Suggests: BiocStyle, covr, knitr, plyranges, rmarkdown, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: 20d18e1b4583982a117710e5fa238cd8 NeedsCompilation: no Title: Additional functions for working with ChIP-Seq data Description: This package builds on existing tools and adds some simple but extremely useful capabilities for working with ChIP-Seq data. The focus is on detecting differential binding windows/regions. One set of functions focusses on set-operations retaining mcols for GRanges objects, whilst another group of functions are to aid visualisation of results. Coercion to tibble objects is also included. biocViews: ChIPSeq, HiC, Sequencing, Coverage Author: Stephen Pederson [aut, cre] () Maintainer: Stephen Pederson URL: https://github.com/steveped/extraChIPs VignetteBuilder: knitr BugReports: https://github.com/steveped/extraChIPs/issues git_url: https://git.bioconductor.org/packages/extraChIPs git_branch: RELEASE_3_16 git_last_commit: 52e1f70 git_last_commit_date: 2023-01-31 Date/Publication: 2023-01-31 source.ver: src/contrib/extraChIPs_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/extraChIPs_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.2/extraChIPs_1.2.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/extraChIPs_1.2.4.tgz vignettes: vignettes/extraChIPs/inst/doc/differential_binding.html, vignettes/extraChIPs/inst/doc/range_based_functions.html vignetteTitles: Differential Binding Analysis, Range-Based Operations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/extraChIPs/inst/doc/differential_binding.R, vignettes/extraChIPs/inst/doc/range_based_functions.R dependencyCount: 182 Package: fabia Version: 2.44.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) MD5sum: 3b0753dffdaf9332be82ef728ea6414a NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: RELEASE_3_16 git_last_commit: c777236 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fabia_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fabia_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fabia_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fabia_2.44.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia, RcmdrPlugin.BiclustGUI, superbiclust importsMe: miRSM, mosbi, BcDiag, CSFA suggestsMe: fabiaData dependencyCount: 7 Package: factDesign Version: 1.74.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL Archs: x64 MD5sum: ba540a009ae24b291d138ef205640b0d NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: RELEASE_3_16 git_last_commit: 741cba6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/factDesign_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/factDesign_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/factDesign_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/factDesign_1.74.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 6 Package: factR Version: 1.0.0 Depends: R (>= 4.2) Imports: BiocGenerics (>= 0.38), Biostrings (>= 2.60), GenomeInfoDb (>= 1.28), dplyr (>= 1.0), GenomicFeatures (>= 1.44), GenomicRanges (>= 1.44), IRanges (>= 2.26), purrr (>= 0.3), rtracklayer (>= 1.52), tidyr (>= 1.1), methods (>= 4.0), BiocParallel (>= 1.26), S4Vectors (>= 0.30), data.table (>= 1.14), rlang (>= 1.0), tibble (>= 3.1), wiggleplotr (>= 1.16), RCurl (>= 1.98), XML (>= 3.99), drawProteins (>= 1.12), ggplot2 (>= 3.3), stringr (>= 1.4), pbapply (>= 1.5), stats (>= 4.1), utils (>= 4.1), graphics (>= 4.1), crayon Suggests: AnnotationHub (>= 2.22), BSgenome (>= 1.58), BSgenome.Mmusculus.UCSC.mm10, testthat, knitr, rmarkdown, markdown, zeallot, rmdformats, bio3d (>= 2.4), signalHsmm (>= 1.5), tidyverse (>= 1.3), covr, patchwork License: file LICENSE MD5sum: 4f90c7ad11f87c93b01a1492a1e37cfd NeedsCompilation: no Title: Functional Annotation of Custom Transcriptomes Description: factR contain tools to process and interact with custom-assembled transcriptomes (GTF). At its core, factR constructs CDS information on custom transcripts and subsequently predicts its functional output. In addition, factR has tools capable of plotting transcripts, correcting chromosome and gene information and shortlisting new transcripts. biocViews: AlternativeSplicing, FunctionalPrediction, GenePrediction Author: Fursham Hamid [aut, cre] Maintainer: Fursham Hamid URL: https://fursham-h.github.io/factR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/factR git_branch: RELEASE_3_16 git_last_commit: aec2060 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/factR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/factR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/factR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/factR_1.0.0.tgz vignettes: vignettes/factR/inst/doc/factR.html vignetteTitles: factR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/factR/inst/doc/factR.R dependencyCount: 120 Package: FamAgg Version: 1.26.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 0dee07a5e9b0bdfd570eb92869e28611 NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: RELEASE_3_16 git_last_commit: d1bcece git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FamAgg_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FamAgg_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FamAgg_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FamAgg_1.26.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 91 Package: famat Version: 1.8.0 Depends: R (>= 4.0) Imports: KEGGREST, mgcv, stats, BiasedUrn, dplyr, gprofiler2, rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager License: GPL-3 MD5sum: ed39234859b59bb3d5e8d0f4f24e455d NeedsCompilation: no Title: Functional analysis of metabolic and transcriptomic data Description: Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process. biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO, Reactome, KEGG Author: Mathieu Charles [aut, cre] () Maintainer: Mathieu Charles URL: https://github.com/emiliesecherre/famat VignetteBuilder: knitr BugReports: https://github.com/emiliesecherre/famat/issues git_url: https://git.bioconductor.org/packages/famat git_branch: RELEASE_3_16 git_last_commit: 75bbf45 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/famat_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/famat_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/famat_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/famat_1.8.0.tgz vignettes: vignettes/famat/inst/doc/famat.html vignetteTitles: famat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/famat/inst/doc/famat.R dependencyCount: 166 Package: farms Version: 1.50.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: e154bc07ef63234d2ebdcaba01eb7fef NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html git_url: https://git.bioconductor.org/packages/farms git_branch: RELEASE_3_16 git_last_commit: a592c92 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/farms_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/farms_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/farms_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/farms_1.50.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R dependencyCount: 13 Package: fastLiquidAssociation Version: 1.34.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 38c9d0564f95b3cf8823705497a16535 NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: RELEASE_3_16 git_last_commit: d6f911c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fastLiquidAssociation_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fastLiquidAssociation_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fastLiquidAssociation_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fastLiquidAssociation_1.34.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 126 Package: FastqCleaner Version: 1.16.1 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d2d61c57b3016966083679f62f4edab0 NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching,ShinyApps Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: RELEASE_3_16 git_last_commit: 37a3010 git_last_commit_date: 2022-11-14 Date/Publication: 2022-11-14 source.ver: src/contrib/FastqCleaner_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/FastqCleaner_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/FastqCleaner_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FastqCleaner_1.16.1.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.html vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 95 Package: fastreeR Version: 1.2.0 Depends: R (>= 4.2) Imports: ape, data.table, dynamicTreeCut, methods, R.utils, rJava, stats, stringr, utils Suggests: BiocFileCache, BiocStyle, graphics, knitr, memuse, rmarkdown, spelling, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: ed3789e9e124a893ee0a864f4fb7c665 NeedsCompilation: no Title: Phylogenetic, Distance and Other Calculations on VCF and Fasta Files Description: Calculate distances, build phylogenetic trees or perform hierarchical clustering between the samples of a VCF or FASTA file. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on the VCF or FASTA file for fast execution. biocViews: Phylogenetics, Metagenomics, Clustering Author: Anestis Gkanogiannis [aut, cre] () Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/fastreeR, https://github.com/gkanogiannis/BioInfoJava-Utils SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/fastreeR/issues git_url: https://git.bioconductor.org/packages/fastreeR git_branch: RELEASE_3_16 git_last_commit: 0acbba5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fastreeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fastreeR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fastreeR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fastreeR_1.2.0.tgz vignettes: vignettes/fastreeR/inst/doc/fastreeR_vignette.html vignetteTitles: fastreeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastreeR/inst/doc/fastreeR_vignette.R dependencyCount: 27 Package: fastseg Version: 1.44.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, grDevices, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) MD5sum: 742e53847386ac1b4950cac18671540b NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer Maintainer: Alexander Blume URL: http://www.bioinf.jku.at/software/fastseg/index.html git_url: https://git.bioconductor.org/packages/fastseg git_branch: RELEASE_3_16 git_last_commit: 96cf905 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fastseg_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fastseg_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fastseg_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fastseg_1.44.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 18 Package: FCBF Version: 2.6.0 Depends: R (>= 4.1) Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment, stats, mclust Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown, testthat, BiocManager License: MIT + file LICENSE Archs: x64 MD5sum: f703ec2896ce7597244b6b92a4d8695c NeedsCompilation: no Title: Fast Correlation Based Filter for Feature Selection Description: This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. The functions are implemented based on the algorithm of Yu and Liu, 2003 and Rajarshi Guha's implementation from 13/05/2005 available (as of 26/08/2018) at http://www.rguha.net/code/R/fcbf.R . biocViews: GeneTarget, FeatureExtraction, Classification, GeneExpression, SingleCell, ImmunoOncology Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FCBF git_branch: RELEASE_3_16 git_last_commit: a07ab8c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FCBF_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FCBF_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FCBF_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FCBF_2.6.0.tgz vignettes: vignettes/FCBF/inst/doc/FCBF-Vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FCBF/inst/doc/FCBF-Vignette.R importsMe: fcoex dependencyCount: 56 Package: fCCAC Version: 1.24.0 Depends: R (>= 4.2.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 40f92c91b3f32744695923e826213be6 NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage. biocViews: Epigenetics, Transcription, Sequencing, Coverage, ChIPSeq, FunctionalGenomics, RNASeq, ATACSeq, MNaseSeq Author: Pedro Madrigal [aut, cre] () Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_16 git_last_commit: 96dac36 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fCCAC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fCCAC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fCCAC_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fCCAC_1.24.0.tgz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 126 Package: fCI Version: 1.28.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: d69351cf103efa256887b07336f30a06 NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: RELEASE_3_16 git_last_commit: 84bf818 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fCI_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fCI_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fCI_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fCI_1.28.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 53 Package: fcoex Version: 1.12.0 Depends: R (>= 4.1) Imports: FCBF, parallel, progress, dplyr, ggplot2, ggrepel, igraph, grid, intergraph, stringr, clusterProfiler, data.table, grDevices, methods, network, scales, sna, utils, stats, SingleCellExperiment, pathwayPCA, Matrix Suggests: testthat (>= 2.1.0), devtools, BiocManager, TENxPBMCData, scater, schex, gridExtra, scran, Seurat, knitr, rmarkdown License: GPL-3 MD5sum: 991b740c85ca970c5cb7d151f64a66cb NeedsCompilation: no Title: FCBF-based Co-Expression Networks for Single Cells Description: The fcoex package implements an easy-to use interface to co-expression analysis based on the FCBF (Fast Correlation-Based Filter) algorithm. it was implemented especifically to deal with single-cell data. The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. The package structure is adapted from the CEMiTool package, relying on visualizations and code designed and written by CEMiTool's authors. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcoex git_branch: RELEASE_3_16 git_last_commit: 4de9d61 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fcoex_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fcoex_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fcoex_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fcoex_1.12.0.tgz vignettes: vignettes/fcoex/inst/doc/fcoex_and_seurat.html, vignettes/fcoex/inst/doc/fcoex.html vignetteTitles: fcoex: co-expression for single-cell data integrated with Seurat, fcoex: co-expression for single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcoex/inst/doc/fcoex_and_seurat.R, vignettes/fcoex/inst/doc/fcoex.R dependencyCount: 149 Package: fcScan Version: 1.12.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges, foreach, doParallel, parallel Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: a21e23e957d67e52d469e39923943a41 NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdullah El-Kurdi [aut], Ghiwa khalil [aut], Georges Khazen [ctb], Pierre Khoueiry [aut, cre] Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: RELEASE_3_16 git_last_commit: 5c1bb30 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fcScan_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fcScan_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fcScan_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fcScan_1.12.0.tgz vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 103 Package: fdrame Version: 1.70.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) MD5sum: 696dbf3518ceecbcb973414a78bac8ad NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: RELEASE_3_16 git_last_commit: a1d2a78 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fdrame_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fdrame_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fdrame_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fdrame_1.70.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FEAST Version: 1.6.0 Depends: R (>= 4.1), mclust, BiocParallel, SummarizedExperiment Imports: SingleCellExperiment, methods, stats, utils, irlba, TSCAN, SC3, matrixStats Suggests: rmarkdown, Seurat, ggpubr, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 Archs: x64 MD5sum: 473ff100cae5da4d4a70140afa11c23f NeedsCompilation: yes Title: FEAture SelcTion (FEAST) for Single-cell clustering Description: Cell clustering is one of the most important and commonly performed tasks in single-cell RNA sequencing (scRNA-seq) data analysis. An important step in cell clustering is to select a subset of genes (referred to as “features”), whose expression patterns will then be used for downstream clustering. A good set of features should include the ones that distinguish different cell types, and the quality of such set could have significant impact on the clustering accuracy. FEAST is an R library for selecting most representative features before performing the core of scRNA-seq clustering. It can be used as a plug-in for the etablished clustering algorithms such as SC3, TSCAN, SHARP, SIMLR, and Seurat. The core of FEAST algorithm includes three steps: 1. consensus clustering; 2. gene-level significance inference; 3. validation of an optimized feature set. biocViews: Sequencing, SingleCell, Clustering, FeatureExtraction Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr BugReports: https://github.com/suke18/FEAST/issues git_url: https://git.bioconductor.org/packages/FEAST git_branch: RELEASE_3_16 git_last_commit: 7d99b9d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FEAST_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FEAST_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FEAST_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FEAST_1.6.0.tgz vignettes: vignettes/FEAST/inst/doc/FEAST.html vignetteTitles: The FEAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEAST/inst/doc/FEAST.R dependencyCount: 118 Package: fedup Version: 1.6.0 Depends: R (>= 4.1) Imports: openxlsx, tibble, dplyr, data.table, ggplot2, ggthemes, forcats, RColorBrewer, RCy3, utils, stats Suggests: biomaRt, tidyr, testthat, knitr, rmarkdown, devtools, covr License: MIT + file LICENSE Archs: x64 MD5sum: a7a9f5314c3a19e37117a9a6466ed309 NeedsCompilation: no Title: Fisher's Test for Enrichment and Depletion of User-Defined Pathways Description: An R package that tests for enrichment and depletion of user-defined pathways using a Fisher's exact test. The method is designed for versatile pathway annotation formats (eg. gmt, txt, xlsx) to allow the user to run pathway analysis on custom annotations. This package is also integrated with Cytoscape to provide network-based pathway visualization that enhances the interpretability of the results. biocViews: GeneSetEnrichment, Pathways, NetworkEnrichment, Network Author: Catherine Ross [aut, cre] Maintainer: Catherine Ross URL: https://github.com/rosscm/fedup VignetteBuilder: knitr BugReports: https://github.com/rosscm/fedup/issues git_url: https://git.bioconductor.org/packages/fedup git_branch: RELEASE_3_16 git_last_commit: 2859a7a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fedup_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fedup_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fedup_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fedup_1.6.0.tgz vignettes: vignettes/fedup/inst/doc/fedup_doubleTest.html, vignettes/fedup/inst/doc/fedup_mutliTest.html, vignettes/fedup/inst/doc/fedup_singleTest.html vignetteTitles: fedup_doubleTest.html, fedup_mutliTest.html, fedup_singleTest.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/fedup/inst/doc/fedup_doubleTest.R, vignettes/fedup/inst/doc/fedup_mutliTest.R, vignettes/fedup/inst/doc/fedup_singleTest.R dependencyCount: 83 Package: FELLA Version: 1.18.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: e8d98d7a33791934d19d2eebb85daeda NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: RELEASE_3_16 git_last_commit: 6990f18 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FELLA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FELLA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FELLA_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FELLA_1.18.0.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 38 Package: ffpe Version: 1.42.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 3c382d38f15d52056b1a187351c92425 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron git_url: https://git.bioconductor.org/packages/ffpe git_branch: RELEASE_3_16 git_last_commit: 980b951 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ffpe_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ffpe_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ffpe_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ffpe_1.42.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R dependencyCount: 165 Package: fgga Version: 1.6.0 Depends: R (>= 4.2), RBGL Imports: graph, stats, e1071, methods, gRbase, jsonlite, BiocFileCache, curl Suggests: knitr, rmarkdown, GOstats, GO.db, BiocGenerics, pROC, RUnit License: GPL-3 MD5sum: 04686fdcbc15d0e1aeaff8ef2dd1f506 NeedsCompilation: no Title: Hierarchical ensemble method based on factor graph Description: Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models. biocViews: Software, StatisticalMethod, Classification, Network, NetworkInference, SupportVectorMachine, GraphAndNetwork, GO Author: Flavio Spetale [aut, cre] Maintainer: Flavio Spetale URL: https://github.com/fspetale/fgga VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fgga git_branch: RELEASE_3_16 git_last_commit: 2da7911 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fgga_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fgga_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fgga_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fgga_1.6.0.tgz vignettes: vignettes/fgga/inst/doc/fgga.html vignetteTitles: FGGA: Factor Graph GO Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgga/inst/doc/fgga.R dependencyCount: 66 Package: FGNet Version: 3.32.0 Depends: R (>= 4.2.0) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png, methods, stats, utils, graphics, grDevices Suggests: RCurl, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, BiocManager License: GPL (>= 2) MD5sum: b956ee3f3a91f134a72306569866e427 NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: RELEASE_3_16 git_last_commit: 3298df0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FGNet_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FGNet_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FGNet_3.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FGNet_3.32.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 31 Package: fgsea Version: 1.24.0 Depends: R (>= 3.3) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), cowplot, grid, fastmatch, Matrix, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery, msigdbr, aggregation License: MIT + file LICENCE MD5sum: 37e277c0b49d3a5f3a63eb4b67198f6f NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin [ctb], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: RELEASE_3_16 git_last_commit: ac74ccd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fgsea_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fgsea_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fgsea_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fgsea_1.24.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html, vignettes/fgsea/inst/doc/geseca-tutorial.html vignetteTitles: Using fgsea package, geseca-tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R, vignettes/fgsea/inst/doc/geseca-tutorial.R dependsOnMe: gsean, metapone, PPInfer importsMe: ASpediaFI, ATACCoGAPS, BioNAR, CelliD, CEMiTool, clustifyr, cTRAP, DOSE, EventPointer, fobitools, lipidr, mCSEA, multiGSEA, NanoTube, omicsViewer, phantasus, piano, RegEnrich, signatureSearch, ViSEAGO, cinaR, DTSEA, scITD suggestsMe: Cepo, decoupleR, gCrisprTools, mdp, pareg, Pi, sparrow, ttgsea, genekitr, grandR, Platypus, rliger dependencyCount: 49 Package: FilterFFPE Version: 1.8.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 Archs: x64 MD5sum: 3972408b3f471a3b2a1f3ef92f216dba NeedsCompilation: no Title: FFPE Artificial Chimeric Read Filter for NGS data Description: This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample. biocViews: StructuralVariation, Sequencing, Alignment, QualityControl, Preprocessing Author: Lanying Wei [aut, cre] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: RELEASE_3_16 git_last_commit: 8561ba8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FilterFFPE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FilterFFPE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FilterFFPE_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FilterFFPE_1.8.0.tgz vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf vignetteTitles: An introduction to FilterFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R dependencyCount: 34 Package: FindIT2 Version: 1.4.0 Depends: GenomicRanges, R (>= 3.5.0) Imports: withr, BiocGenerics, GenomeInfoDb, rtracklayer, S4Vectors, GenomicFeatures, dplyr, rlang, patchwork, ggplot2, BiocParallel, qvalue, stringr, utils, stats, ggrepel, tibble, tidyr, SummarizedExperiment, MultiAssayExperiment, IRanges, progress, purrr, glmnet, methods Suggests: BiocStyle, knitr, rmarkdown, sessioninfo, testthat (>= 3.0.0), TxDb.Athaliana.BioMart.plantsmart28 License: Artistic-2.0 MD5sum: 38324cdd6b7b7472f1760b673cff6afc NeedsCompilation: no Title: find influential TF and Target based on multi-omics data Description: This package implements functions to find influential TF and target based on different input type. It have five module: Multi-peak multi-gene annotaion(mmPeakAnno module), Calculate regulation potential(calcRP module), Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module), Find influential TF based on different input(Find influential TF module), Calculate peak-gene or peak-peak correlation(peakGeneCor module). And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF. biocViews: Software, Annotation, ChIPSeq, ATACSeq, GeneRegulation, MultipleComparison, GeneTarget Author: Guandong Shang [aut, cre] () Maintainer: Guandong Shang URL: https://github.com/shangguandong1996/FindIT2 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/FindIT2 git_url: https://git.bioconductor.org/packages/FindIT2 git_branch: RELEASE_3_16 git_last_commit: 081e7ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FindIT2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FindIT2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FindIT2_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FindIT2_1.4.0.tgz vignettes: vignettes/FindIT2/inst/doc/FindIT2.html vignetteTitles: Introduction to FindIT2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindIT2/inst/doc/FindIT2.R dependencyCount: 124 Package: FISHalyseR Version: 1.32.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: b01ff39be83b70873833243630128e03 NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: RELEASE_3_16 git_last_commit: 14e0fc4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FISHalyseR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FISHalyseR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FISHalyseR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FISHalyseR_1.32.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 50 Package: fishpond Version: 2.4.1 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, IRanges, SummarizedExperiment, GenomicRanges, matrixStats, svMisc, Matrix, SingleCellExperiment, jsonlite Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, limma, ensembldb, EnsDb.Hsapiens.v86, GenomicFeatures, AnnotationDbi, pheatmap, Gviz, GenomeInfoDb, data.table License: GPL-2 MD5sum: 8d2465f38ef496a08732fd304262b4b9 NeedsCompilation: no Title: Fishpond: downstream methods and tools for expression data Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains a number of utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anqi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Euphy Wu [ctb], Noor Pratap Singh [ctb], Scott Van Buren [ctb], Dongze He [ctb], Steve Lianoglou [ctb], Wes Wilson [ctb], Jeroen Gilis [ctb] Maintainer: Michael Love URL: https://mikelove.github.io/fishpond, https://github.com/mikelove/fishpond VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/fishpond git_url: https://git.bioconductor.org/packages/fishpond git_branch: RELEASE_3_16 git_last_commit: 92e0c13 git_last_commit_date: 2023-01-23 Date/Publication: 2023-01-24 source.ver: src/contrib/fishpond_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/fishpond_2.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/fishpond_2.4.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fishpond_2.4.1.tgz vignettes: vignettes/fishpond/inst/doc/allelic.html, vignettes/fishpond/inst/doc/swish.html vignetteTitles: 2. SEESAW - Allelic expression analysis with Salmon and Swish, 1. Swish: DE analysis accounting for inferential uncertainty hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/allelic.R, vignettes/fishpond/inst/doc/swish.R importsMe: singleCellTK suggestsMe: tximeta dependencyCount: 63 Package: FitHiC Version: 1.24.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: cb43eee3f381868b27ec04d8011884d7 NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: RELEASE_3_16 git_last_commit: 1546070 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FitHiC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FitHiC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FitHiC_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FitHiC_1.24.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flagme Version: 1.54.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: x64 MD5sum: 61eeef8f8bf05e52871aed22768e8fed NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography-massspectrometry metabolomics data. biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_16 git_last_commit: 10b6eec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flagme_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flagme_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flagme_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flagme_1.54.0.tgz vignettes: vignettes/flagme/inst/doc/flagme-knitr.pdf, vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data, \texttt{flagme}: Fragment-level analysis of \\ GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme-knitr.R, vignettes/flagme/inst/doc/flagme.R dependencyCount: 139 Package: FLAMES Version: 1.4.3 Imports: basilisk, bambu, Biostrings, BiocGenerics, circlize, ComplexHeatmap, cowplot, dplyr, DropletUtils, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, ggbio, grid, gridExtra, igraph, jsonlite, magrittr, Matrix, parallel, reticulate, Rsamtools, rtracklayer, RColorBrewer, SingleCellExperiment, SummarizedExperiment, scater, S4Vectors, scuttle, stats, scran, stringr, MultiAssayExperiment, tidyr, utils, withr, zlibbioc, LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, GEOquery, knitr, rmarkdown, markdown, BiocFileCache, R.utils, ShortRead, uwot, testthat (>= 3.0.0) License: GPL (>= 2) Archs: x64 MD5sum: 2cb9449a0e23ba2971d7175e8a4958b0 NeedsCompilation: yes Title: FLAMES: Full Length Analysis of Mutations and Splicing in long read RNA-seq data Description: Semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution. biocViews: RNASeq, SingleCell, Transcriptomics, DataImport, DifferentialSplicing, AlternativeSplicing, GeneExpression Author: Tian Luyi [aut], Voogd Oliver [aut, cre], Schuster Jakob [aut], Wang Changqing [aut], Su Shian [aut], Ritchie Matthew [ctb] Maintainer: Voogd Oliver URL: https://github.com/OliverVoogd/FLAMES SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FLAMES git_branch: RELEASE_3_16 git_last_commit: 0d09bcc git_last_commit_date: 2023-02-14 Date/Publication: 2023-02-15 source.ver: src/contrib/FLAMES_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/FLAMES_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.2/FLAMES_1.4.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FLAMES_1.4.3.tgz vignettes: vignettes/FLAMES/inst/doc/FLAMES_vignette.html vignetteTitles: FLAMES hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FLAMES/inst/doc/FLAMES_vignette.R dependencyCount: 226 Package: flowAI Version: 1.28.0 Depends: R (>= 3.6) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny, BiocStyle License: GPL (>= 2) Archs: x64 MD5sum: 963544898f16672a580c8c7e87829231 NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: RELEASE_3_16 git_last_commit: 2754096 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowAI_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowAI_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowAI_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowAI_1.28.0.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R dependencyCount: 76 Package: flowBeads Version: 1.36.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: d8c9b095aa6fc139fd07c21ee4faefa3 NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: RELEASE_3_16 git_last_commit: 3efe223 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowBeads_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowBeads_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowBeads_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowBeads_1.36.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 31 Package: flowBin Version: 1.34.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 113e74d35b74596e0c74bcfc31ffc13e NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: RELEASE_3_16 git_last_commit: d815274 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowBin_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowBin_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowBin_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowBin_1.34.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 36 Package: flowcatchR Version: 1.32.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 3d936fd21353498347aab0feeab24631 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: RELEASE_3_16 git_last_commit: 8968ea3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowcatchR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowcatchR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowcatchR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowcatchR_1.32.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 105 Package: flowCHIC Version: 1.32.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 1a1423c88a0f1967b2e8490cf85eea52 NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: RELEASE_3_16 git_last_commit: c9ba335 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowCHIC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCHIC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCHIC_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowCHIC_1.32.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 86 Package: flowCL Version: 1.35.0 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 44bac9a5839ae8754c782b2dbbc15629 NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry, ImmunoOncology Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas git_url: https://git.bioconductor.org/packages/flowCL git_branch: master git_last_commit: ac50eb1 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/flowCL_1.35.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCL_1.35.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCL_1.35.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: flowClean Version: 1.36.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 880c4b1aafaf6116571bb449e16e0158 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: RELEASE_3_16 git_last_commit: 343fb73 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowClean_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowClean_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowClean_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowClean_1.36.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 24 Package: flowClust Version: 3.36.1 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, flowCore, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto, flowStats(>= 4.7.1) License: MIT MD5sum: 4cfc7d8ea7b0168c1d77ba35eda094cd NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo, Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: RELEASE_3_16 git_last_commit: 6db19fb git_last_commit_date: 2023-03-23 Date/Publication: 2023-03-24 source.ver: src/contrib/flowClust_3.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowClust_3.36.1.zip mac.binary.ver: bin/macosx/contrib/4.2/flowClust_3.36.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowClust_3.36.1.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: cyanoFilter, flowTrans suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 19 Package: flowCore Version: 2.10.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 2.3.4), S4Vectors LinkingTo: cpp11, BH(>= 1.65.0.1), cytolib, RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 Archs: x64 MD5sum: 99d7336ef628e6fdd738ab402256c89c NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: RELEASE_3_16 git_last_commit: 2bb3556 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowCore_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCore_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCore_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowCore_2.10.0.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, infinityFlow, ncdfFlow, HDCytoData, healthyFlowData, highthroughputassays importsMe: CATALYST, cmapR, cyanoFilter, cydar, cytoMEM, CytoML, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowStats, flowTrans, flowViz, flowWorkspace, GateFinder, ImmuneSpaceR, MetaCyto, oneSENSE, PeacoQC, scDataviz, scifer, Sconify suggestsMe: COMPASS, flowPloidyData, beadplexr, hypergate, segmenTier dependencyCount: 16 Package: flowCut Version: 1.8.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr, markdown, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 1fd2359cf57c55a553f2bd9d62f555ad NeedsCompilation: no Title: Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis Description: Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments. biocViews: FlowCytometry, Preprocessing, QualityControl, CellBasedAssays Author: Justin Meskas [cre, aut], Sherrie Wang [aut] Maintainer: Justin Meskas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: RELEASE_3_16 git_last_commit: c1e912c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowCut_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCut_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCut_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowCut_1.8.0.tgz vignettes: vignettes/flowCut/inst/doc/flowCut.html vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCut/inst/doc/flowCut.R dependencyCount: 135 Package: flowCyBar Version: 1.34.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 5cd2e7c69f7f2feea82a7d55d9a3281e NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: RELEASE_3_16 git_last_commit: 0512d83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowCyBar_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowCyBar_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowCyBar_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowCyBar_1.34.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 20 Package: flowDensity Version: 1.32.0 Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats, grDevices Suggests: knitr,rmarkdown License: Artistic-2.0 MD5sum: 952a56806427a23ad1d916a6da3c9f04 NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: RELEASE_3_16 git_last_commit: 74d191c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowDensity_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowDensity_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowDensity_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowDensity_1.32.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensity.html vignetteTitles: Introduction to automated gating hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: cyanoFilter, ddPCRclust, flowCut dependencyCount: 130 Package: flowFP Version: 1.56.3 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 MD5sum: 19693e8d4165b98c681e1adc829806e5 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst , Wade Rogers git_url: https://git.bioconductor.org/packages/flowFP git_branch: RELEASE_3_16 git_last_commit: 047efef git_last_commit_date: 2022-11-15 Date/Publication: 2022-11-16 source.ver: src/contrib/flowFP_1.56.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowFP_1.56.3.zip mac.binary.ver: bin/macosx/contrib/4.2/flowFP_1.56.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowFP_1.56.3.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 32 Package: flowGraph Version: 1.6.0 Depends: R (>= 4.1) Imports: effsize, furrr, future, purrr, ggiraph, ggrepel, ggplot2, igraph, Matrix, matrixStats, stats, utils, visNetwork, htmlwidgets, grDevices, methods, stringr, stringi, Rdpack, data.table (>= 1.9.5), gridExtra, Suggests: BiocStyle, dplyr, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 Archs: x64 MD5sum: ca3c410472371c976e9d0f9053a74e9b NeedsCompilation: no Title: Identifying differential cell populations in flow cytometry data accounting for marker frequency Description: Identifies maximal differential cell populations in flow cytometry data taking into account dependencies between cell populations; flowGraph calculates and plots SpecEnr abundance scores given cell population cell counts. biocViews: FlowCytometry, StatisticalMethod, ImmunoOncology, Software, CellBasedAssays, Visualization Author: Alice Yue [aut, cre] Maintainer: Alice Yue URL: https://github.com/aya49/flowGraph VignetteBuilder: knitr BugReports: https://github.com/aya49/flowGraph/issues git_url: https://git.bioconductor.org/packages/flowGraph git_branch: RELEASE_3_16 git_last_commit: d0d96a7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowGraph_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowGraph_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowGraph_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowGraph_1.6.0.tgz vignettes: vignettes/flowGraph/inst/doc/flowGraph.html vignetteTitles: flowGraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowGraph/inst/doc/flowGraph.R dependencyCount: 83 Package: flowMap Version: 1.36.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: 7ae50f8906ce6125a68b9063d5bde05b NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: ImmunoOncology, MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMap git_branch: RELEASE_3_16 git_last_commit: a999c7c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowMap_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMap_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMap_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowMap_1.36.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R dependencyCount: 39 Package: flowMatch Version: 1.34.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 MD5sum: 0cd3ce3d9d540276f1a898a67db45912 NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: RELEASE_3_16 git_last_commit: 294abce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowMatch_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMatch_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMatch_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowMatch_1.34.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 17 Package: flowMeans Version: 1.58.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 Archs: x64 MD5sum: 795b8d4d8018030f6ac50e5b221767df NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: RELEASE_3_16 git_last_commit: a06d19b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowMeans_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMeans_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMeans_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowMeans_1.58.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 39 Package: flowMerge Version: 2.46.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 83ff08da04abc47e77f4abdea2204352 NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: RELEASE_3_16 git_last_commit: 19342f2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowMerge_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowMerge_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowMerge_2.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowMerge_2.46.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R suggestsMe: segmenTier dependencyCount: 47 Package: flowPeaks Version: 1.44.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 MD5sum: 41ed6b2b0dfe9d2c59b26025e441a204 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: RELEASE_3_16 git_last_commit: e7a4e64 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowPeaks_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowPeaks_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowPeaks_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowPeaks_1.44.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust dependencyCount: 0 Package: flowPloidy Version: 1.24.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 Archs: x64 MD5sum: a7de42c8ef95740b57b180c4e6a1697e NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: RELEASE_3_16 git_last_commit: 245c596 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowPloidy_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowPloidy_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowPloidy_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowPloidy_1.24.0.tgz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 115 Package: flowPlots Version: 1.46.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: d669fc443f8e16f07beb2ec0ab92b4ba NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: RELEASE_3_16 git_last_commit: f7a4eeb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowPlots_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowPlots_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowPlots_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowPlots_1.46.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowSOM Version: 2.6.0 Depends: R (>= 4.0), igraph Imports: stats, utils, colorRamps, ConsensusClusterPlus, dplyr, flowCore, ggforce, ggnewscale, ggplot2, ggpubr, grDevices, magrittr, methods, rlang, Rtsne, tidyr, BiocGenerics, XML Suggests: BiocStyle, testthat, CytoML, flowWorkspace, ggrepel, scattermore, pheatmap, ggpointdensity License: GPL (>= 2) MD5sum: a2f46b1c88174605c22c98b2103d7cee NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen [aut, cre], Artuur Couckuyt [aut], Katrien Quintelier [aut], Annelies Emmaneel [aut], Britt Callebaut [aut], Yvan Saeys [aut] Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: RELEASE_3_16 git_last_commit: 3783b6c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FlowSOM_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FlowSOM_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FlowSOM_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FlowSOM_2.6.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, diffcyt suggestsMe: HDCytoData dependencyCount: 112 Package: flowSpecs Version: 1.12.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE Archs: x64 MD5sum: 2cf3ac4b436dddd4394ce557c75d9a5d NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization,Normalization,DataImport Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: RELEASE_3_16 git_last_commit: 29ad279 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowSpecs_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowSpecs_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowSpecs_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowSpecs_1.12.0.tgz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 61 Package: flowStats Version: 4.10.0 Depends: R (>= 3.0.2) Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov, corpcor, mnormt Suggests: xtable, testthat, openCyto, ggcyto, ggridges Enhances: RBGL,graph License: Artistic-2.0 MD5sum: b4abf02b574012875424458cb8d153e5 NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak , Mike Jiang URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: RELEASE_3_16 git_last_commit: 19e0ce5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowStats_4.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowStats_4.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowStats_4.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowStats_4.10.0.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS, highthroughputassays suggestsMe: cydar, flowClust, flowCore, flowViz, ggcyto, openCyto dependencyCount: 98 Package: flowTime Version: 1.22.2 Depends: R (>= 3.4), flowCore Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang Suggests: knitr, rmarkdown, ggplot2, BiocGenerics, stats, flowClust License: Artistic-2.0 MD5sum: d9a0c1f89c1cc0485809b359cc6224ee NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: RELEASE_3_16 git_last_commit: a61d082 git_last_commit_date: 2022-12-06 Date/Publication: 2022-12-06 source.ver: src/contrib/flowTime_1.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowTime_1.22.2.zip mac.binary.ver: bin/macosx/contrib/4.2/flowTime_1.22.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowTime_1.22.2.tgz vignettes: vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 34 Package: flowTrans Version: 1.50.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: e7e0b2e4efe10276a6860b2577881e03 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: RELEASE_3_16 git_last_commit: 69b1827 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowTrans_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowTrans_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowTrans_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowTrans_1.50.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 35 Package: flowViz Version: 1.62.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, rmarkdown, markdown, testthat License: Artistic-2.0 MD5sum: 6a49c31044f9ca71be913c31fde0b248 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_16 git_last_commit: 0ef368f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowViz_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowViz_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowViz_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowViz_1.62.0.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowDensity, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, ggcyto dependencyCount: 31 Package: flowVS Version: 1.30.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 9df3dc083f106a0e963c7e6a1fcc7fc4 NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: RELEASE_3_16 git_last_commit: 771cdbc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/flowVS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowVS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/flowVS_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowVS_1.30.0.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R dependencyCount: 99 Package: flowWorkspace Version: 4.10.1 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.3.9), XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, scales, matrixStats, RProtoBufLib, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4), DelayedArray, S4Vectors LinkingTo: cpp11, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.3.7),Rhdf5lib Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, rmarkdown, ggcyto, parallel, CytoML, openCyto License: AGPL-3.0-only License_restricts_use: no MD5sum: c3bf8a69c2659fe56f9643026fd3cc6b NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: RELEASE_3_16 git_last_commit: af121c6 git_last_commit_date: 2022-12-28 Date/Publication: 2022-12-29 source.ver: src/contrib/flowWorkspace_4.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/flowWorkspace_4.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/flowWorkspace_4.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/flowWorkspace_4.10.1.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R dependsOnMe: ggcyto, highthroughputassays importsMe: CytoML, flowDensity, flowStats, ImmuneSpaceR, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore, FlowSOM linksToMe: CytoML dependencyCount: 61 Package: fmcsR Version: 1.40.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap,rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 5049f2dd0a203e32da67e0c5c2d5c06b NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: RELEASE_3_16 git_last_commit: 980b63e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fmcsR_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fmcsR_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fmcsR_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fmcsR_1.40.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: chemodiv suggestsMe: ChemmineR, xnet dependencyCount: 79 Package: fmrs Version: 1.8.0 Depends: R (>= 4.1.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL-3 Archs: x64 MD5sum: edc06e391710c1670de1721d830779f9 NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Models Description: The package obtains parameter estimation, i.e., maximum likelihood estimators (MLE), via the Expectation-Maximization (EM) algorithm for the Finite Mixture of Regression (FMR) models with Normal distribution, and MLE for the Finite Mixture of Accelerated Failure Time Regression (FMAFTR) subject to right censoring with Log-Normal and Weibull distributions via the EM algorithm and the Newton-Raphson algorithm (for Weibull distribution). More importantly, the package obtains the maximum penalized likelihood (MPLE) for both FMR and FMAFTR models (collectively called FMRs). A component-wise tuning parameter selection based on a component-wise BIC is implemented in the package. Furthermore, this package provides Ridge Regression and Elastic Net. biocViews: Survival, Regression, DimensionReduction Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: RELEASE_3_16 git_last_commit: a96fb6e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fmrs_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fmrs_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fmrs_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fmrs_1.8.0.tgz vignettes: vignettes/fmrs/inst/doc/usingfmrs.html vignetteTitles: Using fmrs package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R importsMe: HhP dependencyCount: 10 Package: fobitools Version: 1.6.0 Depends: R (>= 4.1) Imports: clisymbols, crayon, dplyr, fgsea, ggplot2, ggraph, magrittr, ontologyIndex, purrr, RecordLinkage, stringr, textclean, tictoc, tidygraph, tidyr, vroom Suggests: BiocStyle, covr, ggrepel, kableExtra, knitr, metabolomicsWorkbenchR, POMA, rmarkdown, rvest, SummarizedExperiment, testthat (>= 2.3.2), tidyverse License: GPL-3 Archs: x64 MD5sum: aa300176fb8fee2c5592a9373ff76345 NeedsCompilation: no Title: Tools For Manipulating FOBI Ontology Description: A set of tools for interacting with Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data. biocViews: MassSpectrometry, Metabolomics, Software, Visualization, BiomedicalInformatics, GraphAndNetwork, Annotation, Cheminformatics, Pathways, GeneSetEnrichment Author: Pol Castellano-Escuder [aut, cre] (), Cristina Andrés-Lacueva [aut] (), Alex Sánchez-Pla [aut] () Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/fobitools/ VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/fobitools/issues git_url: https://git.bioconductor.org/packages/fobitools git_branch: RELEASE_3_16 git_last_commit: f4b93f2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/fobitools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/fobitools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/fobitools_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/fobitools_1.6.0.tgz vignettes: vignettes/fobitools/inst/doc/Dietary_data_annotation.html, vignettes/fobitools/inst/doc/food_enrichment_analysis.html, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.html, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.html vignetteTitles: Dietary text annotation, Simple food ORA, Use case ST000291, Use case ST000629 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fobitools/inst/doc/Dietary_data_annotation.R, vignettes/fobitools/inst/doc/food_enrichment_analysis.R, vignettes/fobitools/inst/doc/MW_ST000291_enrichment.R, vignettes/fobitools/inst/doc/MW_ST000629_enrichment.R dependencyCount: 126 Package: FoldGO Version: 1.16.0 Depends: R (>= 4.0) Imports: topGO (>= 2.30.1), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), stats, methods Suggests: knitr, rmarkdown, devtools, kableExtra License: GPL-3 MD5sum: 7a2afedbc5428ea48f0f86bc2e05312a NeedsCompilation: no Title: Package for Fold-specific GO Terms Recognition Description: FoldGO is a package designed to annotate gene sets derived from expression experiments and identify fold-change-specific GO terms. biocViews: DifferentialExpression, GeneExpression, GO, Software Author: Daniil Wiebe [aut, cre] Maintainer: Daniil Wiebe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FoldGO git_branch: RELEASE_3_16 git_last_commit: 9d392e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FoldGO_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FoldGO_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FoldGO_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FoldGO_1.16.0.tgz vignettes: vignettes/FoldGO/inst/doc/vignette.html vignetteTitles: FoldGO: a tool for fold-change-specific functional enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FoldGO/inst/doc/vignette.R dependencyCount: 81 Package: FRASER Version: 1.10.2 Depends: BiocParallel, data.table, Rsamtools, SummarizedExperiment Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt, BSgenome, cowplot, DelayedArray (>= 0.5.11), DelayedMatrixStats, extraDistr, generics, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods, OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer, rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools, utils, VGAM LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE MD5sum: 636b41c1d73d8d59e45376736913cd50 NeedsCompilation: yes Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. Read count ratio expectations are modeled by an autoencoder to control for confounding factors in the data. Given these expectations, the ratios are assumed to follow a beta-binomial distribution with a junction specific dispersion. Outlier events are then identified as read-count ratios that deviate significantly from this distribution. FRASER is able to detect alternative splicing, but also intron retention. The package aims to support diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects. biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics, Coverage Author: Christian Mertes [aut, cre], Ines Scheller [aut], Vicente Yepez [ctb], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FRASER git_branch: RELEASE_3_16 git_last_commit: 411304e git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/FRASER_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/FRASER_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/FRASER_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FRASER_1.10.2.tgz vignettes: vignettes/FRASER/inst/doc/FRASER.pdf vignetteTitles: FRASER: Find RAre Splicing Evens in RNA-seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FRASER/inst/doc/FRASER.R dependencyCount: 182 Package: frenchFISH Version: 1.10.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: c321cd5a6d5a058187e136e35a8fcfe5 NeedsCompilation: no Title: Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections Description: FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, HiddenMarkovModel, Preprocessing Author: Adam Berman, Geoff Macintyre Maintainer: Adam Berman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: RELEASE_3_16 git_last_commit: 61f827b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/frenchFISH_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/frenchFISH_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/frenchFISH_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/frenchFISH_1.10.0.tgz vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html vignetteTitles: Correcting FISH probe counts with frenchFISH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R dependencyCount: 83 Package: FRGEpistasis Version: 1.34.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: e0b30a5d8c3d78473741c303ec5baebb NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: RELEASE_3_16 git_last_commit: 665320b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FRGEpistasis_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FRGEpistasis_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FRGEpistasis_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FRGEpistasis_1.34.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 58 Package: frma Version: 1.50.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: 33285e71ae493c2296d35ea957fb302a NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: RELEASE_3_16 git_last_commit: 82c112c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/frma_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/frma_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/frma_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/frma_1.50.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013 suggestsMe: frmaTools, antiProfilesData dependencyCount: 56 Package: frmaTools Version: 1.50.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: 9f0b393cdd57ad288138cbe274e7d946 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: RELEASE_3_16 git_last_commit: 21593ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/frmaTools_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/frmaTools_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/frmaTools_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/frmaTools_1.50.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R importsMe: DeSousa2013 dependencyCount: 13 Package: FScanR Version: 1.8.0 Depends: R (>= 4.0) Imports: stats Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 9eea5e3aea089f3e89d9ac5e18687515 NeedsCompilation: no Title: Detect Programmed Ribosomal Frameshifting Events from mRNA/cDNA BLASTX Output Description: 'FScanR' identifies Programmed Ribosomal Frameshifting (PRF) events from BLASTX homolog sequence alignment between targeted genomic/cDNA/mRNA sequences against the peptide library of the same species or a close relative. The output by BLASTX or diamond BLASTX will be used as input of 'FScanR' and should be in a tabular format with 14 columns. For BLASTX, the output parameter should be: -outfmt '6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe sframe'. For diamond BLASTX, the output parameter should be: -outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe qframe. biocViews: Alignment, Annotation, Software Author: Xiao Chen [aut, cre] () Maintainer: Xiao Chen VignetteBuilder: knitr BugReports: https://github.com/seanchen607/FScanR/issues git_url: https://git.bioconductor.org/packages/FScanR git_branch: RELEASE_3_16 git_last_commit: 60ea61e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FScanR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FScanR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FScanR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FScanR_1.8.0.tgz vignettes: vignettes/FScanR/inst/doc/FScanR.html vignetteTitles: FScanR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FScanR/inst/doc/FScanR.R dependencyCount: 1 Package: FunChIP Version: 1.24.0 Depends: R (>= 3.5.0), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 MD5sum: 5640ba1fc069dd80849327c34befc7a6 NeedsCompilation: yes Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes Description: Preprocessing and smoothing of ChIP-Seq peaks and efficient implementation of the k-mean alignment algorithm to classify them. biocViews: StatisticalMethod, Clustering, ChIPSeq Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M. Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut] Maintainer: Alice Parodi git_url: https://git.bioconductor.org/packages/FunChIP git_branch: RELEASE_3_16 git_last_commit: 88132da git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/FunChIP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/FunChIP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/FunChIP_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FunChIP_1.24.0.tgz vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf vignetteTitles: An introduction to FunChIP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R dependencyCount: 113 Package: funtooNorm Version: 1.22.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: d6e884ad73297602d50d66e42e48fdc4 NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: RELEASE_3_16 git_last_commit: ed7339f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/funtooNorm_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/funtooNorm_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/funtooNorm_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/funtooNorm_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 143 Package: FuseSOM Version: 1.0.1 Depends: R (>= 4.2.0) Imports: psych, FCPS, analogue, coop, pheatmap, ggplotify, fastcluster, fpc, ggplot2, stringr, ggpubr, proxy, cluster, diptest, methods, SummarizedExperiment, stats, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: 3d94408e2d1fc717ef6f306cbad315a6 NeedsCompilation: yes Title: A Correlation Based Multiview Self Organizing Maps Clustering For IMC Datasets Description: A correlation-based multiview self-organizing map for the characterization of cell types in highly multiplexed in situ imaging cytometry assays (`FuseSOM`) is a tool for unsupervised clustering. `FuseSOM` is robust and achieves high accuracy by combining a `Self Organizing Map` architecture and a `Multiview` integration of correlation based metrics. This allows FuseSOM to cluster highly multiplexed in situ imaging cytometry assays. biocViews: SingleCell, CellBasedAssays, Clustering, Spatial Author: Elijah Willie [aut, cre] Maintainer: Elijah Willie VignetteBuilder: knitr BugReports: https://github.com/ecool50/FuseSOM/issues git_url: https://git.bioconductor.org/packages/FuseSOM git_branch: RELEASE_3_16 git_last_commit: b3abd22 git_last_commit_date: 2022-11-22 Date/Publication: 2022-11-22 source.ver: src/contrib/FuseSOM_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/FuseSOM_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/FuseSOM_1.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/FuseSOM_1.0.1.tgz vignettes: vignettes/FuseSOM/inst/doc/Introduction.html vignetteTitles: FuseSOM package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FuseSOM/inst/doc/Introduction.R dependencyCount: 139 Package: GA4GHclient Version: 1.22.0 Depends: R (>= 3.5.0), S4Vectors Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: x64 MD5sum: f8e2ccfee5a5ec4ef6d69c5672287881 NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: RELEASE_3_16 git_last_commit: f24ce9b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GA4GHclient_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GA4GHclient_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GA4GHclient_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GA4GHclient_1.22.0.tgz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 98 Package: GA4GHshiny Version: 1.20.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 5accad4942c44d4dc89f807bd50e4126 NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: RELEASE_3_16 git_last_commit: 65ab853 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GA4GHshiny_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GA4GHshiny_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GA4GHshiny_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GA4GHshiny_1.20.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 129 Package: gaga Version: 2.44.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) MD5sum: 744126f64436444fccea8176eeef46ce NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: RELEASE_3_16 git_last_commit: 1229b55 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gaga_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gaga_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gaga_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gaga_2.44.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 16 Package: gage Version: 2.48.0 Depends: R (>= 3.5.0) Imports: graph, KEGGREST, AnnotationDbi, GO.db Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma License: GPL (>=2.0) Archs: x64 MD5sum: 9bce542740c670a1cd4f3ba1aa39fcd3 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/gage, http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: RELEASE_3_16 git_last_commit: d067e0a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gage_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gage_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gage_2.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gage_2.48.0.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 48 Package: gaggle Version: 1.66.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 57ff8d473c0af4d060e9a226d77cc20f NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ git_url: https://git.bioconductor.org/packages/gaggle git_branch: RELEASE_3_16 git_last_commit: fd738be git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gaggle_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gaggle_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gaggle_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gaggle_1.66.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R dependencyCount: 9 Package: GAprediction Version: 1.24.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 144a6462c2c70558a3225681fca826cf NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: RELEASE_3_16 git_last_commit: 5453a1c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GAprediction_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GAprediction_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GAprediction_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GAprediction_1.24.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 17 Package: garfield Version: 1.26.0 Suggests: knitr License: GPL-3 MD5sum: 43e981ca34ee93dc4a6a486189039b38 NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: RELEASE_3_16 git_last_commit: 0c51209 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/garfield_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/garfield_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/garfield_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/garfield_1.26.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.18.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: x64 MD5sum: 06bec8c80aa90ef0e5a8fc0f34e47288 NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: RELEASE_3_16 git_last_commit: 9a82f94 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GARS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GARS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GARS_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GARS_1.18.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R dependencyCount: 261 Package: GateFinder Version: 1.18.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: a7706b6e4b17e8269c108b4d7db8c29e NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: RELEASE_3_16 git_last_commit: 153ef5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GateFinder_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GateFinder_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GateFinder_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GateFinder_1.18.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 40 Package: GBScleanR Version: 1.2.14 Depends: SeqArray Imports: stats, utils, methods, ggplot2, tidyr, expm, Rcpp, RcppParallel, gdsfmt LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, testthat (>= 3.0.0), knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: a3bd0bcc1f10d16fa4fe4a27ff207a7c NeedsCompilation: yes Title: Error correction tool for noisy genotyping by sequencing (GBS) data Description: GBScleanR is a package for quality check, filtering, and error correction of genotype data derived from next generation sequcener (NGS) based genotyping platforms. GBScleanR takes Variant Call Format (VCF) file as input. The main function of this package is `estGeno()` which estimates the true genotypes of samples from given read counts for genotype markers using a hidden Markov model with incorporating uneven observation ratio of allelic reads. This implementation gives robust genotype estimation even in noisy genotype data usually observed in Genotyping-By-Sequnencing (GBS) and similar methods, e.g. RADseq. The current implementation accepts genotype data of a diploid population at any generation of multi-parental cross, e.g. biparental F2 from inbred parents, biparental F2 from outbred parents, and 8-way recombinant inbred lines (8-way RILs) which can be refered to as MAGIC population. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel, Sequencing, QualityControl Author: Tomoyuki Furuta [aut, cre] () Maintainer: Tomoyuki Furuta URL: https://github.com/tomoyukif/GBScleanR SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/tomoyukif/GBScleanR/issues git_url: https://git.bioconductor.org/packages/GBScleanR git_branch: RELEASE_3_16 git_last_commit: 48ea0c3 git_last_commit_date: 2023-04-06 Date/Publication: 2023-04-07 source.ver: src/contrib/GBScleanR_1.2.14.tar.gz win.binary.ver: bin/windows/contrib/4.2/GBScleanR_1.2.9.zip mac.binary.ver: bin/macosx/contrib/4.2/GBScleanR_1.2.14.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GBScleanR_1.2.14.tgz vignettes: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.html vignetteTitles: BasicUsageOfGBScleanR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GBScleanR/inst/doc/BasicUsageOfGBScleanR.R dependencyCount: 63 Package: gcapc Version: 1.22.0 Depends: R (>= 3.4) Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 Archs: x64 MD5sum: d7ba08a1d100db010401a45ecda88e36 NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: RELEASE_3_16 git_last_commit: 968eb62 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gcapc_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gcapc_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gcapc_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gcapc_1.22.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R suggestsMe: epigraHMM dependencyCount: 49 Package: gcatest Version: 1.28.2 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 MD5sum: 083e1a76b08fec3b8718f34742ac2500 NeedsCompilation: yes Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait. models. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: RELEASE_3_16 git_last_commit: 530d084 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/gcatest_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/gcatest_1.28.2.zip mac.binary.ver: bin/macosx/contrib/4.2/gcatest_1.28.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gcatest_1.28.2.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R dependencyCount: 3 Package: gCrisprTools Version: 2.4.0 Depends: R (>= 4.1) Imports: Biobase, limma, RobustRankAggreg, ggplot2, SummarizedExperiment, grid, rmarkdown, grDevices, graphics, methods, ComplexHeatmap, stats, utils, parallel Suggests: edgeR, knitr, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, BiocGenerics, markdown, RUnit, sparrow, msigdbr, fgsea License: Artistic-2.0 Archs: x64 MD5sum: 8697be3504f496933b4e2f178da1ceee NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. Version 2.0 extends these applications to include a variety of tools for contextualizing and integrating signals across many experiments, incorporates extended signal enrichment methodologies via the "sparrow" package, and streamlines many formal requirements to aid in interpretablity. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Russell Bainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: RELEASE_3_16 git_last_commit: 154bc8e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gCrisprTools_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gCrisprTools_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gCrisprTools_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gCrisprTools_2.4.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.html, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Contrast_Comparisons_gCrisprTools, Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Contrast_Comparisons.R, vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 93 Package: gcrma Version: 2.70.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL MD5sum: 4bc61327d51b514dc215c47806573b02 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: RELEASE_3_16 git_last_commit: 095f389 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gcrma_2.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gcrma_2.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gcrma_2.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gcrma_2.70.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, bgx, maskBAD, webbioc importsMe: affycoretools, affylmGUI suggestsMe: panp, aroma.affymetrix dependencyCount: 24 Package: GCSscore Version: 1.12.0 Depends: R (>= 3.6) Imports: BiocManager, Biobase, utils, methods, RSQLite, devtools, dplR, stringr, graphics, stats, affxparser, data.table Suggests: siggenes, GEOquery, R.utils License: GPL (>=3) Archs: x64 MD5sum: 37d9e78451873d8cee54f50c19b5a870 NeedsCompilation: no Title: GCSscore: an R package for microarray analysis for Affymetrix/Thermo Fisher arrays Description: For differential expression analysis of 3'IVT and WT-style microarrays from Affymetrix/Thermo-Fisher. Based on S-score algorithm originally described by Zhang et al 2002. biocViews: DifferentialExpression, Microarray, OneChannel, ProprietaryPlatforms, DataImport Author: Guy M. Harris & Shahroze Abbas & Michael F. Miles Maintainer: Guy M. Harris git_url: https://git.bioconductor.org/packages/GCSscore git_branch: RELEASE_3_16 git_last_commit: 6836284 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GCSscore_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GCSscore_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GCSscore_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GCSscore_1.12.0.tgz vignettes: vignettes/GCSscore/inst/doc/GCSscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSscore/inst/doc/GCSscore.R dependencyCount: 127 Package: GDCRNATools Version: 1.18.0 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 9257806e6f0df44a9068f050cc1d029e NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: RELEASE_3_16 git_last_commit: 3d1e731 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GDCRNATools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GDCRNATools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GDCRNATools_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GDCRNATools_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 236 Package: GDSArray Version: 1.18.0 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, markdown, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: e5b248144e7ee5496221dcf65cd814b8 NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut, cre], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: RELEASE_3_16 git_last_commit: ed70604 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GDSArray_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GDSArray_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GDSArray_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GDSArray_1.18.0.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R importsMe: CNVRanger, VariantExperiment suggestsMe: DelayedDataFrame dependencyCount: 29 Package: gdsfmt Version: 1.34.1 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics License: LGPL-3 MD5sum: d3ceaf3f2f7fce9efb1bbef756fcf266 NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: RELEASE_3_16 git_last_commit: d5fa465 git_last_commit_date: 2023-03-31 Date/Publication: 2023-03-31 source.ver: src/contrib/gdsfmt_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/gdsfmt_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/gdsfmt_1.34.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gdsfmt_1.34.1.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, SAIGEgds, SCArray, SeqArray, SNPRelate, Mega2R importsMe: CNVRanger, GBScleanR, GENESIS, ggmanh, GWASTools, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: AnnotationHub, HIBAG, coxmeg linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.24.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics, rmarkdown, markdown License: Artistic-2.0 Archs: x64 MD5sum: 0c0c434a859ef5f1c4375d70877769c1 NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: RELEASE_3_16 git_last_commit: c0f455d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEM_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEM_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEM_1.24.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 36 Package: gemini Version: 1.12.0 Depends: R (>= 4.1.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: b6f766f7f6b868d4e3dd6f362f64107a NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: RELEASE_3_16 git_last_commit: 75413e5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gemini_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gemini_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gemini_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gemini_1.12.0.tgz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 85 Package: gemma.R Version: 1.0.1 Imports: magrittr, glue, memoise, jsonlite, data.table, rlang, lubridate, utils, stringr, SummarizedExperiment, Biobase, tibble, tidyr, S4Vectors, httr, rappdirs, bit64 Suggests: testthat (>= 2.0.0), rmarkdown, knitr, dplyr, covr, ggplot2, ggrepel, BiocStyle, microbenchmark, magick, purrr, pheatmap, viridis License: Apache License (>= 2) MD5sum: b9a86a8899a38d7520457cc7a1364b56 NeedsCompilation: no Title: A wrapper for Gemma's Restful API to access curated gene expression data and differential expression analyses Description: Low- and high-level wrappers for Gemma's RESTful API. They enable access to curated expression and differential expression data from over 10,000 published studies. Gemma is a web site, database and a set of tools for the meta-analysis, re-use and sharing of genomics data, currently primarily targeted at the analysis of gene expression profiles. biocViews: Software, DataImport, Microarray, SingleCell, ThirdPartyClient, DifferentialExpression, GeneExpression, Bayesian, Annotation, ExperimentalDesign, Normalization, BatchEffect, Preprocessing Author: Javier Castillo-Arnemann [aut] (), Jordan Sicherman [aut] (), Ogan Mancarci [cre, aut] (), Guillaume Poirier-Morency [aut] () Maintainer: Ogan Mancarci URL: https://pavlidislab.github.io/gemma.R/, https://github.com/PavlidisLab/gemma.R VignetteBuilder: knitr BugReports: https://github.com/PavlidisLab/gemma.R/issues git_url: https://git.bioconductor.org/packages/gemma.R git_branch: RELEASE_3_16 git_last_commit: 43adbd5 git_last_commit_date: 2023-01-24 Date/Publication: 2023-01-25 source.ver: src/contrib/gemma.R_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/gemma.R_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/gemma.R_1.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gemma.R_1.0.1.tgz vignettes: vignettes/gemma.R/inst/doc/gemma.R.html vignetteTitles: Accessing curated gene expression data with gemma.R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gemma.R/inst/doc/gemma.R.R dependencyCount: 62 Package: genArise Version: 1.74.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 29a1d26d9b9b8bd343c1963ab4eee375 NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: RELEASE_3_16 git_last_commit: 03467e9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genArise_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genArise_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genArise_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genArise_1.74.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: genbankr Version: 1.26.0 Depends: methods Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), GenomicFeatures (>= 1.31.5), Biostrings, VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28), GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 4c04bddf5b5c6319eabb8c00791264f5 NeedsCompilation: no Title: Parsing GenBank files into semantically useful objects Description: Reads Genbank files. biocViews: Infrastructure, DataImport Author: Gabriel Becker [aut, cre], Michael Lawrence [aut] Maintainer: Gabriel Becker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genbankr git_branch: RELEASE_3_16 git_last_commit: e825650 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genbankr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genbankr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genbankr_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genbankr_1.26.0.tgz vignettes: vignettes/genbankr/inst/doc/genbankr.html vignetteTitles: An introduction to genbankr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R importsMe: PACVr dependencyCount: 98 Package: GeneAccord Version: 1.15.0 Depends: R (>= 3.5) Imports: biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools, ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats, tibble, utils Suggests: assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat License: file LICENSE MD5sum: 8e2378e682a98ec7d5c648050f604311 NeedsCompilation: no Title: Detection of clonally exclusive gene or pathway pairs in a cohort of cancer patients Description: A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations. biocViews: BiomedicalInformatics, GeneticVariability, GenomicVariation, SomaticMutation, FunctionalGenomics, Genetics, MathematicalBiology, SystemsBiology, FeatureExtraction, PatternLogic, Pathways Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel Maintainer: Ariane L. Moore URL: https://github.com/cbg-ethz/GeneAccord VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneAccord git_branch: master git_last_commit: bb02137 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/GeneAccord_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneAccord_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneAccord_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneAccord_1.16.0.tgz vignettes: vignettes/GeneAccord/inst/doc/GeneAccord.html vignetteTitles: GeneAccord hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneAccord/inst/doc/GeneAccord.R dependencyCount: 138 Package: geneAttribution Version: 1.24.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 544924665a025512a7fbae129df9d66f NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: RELEASE_3_16 git_last_commit: ddf1343 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geneAttribution_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneAttribution_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneAttribution_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneAttribution_1.24.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 97 Package: GeneBreak Version: 1.28.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: df0c2a7e2f64835fe2b31854fe4ee0ad NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: RELEASE_3_16 git_last_commit: 1cc8091 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneBreak_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneBreak_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneBreak_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneBreak_1.28.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 50 Package: geneClassifiers Version: 1.22.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 0bc02f7d57cde6260f35b8ab32288ed4 NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] () Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: RELEASE_3_16 git_last_commit: 832b04b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geneClassifiers_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneClassifiers_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneClassifiers_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneClassifiers_1.22.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 6 Package: GeneExpressionSignature Version: 1.44.0 Depends: R (>= 4.0) Imports: Biobase, stats, methods Suggests: apcluster, GEOquery, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: f2097f153018d759d0c48b8f43dca38b NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao [aut, cre], Fei Li [ctb], Lu Han [ctb] Maintainer: Yang Cao URL: https://github.com/yiluheihei/GeneExpressionSignature VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/GeneExpressionSignature/issues/ git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: RELEASE_3_16 git_last_commit: 5985d14 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneExpressionSignature_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneExpressionSignature_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneExpressionSignature_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneExpressionSignature_1.44.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.html vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 6 Package: genefilter Version: 1.80.3 Imports: BiocGenerics, AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival, grDevices Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 MD5sum: edad8d6bca4e69d60013613f26207b57 NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: Robert Gentleman, Vincent J. Carey, Wolfgang Huber, Florian Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: RELEASE_3_16 git_last_commit: 2138dca git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/genefilter_1.80.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/genefilter_1.80.3.zip mac.binary.ver: bin/macosx/contrib/4.2/genefilter_1.80.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genefilter_1.80.3.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf vignetteTitles: 01 - Using the genefilter function to filter genes from a microarray dataset, 02 - How to find genes whose expression profile is similar to that of specified genes, 03 - Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R dependsOnMe: cellHTS2, CNTools, GeneMeta, sva, Hiiragi2013, rnaseqGene, lmQCM importsMe: a4Base, annmap, arrayQualityMetrics, Category, cbaf, ClassifyR, countsimQC, covRNA, DEXSeq, GISPA, GSRI, metaseqR2, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, NxtIRFcore, pcaExplorer, PECA, phenoTest, protGear, pwrEWAS, Ringo, spatialHeatmap, SpliceWiz, tilingArray, XDE, zinbwave, FlowSorted.Blood.EPIC, IHWpaper, RNAinteractMAPK, CoNI, dGAselID, INCATome, MiDA, netgsa, oncoPredict suggestsMe: annotate, BioNet, categoryCompare, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, simplifyEnrichment, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData, curatedOvarianData, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, SuperLearner dependencyCount: 54 Package: genefu Version: 2.30.0 Depends: R (>= 4.1), survcomp, biomaRt, iC10, AIMS Imports: amap, impute, mclust, limma, graphics, stats, utils Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival, BiocStyle, magick, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: a29769e87f67b3fd97a056560be7ad2a NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo [aut], Natchar Ratanasirigulchai [aut], Markus S. Schroeder [aut], Laia Pare [aut], Joel S Parker [aut], Aleix Prat [aut], Nikta Feizi [ctb], Christopher Eeles [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: RELEASE_3_16 git_last_commit: 84cd074 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genefu_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genefu_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genefu_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genefu_2.30.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.html vignetteTitles: genefu: A Package For Breast Cancer Gene Expression Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV, PDATK suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 113 Package: GeneGA Version: 1.48.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: a986a9a8ddab49e94fc35817f32d5999 NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: RELEASE_3_16 git_last_commit: fe0e638 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneGA_1.48.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/GeneGA_1.48.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 17 Package: GeneGeneInteR Version: 1.24.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods License: GPL (>= 2) MD5sum: 400f2839067b6ee7b69f8b206c066b29 NeedsCompilation: yes Title: Tools for Testing Gene-Gene Interaction at the Gene Level Description: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. . biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell [ctb], Magalie Houee-Bigot [aut] Maintainer: Mathieu Emily git_url: https://git.bioconductor.org/packages/GeneGeneInteR git_branch: RELEASE_3_16 git_last_commit: b97e029 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneGeneInteR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneGeneInteR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneGeneInteR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneGeneInteR_1.24.0.tgz vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R dependencyCount: 141 Package: GeneMeta Version: 1.70.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 Archs: x64 MD5sum: dbe85479610c82bc4799ad1a03cc4466 NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: RELEASE_3_16 git_last_commit: e5db82e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneMeta_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneMeta_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneMeta_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneMeta_1.70.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 55 Package: GeneNetworkBuilder Version: 1.40.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny, STRINGdb, BiocStyle, magick, rmarkdown, org.Hs.eg.db License: GPL (>= 2) MD5sum: 28db091e332b9bdbdea66e79fc955b9b NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: RELEASE_3_16 git_last_commit: 038b02c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneNetworkBuilder_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneNetworkBuilder_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneNetworkBuilder_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneNetworkBuilder_1.40.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene, Working with BioGRID,, STRING hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R dependencyCount: 47 Package: GeneOverlap Version: 1.34.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: x64 MD5sum: 38427040d6c3d6da80207f4da1f3eb6a NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Icahn School of Medicine at Mount Sinai Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: RELEASE_3_16 git_last_commit: 46aaefe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneOverlap_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneOverlap_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneOverlap_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneOverlap_1.34.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R importsMe: ATACCoGAPS dependencyCount: 9 Package: geneplast Version: 1.24.1 Depends: R (>= 4.0), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: f27519844bf64ec209ba15fa0258e316 NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: RELEASE_3_16 git_last_commit: ecd4cf3 git_last_commit_date: 2023-03-11 Date/Publication: 2023-03-12 source.ver: src/contrib/geneplast_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneplast_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/geneplast_1.24.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneplast_1.24.1.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary analysis of orthologous groups." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R suggestsMe: TreeAndLeaf, geneplast.data dependencyCount: 20 Package: geneplotter Version: 1.76.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db License: Artistic-2.0 MD5sum: 16c5a8f55d43a3dba0ac29eb801f7c29 NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/geneplotter git_branch: RELEASE_3_16 git_last_commit: 4eb6a78 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geneplotter_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneplotter_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneplotter_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneplotter_1.76.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HD2013SGI, Hiiragi2013 importsMe: biocGraph, DESeq2, DEXSeq, MethylSeekR, RNAinteract suggestsMe: biocGraph, Category, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 52 Package: geneRecommender Version: 1.70.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: aac6ea09d3cce05f9e9a0058d3eb70bc NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: RELEASE_3_16 git_last_commit: f96e510 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geneRecommender_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneRecommender_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneRecommender_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneRecommender_1.70.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 6 Package: GeneRegionScan Version: 1.54.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 81798867dd3ee484d8a9590a7ba2d3b6 NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: RELEASE_3_16 git_last_commit: 78207d7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneRegionScan_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneRegionScan_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneRegionScan_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneRegionScan_1.54.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 21 Package: geneRxCluster Version: 1.34.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: cf994ae3d7dc824451f4576d3a9dd47b NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: RELEASE_3_16 git_last_commit: 78f1a72 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geneRxCluster_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneRxCluster_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneRxCluster_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneRxCluster_1.34.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 16 Package: GeneSelectMMD Version: 2.42.3 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) Archs: x64 MD5sum: a0cc2dd1b9d94f309ccfcf285e21d467 NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions. biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: RELEASE_3_16 git_last_commit: 70e2082 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/GeneSelectMMD_2.42.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneSelectMMD_2.42.3.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneSelectMMD_2.42.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneSelectMMD_2.42.3.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 9 Package: GENESIS Version: 2.28.0 Imports: Biobase, BiocGenerics, BiocParallel, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: x64 MD5sum: 56e24fb6f32d4c9f102b3074ce86d881 NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Thomas Lumley, Kenneth Rice, Tamar Sofer, Adrienne Stilp, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: RELEASE_3_16 git_last_commit: 5ed68a0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GENESIS_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GENESIS_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GENESIS_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GENESIS_2.28.0.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R suggestsMe: coxmeg dependencyCount: 93 Package: GeneStructureTools Version: 1.18.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 393e92d07cffc45540c7eb4bf21236bf NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: RELEASE_3_16 git_last_commit: d5d143a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneStructureTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneStructureTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneStructureTools_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneStructureTools_1.18.0.tgz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 156 Package: geNetClassifier Version: 1.38.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics, grDevices Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 4a54f89f055a14e6bd4341193b83eb8f NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: RELEASE_3_16 git_last_commit: 8722088 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geNetClassifier_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geNetClassifier_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geNetClassifier_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geNetClassifier_1.38.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 17 Package: GeneticsPed Version: 1.60.3 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE MD5sum: 4c13272db974ffa612f1b91bc06e5750 NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: RELEASE_3_16 git_last_commit: db0da9a git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/GeneticsPed_1.60.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneticsPed_1.60.3.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneticsPed_1.60.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneticsPed_1.60.3.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R importsMe: LRQMM dependencyCount: 11 Package: GeneTonic Version: 2.2.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, backbone, bs4Dash (>= 2.0.0), circlize, colorspace, colourpicker, ComplexHeatmap, ComplexUpset, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2, ggrepel, GO.db, graphics, grDevices, grid, igraph, matrixStats, methods, plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors, scales, shiny, shinyAce, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tippy, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: b99284baf334861c911daa3b77492abb NeedsCompilation: no Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides a Shiny application that aims to combine at different levels the existing pieces of the transcriptome data and results, in a way that makes it easier to generate insightful observations and hypothesis - combining the benefits of interactivity and reproducibility, e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, GO Author: Federico Marini [aut, cre] (), Annekathrin Ludt [aut] () Maintainer: Federico Marini URL: https://github.com/federicomarini/GeneTonic VignetteBuilder: knitr BugReports: https://github.com/federicomarini/GeneTonic/issues git_url: https://git.bioconductor.org/packages/GeneTonic git_branch: RELEASE_3_16 git_last_commit: cbc0a9f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeneTonic_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeneTonic_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeneTonic_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeneTonic_2.2.0.tgz vignettes: vignettes/GeneTonic/inst/doc/GeneTonic_manual.html vignetteTitles: The GeneTonic User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneTonic/inst/doc/GeneTonic_manual.R dependencyCount: 169 Package: geneXtendeR Version: 1.24.0 Depends: rtracklayer, GO.db, R (>= 3.5.0) Imports: data.table, dplyr, graphics, networkD3, RColorBrewer, SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle, org.Rn.eg.db Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer License: GPL (>= 3) MD5sum: 4bcfa9e2d7121949ad609e6dd95ccc4d NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues git_url: https://git.bioconductor.org/packages/geneXtendeR git_branch: RELEASE_3_16 git_last_commit: 869b7e0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geneXtendeR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geneXtendeR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geneXtendeR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geneXtendeR_1.24.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 116 Package: GENIE3 Version: 1.20.0 Imports: stats, reshape2, dplyr Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) Archs: x64 MD5sum: 4e7d20a2312519ef7a4270f7c57afd5b NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: RELEASE_3_16 git_last_commit: aea2e68 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GENIE3_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GENIE3_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GENIE3_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GENIE3_1.20.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: BioNERO, MetNet, bulkAnalyseR suggestsMe: dnapath dependencyCount: 27 Package: genoCN Version: 1.50.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: x64 MD5sum: 4c53f4efeea51711c6658d1e7048bf5c NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun git_url: https://git.bioconductor.org/packages/genoCN git_branch: RELEASE_3_16 git_last_commit: e166b5b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genoCN_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genoCN_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genoCN_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genoCN_1.50.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R dependencyCount: 3 Package: genomation Version: 1.30.0 Depends: R (>= 3.5.0), grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 173e543aae963b1c5276666aca99cb21 NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: RELEASE_3_16 git_last_commit: 99239c1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genomation_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomation_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomation_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genomation_1.30.0.tgz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, EpiCompare, fCCAC, RCAS suggestsMe: methylKit dependencyCount: 95 Package: GenomAutomorphism Version: 1.0.3 Depends: R (>= 4.2), Imports: Biostrings, BiocGenerics, BiocParallel, GenomeInfoDb, GenomicRanges, IRanges, dplyr, data.table, parallel, doParallel, foreach, methods, S4Vectors, stats, numbers, utils Suggests: spelling, rmarkdown, BiocStyle, testthat (>= 3.0.0), knitr License: Artistic-2.0 Archs: x64 MD5sum: b0fbf5f38939ecf0d5667eda6a51652e NeedsCompilation: no Title: Compute the automorphisms between DNA's Abelian group representations Description: This is a R package to compute the automorphisms between pairwise aligned DNA sequences represented as elements from a Genomic Abelian group. In a general scenario, from genomic regions till the whole genomes from a given population (from any species or close related species) can be algebraically represented as a direct sum of cyclic groups or more specifically Abelian p-groups. Basically, we propose the representation of multiple sequence alignments of length N bp as element of a finite Abelian group created by the direct sum of homocyclic Abelian group of prime-power order. biocViews: MathematicalBiology, ComparativeGenomics, FunctionalGenomics, MultipleSequenceAlignment Author: Robersy Sanchez [aut, cre] () Maintainer: Robersy Sanchez URL: https://github.com/genomaths/GenomAutomorphism VignetteBuilder: knitr BugReports: https://github.com/genomaths/GenomAutomorphism/issues git_url: https://git.bioconductor.org/packages/GenomAutomorphism git_branch: RELEASE_3_16 git_last_commit: 4ed0e65 git_last_commit_date: 2023-02-24 Date/Publication: 2023-02-26 source.ver: src/contrib/GenomAutomorphism_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomAutomorphism_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomAutomorphism_1.0.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomAutomorphism_1.0.3.tgz vignettes: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.html vignetteTitles: Get started-with GenomAutomorphism hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomAutomorphism/inst/doc/GenomAutomorphism.R dependencyCount: 50 Package: GenomeInfoDb Version: 1.34.9 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.25.12), IRanges (>= 2.13.12) Imports: stats, stats4, utils, RCurl, GenomeInfoDbData Suggests: GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 4f2943dd3daeeaddf33568a8b537d81f NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora [aut], Martin Morgan [aut], Marc Carlson [aut], Hervé Pagès [aut, cre], Prisca Chidimma Maduka [ctb], Atuhurira Kirabo Kakopo [ctb], Emmanuel Chigozie Elendu [ctb] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomeInfoDb VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: RELEASE_3_16 git_last_commit: 24d7cb9 git_last_commit_date: 2023-02-02 Date/Publication: 2023-02-02 source.ver: src/contrib/GenomeInfoDb_1.34.9.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomeInfoDb_1.34.9.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomeInfoDb_1.34.9.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomeInfoDb_1.34.9.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb: Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: Biostrings, BRGenomics, BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, IdeoViz, Rsamtools, SCOPE, VariantAnnotation, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, UCSCRepeatMasker, RTIGER importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, ATACseqTFEA, atena, BaalChIP, ballgown, bambu, BindingSiteFinder, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, cageminer, CAGEr, cardelino, casper, cBioPortalData, CexoR, cfDNAPro, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, Cogito, comapr, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, CopywriteR, crisprBowtie, crisprBwa, crisprDesign, CRISPRseek, CrispRVariants, crisprViz, csaw, customProDB, DAMEfinder, dasper, decompTumor2Sig, DeepBlueR, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffUTR, DMRcate, DMRScan, dmrseq, DominoEffect, easyRNASeq, ELMER, enhancerHomologSearch, enrichTF, ensembldb, ensemblVEP, epialleleR, EpiCompare, epigenomix, epigraHMM, EpiMix, epimutacions, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, extraChIPs, factR, FindIT2, FLAMES, FRASER, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, genomation, GenomAutomorphism, genomeIntervals, GenomicDistributions, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicScores, genotypeeval, GenVisR, ggbio, gmoviz, GOTHiC, GRaNIE, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiCBricks, HiCDOC, HiContacts, HiTC, HTSeqGenie, idr2d, IMAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, katdetectr, MADSEQ, maser, metagene, metagene2, metaseqR2, metavizr, MethCP, methimpute, methInheritSim, methylKit, methylPipe, methylSig, methylumi, minfi, MinimumDistance, MMAPPR2, monaLisa, mosaics, Motif2Site, motifbreakR, motifmatchr, MouseFM, msgbsR, multicrispr, multiHiCcompare, MungeSumstats, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, nullranges, NxtIRFcore, ODER, OGRE, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, Pi, pipeFrame, plyranges, podkat, pram, prebs, proActiv, profileplyr, ProteoDisco, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment, RareVariantVis, Rcade, RCAS, RcisTarget, recount, recoup, regioneR, regionReport, REMP, Repitools, RESOLVE, rfPred, RgnTX, rGREAT, RiboCrypt, RiboProfiling, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RLSeq, rnaEditr, RNAmodR, roar, RTCGAToolbox, rtracklayer, scanMiR, scanMiRApp, scDblFinder, scmeth, scruff, segmentSeq, SeqArray, seqCAT, seqsetvis, sesame, sevenC, SGSeq, ShortRead, signeR, SigsPack, SingleMoleculeFootprinting, sitadela, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spatzie, spiky, SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, svaNUMT, svaRetro, TAPseq, TarSeqQC, TCGAutils, TENxIO, TFBSTools, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TVTB, tximeta, Ularcirc, UMI4Cats, VanillaICE, VariantFiltering, VariantTools, VaSP, VplotR, wiggleplotr, YAPSA, fitCons.UCSC.hg19, GenomicState, grasp2db, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, chipenrich.data, GenomicDistributionsData, MethylSeqData, sesameData, ActiveDriverWGS, crispRdesignR, deconstructSigs, driveR, ggcoverage, HiCfeat, ICAMS, MAAPER, Mega2R, MOCHA, Signac, simMP, xQTLbiolinks suggestsMe: AnnotationForge, AnnotationHub, BiocOncoTK, chromswitch, ExperimentHubData, fishpond, ldblock, megadepth, methrix, parglms, plotgardener, QDNAseq, regioneReloaded, scTreeViz, splatter, systemPipeR, TFutils, xcoredata, gkmSVM, polyRAD, Seurat dependencyCount: 11 Package: genomeIntervals Version: 1.54.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 MD5sum: b214c9e27721554808bc1c803d65f774 NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: RELEASE_3_16 git_last_commit: a1bab6a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genomeIntervals_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomeIntervals_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomeIntervals_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genomeIntervals_1.54.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe, ChIC.data importsMe: ChIC, easyRNASeq dependencyCount: 17 Package: genomes Version: 3.28.0 Depends: readr, curl License: GPL-3 MD5sum: bef2ad1de8e48861ba1a220736cf011c NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: RELEASE_3_16 git_last_commit: 70f5588 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genomes_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomes_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomes_3.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genomes_3.28.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 31 Package: GenomicAlignments Version: 1.34.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 9f5b23e4f6b8658e47fac20d53beac4c NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology Author: Hervé Pagès [aut, cre], Valerie Obenchain [aut], Martin Morgan [aut] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicAlignments Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: RELEASE_3_16 git_last_commit: f65563c git_last_commit_date: 2023-03-08 Date/Publication: 2023-03-09 source.ver: src/contrib/GenomicAlignments_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicAlignments_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicAlignments_1.34.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicAlignments_1.34.1.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, ChIPexoQual, groHMM, HelloRanges, hiReadsProcessor, igvR, ORFik, prebs, recoup, RiboDiPA, ShortRead, SplicingGraphs, alpineData, SCATEData, sequencing importsMe: alpine, AneuFinder, APAlyzer, ASpediaFI, ASpli, ATACseqQC, ATACseqTFEA, atena, BaalChIP, bambu, biovizBase, breakpointR, BRGenomics, CAGEfightR, CAGEr, cfDNAPro, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, consensusDE, contiBAIT, CopywriteR, CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, DNAfusion, easyRNASeq, esATAC, FRASER, FunChIP, gcapc, genomation, GenomicFiles, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylPipe, mosaics, Motif2Site, msgbsR, NADfinder, PICS, plyranges, pram, proActiv, ramwas, Rcade, Repitools, RiboProfiling, ribosomeProfilingQC, RNAmodR, roar, Rqc, rtracklayer, SCATE, scPipe, scruff, seqsetvis, SGSeq, soGGi, spiky, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TAPseq, TarSeqQC, TCseq, trackViewer, transcriptR, Ularcirc, UMI4Cats, VaSP, VplotR, leeBamViews, alakazam, ExomeDepth, ggcoverage, MAAPER, PACVr, pulseTD, VALERIE suggestsMe: amplican, BindingSiteFinder, BiocParallel, csaw, EpiCompare, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, QuasR, Rsamtools, similaRpeak, Streamer, systemPipeR, alpineData, NanoporeRNASeq, parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 39 Package: GenomicDataCommons Version: 1.22.3 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, tibble Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools, BiocParallel, TxDb.Hsapiens.UCSC.hg38.knownGene, VariantAnnotation, maftools, R.utils, data.table License: Artistic-2.0 Archs: x64 MD5sum: ffd3b694a7a1faa6bf2e1be06ff4353f NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre], Marcel Ramos [ctb] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons, http://bioconductor.github.io/GenomicDataCommons/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: RELEASE_3_16 git_last_commit: bd48c6b git_last_commit_date: 2023-04-10 Date/Publication: 2023-04-10 source.ver: src/contrib/GenomicDataCommons_1.22.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicDataCommons_1.22.3.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicDataCommons_1.22.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicDataCommons_1.22.3.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.html, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons, Questions and answers from over the years, Somatic Mutation Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R, vignettes/GenomicDataCommons/inst/doc/questions-and-answers.R, vignettes/GenomicDataCommons/inst/doc/somatic_mutations.R importsMe: GDCRNATools, TCGAutils dependencyCount: 53 Package: GenomicDistributions Version: 1.6.0 Depends: R (>= 4.0), IRanges, GenomicRanges Imports: data.table, ggplot2, reshape2, methods, utils, Biostrings, plyr, dplyr, scales, broom, GenomeInfoDb, stats Suggests: AnnotationFilter, rtracklayer, testthat, knitr, BiocStyle, rmarkdown, GenomicDistributionsData Enhances: BSgenome, extrafont, ensembldb, GenomicFeatures License: BSD_2_clause + file LICENSE MD5sum: 5c4e65e6fee3afa33a156c04ba0cbc68 NeedsCompilation: no Title: GenomicDistributions: fast analysis of genomic intervals with Bioconductor Description: If you have a set of genomic ranges, this package can help you with visualization and comparison. It produces several kinds of plots, for example: Chromosome distribution plots, which visualize how your regions are distributed over chromosomes; feature distance distribution plots, which visualizes how your regions are distributed relative to a feature of interest, like Transcription Start Sites (TSSs); genomic partition plots, which visualize how your regions overlap given genomic features such as promoters, introns, exons, or intergenic regions. It also makes it easy to compare one set of ranges to another. biocViews: Software, GenomeAnnotation, GenomeAssembly, DataRepresentation, Sequencing, Coverage, FunctionalGenomics, Visualization Author: Kristyna Kupkova [aut, cre], Jose Verdezoto [aut], Tessa Danehy [aut], John Lawson [aut], Jose Verdezoto [aut], Michal Stolarczyk [aut], Jason Smith [aut], Bingjie Xue [aut], Sophia Rogers [aut], John Stubbs [aut], Nathan C. Sheffield [aut] Maintainer: Kristyna Kupkova URL: http://code.databio.org/GenomicDistributions VignetteBuilder: knitr BugReports: http://github.com/databio/GenomicDistributions git_url: https://git.bioconductor.org/packages/GenomicDistributions git_branch: RELEASE_3_16 git_last_commit: a89bb3d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicDistributions_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicDistributions_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicDistributions_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicDistributions_1.6.0.tgz vignettes: vignettes/GenomicDistributions/inst/doc/full-power.html, vignettes/GenomicDistributions/inst/doc/intro.html vignetteTitles: 2. Full power GenomicDistributions, 1. Getting started with GenomicDistributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicDistributions/inst/doc/intro.R dependencyCount: 64 Package: GenomicFeatures Version: 1.50.4 Depends: R (>= 3.5.0), BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>= 2.13.23), GenomeInfoDb (>= 1.34.7), GenomicRanges (>= 1.31.17), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl, XVector (>= 0.19.7), Biostrings (>= 2.47.6), BiocIO, rtracklayer (>= 1.51.5), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: RMariaDB, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, AnnotationFilter, RUnit, BiocStyle, knitr, markdown License: Artistic-2.0 MD5sum: cc6e1d7ae0158fad765ad5fc59c21fe4 NeedsCompilation: no Title: Conveniently import and query gene models Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson [aut], H. Pagès [aut, cre], P. Aboyoun [aut], S. Falcon [aut], M. Morgan [aut], D. Sarkar [aut], M. Lawrence [aut], V. Obenchain [aut], S. Arora [ctb], J. MacDonald [ctb], M. Ramos [ctb], S. Saini [ctb], P. Shannon [ctb], L. Shepherd [ctb], D. Tenenbaum [ctb], D. Van Twisk [ctb] Maintainer: H. Pagès URL: https://bioconductor.org/packages/GenomicFeatures VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicFeatures/issues git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: RELEASE_3_16 git_last_commit: 68bf6e1 git_last_commit_date: 2022-12-14 Date/Publication: 2023-01-24 source.ver: src/contrib/GenomicFeatures_1.50.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicFeatures_1.50.4.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicFeatures_1.50.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicFeatures_1.50.4.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.html vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: Cogito, cpvSNP, ensembldb, GSReg, Guitar, HelloRanges, OrganismDbi, OUTRIDER, RareVariantVis, RiboDiPA, SplicingGraphs, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Athaliana.BioMart.plantsmart51, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Cfamiliaris.UCSC.canFam4.refGene, TxDb.Cfamiliaris.UCSC.canFam5.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg38.refGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpediaFI, ASpli, ATACCoGAPS, bambu, BgeeCall, BiocOncoTK, 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Package: GenomicFiles Version: 1.34.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), MatrixGenerics, GenomicRanges (>= 1.31.16), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25), VariantAnnotation (>= 1.27.9), GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, snpStats, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: 03264fd9bf67d47d3e8844b3307affa5 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut] Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: RELEASE_3_16 git_last_commit: 65f9a64 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicFiles_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicFiles_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicFiles_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicFiles_1.34.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, contiBAIT, derfinder, QuasR, Rqc, VCFArray suggestsMe: ldblock, MungeSumstats, TFutils dependencyCount: 98 Package: genomicInstability Version: 1.4.0 Depends: R (>= 4.1.0), checkmate Imports: mixtools, SummarizedExperiment Suggests: SingleCellExperiment, ExperimentHub, pROC License: file LICENSE MD5sum: e458921b9bc440fdef44d11d47c0cf88 NeedsCompilation: no Title: Genomic Instability estimation for scRNA-Seq Description: This package contain functions to run genomic instability analysis (GIA) from scRNA-Seq data. GIA estimates the association between gene expression and genomic location of the coding genes. It uses the aREA algorithm to quantify the enrichment of sets of contiguous genes (loci-blocks) on the gene expression profiles and estimates the Genomic Instability Score (GIS) for each analyzed cell. biocViews: SystemsBiology, GeneExpression, SingleCell Author: Mariano Alvarez [aut, cre], Pasquale Laise [aut], DarwinHealth [cph] Maintainer: Mariano Alvarez URL: https://github.com/DarwinHealth/genomicInstability BugReports: https://github.com/DarwinHealth/genomicInstability git_url: https://git.bioconductor.org/packages/genomicInstability git_branch: RELEASE_3_16 git_last_commit: 646fb2d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genomicInstability_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genomicInstability_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genomicInstability_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genomicInstability_1.4.0.tgz vignettes: vignettes/genomicInstability/inst/doc/genomicInstability.pdf vignetteTitles: Using genomicInstability hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genomicInstability/inst/doc/genomicInstability.R dependencyCount: 101 Package: GenomicInteractionNodes Version: 1.2.0 Depends: R (>= 4.2.0), stats Imports: AnnotationDbi, graph, GO.db, GenomicRanges, GenomicFeatures, GenomeInfoDb, methods, IRanges, RBGL, S4Vectors Suggests: RUnit, BiocStyle, knitr, rmarkdown, rtracklayer, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: file LICENSE MD5sum: c04ed88fa81e9812f64d02cf9afa08ee NeedsCompilation: no Title: A R/Bioconductor package to detect the interaction nodes from HiC/HiChIP/HiCAR data Description: The GenomicInteractionNodes package can import interactions from bedpe file and define the interaction nodes, the genomic interaction sites with multiple interaction loops. The interaction nodes is a binding platform regulates one or multiple genes. The detected interaction nodes will be annotated for downstream validation. biocViews: HiC, Sequencing, Software Author: Jianhong Ou [aut, cre], Yarui Diao [fnd] Maintainer: Jianhong Ou URL: https://github.com/jianhong/GenomicInteractionNodes VignetteBuilder: knitr BugReports: https://github.com/jianhong/GenomicInteractionNodes/issues git_url: https://git.bioconductor.org/packages/GenomicInteractionNodes git_branch: RELEASE_3_16 git_last_commit: a61a04f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicInteractionNodes_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicInteractionNodes_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicInteractionNodes_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicInteractionNodes_1.2.0.tgz vignettes: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.html vignetteTitles: GenomicInteractionNodes Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenomicInteractionNodes/inst/doc/GenomicInteractionNodes_vignettes.R dependencyCount: 99 Package: GenomicInteractions Version: 1.32.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL-3 MD5sum: d82d3a08381400e9abbc2f4ab849938a NeedsCompilation: no Title: Utilities for handling genomic interaction data Description: Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: RELEASE_3_16 git_last_commit: bc2d152 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicInteractions_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicInteractions_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicInteractions_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicInteractions_1.32.0.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR, extraChIPs, HiContacts, spatzie suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 155 Package: GenomicOZone Version: 1.12.0 Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack Suggests: readxl, GEOquery, knitr, rmarkdown License: LGPL (>=3) MD5sum: db2908cde77a346513881dd5f3d46241 NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation, CopyNumberVariation Author: Hua Zhong, Mingzhou Song Maintainer: Hua Zhong, Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: RELEASE_3_16 git_last_commit: e6068df git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicOZone_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicOZone_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicOZone_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicOZone_1.12.0.tgz vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html vignetteTitles: GenomicOZone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R dependencyCount: 161 Package: GenomicRanges Version: 1.50.2 Depends: R (>= 4.0.0), methods, stats4, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.31.2), GenomeInfoDb (>= 1.15.2) Imports: utils, stats, XVector (>= 0.29.2) LinkingTo: S4Vectors, IRanges Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>= 1.13.53), GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGGREST, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: fbd8ce202a23da66d0df008f665ad74e NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, Coverage Author: Patrick Aboyoun [aut], Hervé Pagès [aut, cre], Michael Lawrence [aut] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/GenomicRanges VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicRanges/issues git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: RELEASE_3_16 git_last_commit: 6125839 git_last_commit_date: 2022-12-15 Date/Publication: 2022-12-16 source.ver: src/contrib/GenomicRanges_1.50.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicRanges_1.50.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicRanges_1.50.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicRanges_1.50.2.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. 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trackViewer, TransView, traseR, tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VarCon, VariantAnnotation, VariantTools, VplotR, vtpnet, vulcan, wavClusteR, YAPSA, EuPathDB, excluderanges, ChAMPdata, EatonEtAlChIPseq, nullrangesData, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, SCATEData, WGSmapp, liftOver, sequencing, HiCfeat, PlasmaMutationDetector, PlasmaMutationDetector2, RTIGER importsMe: ACE, ALDEx2, alpine, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACCoGAPS, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, ballgown, bambu, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, BiocOncoTK, BioTIP, biovizBase, biscuiteer, BiSeq, BOBaFIT, borealis, brainflowprobes, branchpointer, BRGenomics, BSgenome, BUSpaRse, cageminer, CAGEr, cardelino, cBioPortalData, CexoR, cfDNAPro, ChIC, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, chromDraw, ChromHeatMap, ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq, cliProfiler, CNEr, CNVfilteR, CNViz, CNVMetrics, comapr, coMET, coMethDMR, compartmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprBase, crisprBowtie, crisprDesign, CRISPRseek, crisprseekplus, CrispRVariants, crisprViz, customProDB, DAMEfinder, dasper, debrowser, decompTumor2Sig, deconvR, DeepBlueR, DEFormats, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffUTR, DMRcate, dmrseq, DNAfusion, DominoEffect, DRIMSeq, DropletUtils, easyRNASeq, EDASeq, eisaR, ELMER, enhancerHomologSearch, enrichTF, epialleleR, EpiCompare, epidecodeR, epigraHMM, EpiMix, epimutacions, epistack, EpiTxDb, epivizr, epivizrData, erma, EventPointer, factR, fcScan, FilterFFPE, fishpond, FLAMES, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, genomation, GenomAutomorphism, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractionNodes, GenomicInteractions, genotypeeval, GenVisR, ggbio, GOfuncR, GRaNIE, gwascat, h5vc, heatmaps, hermes, HiCBricks, HiCcompare, HiContacts, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, INSPEcT, InterMineR, ipdDb, IsoformSwitchAnalyzeR, isomiRs, IVAS, karyoploteR, katdetectr, loci2path, LOLA, LoomExperiment, lumi, MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, megadepth, memes, metaseqR2, MethCP, methInheritSim, MethReg, methrix, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, mitoClone2, MMAPPR2, MMDiff2, Modstrings, monaLisa, mosaics, Motif2Site, motifbreakR, motifmatchr, MouseFM, MSA2dist, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, MungeSumstats, musicatk, NanoMethViz, nanotatoR, ncRNAtools, nearBynding, normr, nucleR, nullranges, NxtIRFcore, ODER, OGRE, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, pageRank, panelcn.mops, PAST, pcaExplorer, pepStat, PhIPData, Pi, PICS, PING, pqsfinder, pram, prebs, preciseTAD, primirTSS, proActiv, proBAMr, profileplyr, ProteoDisco, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, RcisTarget, recount, recount3, regioneR, regionReport, regutools, REMP, Repitools, RESOLVE, rfPred, rGADEM, RGMQL, RgnTX, Rhisat2, RiboCrypt, RiboDiPA, RiboProfiling, RIPAT, RLSeq, Rmmquant, rmspc, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, RTCGAToolbox, scanMiR, scanMiRApp, SCATE, scDblFinder, scmeth, scoreInvHap, scPipe, scruff, scuttle, segmenter, seq2pathway, SeqArray, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, shinyepico, ShortRead, signeR, SigsPack, SimFFPE, SingleCellExperiment, SingleMoleculeFootprinting, sitadela, snapcount, soGGi, SparseSignatures, spatzie, SpectralTAD, SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, syntenet, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TENxIO, terraTCGAdata, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, tidybulk, TitanCNA, tLOH, tracktables, transcriptR, transite, trena, TRESS, tricycle, triplex, tscR, TVTB, txcutr, tximeta, Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, VariantFiltering, VaSP, VCFArray, wiggleplotr, xcore, XNAString, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BioPlex, chipenrich.data, COSMIC.67, ELMER.data, GenomicDistributionsData, leeBamViews, MethylSeqData, pepDat, scRNAseq, sesameData, SomaticCancerAlterations, spatialLIBD, VariantToolsData, recountWorkflow, ActiveDriverWGS, cinaR, crispRdesignR, driveR, ExomeDepth, geneHapR, geno2proteo, ggcoverage, hoardeR, ICAMS, lolliplot, LoopRig, MAAPER, MitoHEAR, MOCHA, noisyr, numbat, oncoPredict, PACVr, pagoo, RapidoPGS, scPloidy, Signac, simMP, utr.annotation, VALERIE, xQTLbiolinks suggestsMe: AnnotationHub, autonomics, biobroom, BiocGenerics, BiocParallel, Chicago, CNVgears, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, ggmanh, Glimma, GSReg, GWASTools, HDF5Array, InteractiveComplexHeatmap, interactiveDisplay, IRanges, maftools, MiRaGE, omicsPrint, parglms, plotgardener, recountmethylation, RTCGA, S4Vectors, SeqGSEA, splatter, TFutils, universalmotif, updateObject, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, CTCF, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, Single.mTEC.Transcriptomes, systemPipeRdata, xcoredata, CAGEWorkflow, cancerTiming, chicane, DGEobj, gkmSVM, MARVEL, polyRAD, Rgff, rliger, seqmagick, Seurat, sigminer, SNPassoc, updog, valr dependencyCount: 15 Package: GenomicScores Version: 2.10.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, httr, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: RUnit, BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19, MafDb.1Kgenomes.phase1.hs37d5, SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation, TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer, shiny, shinyjs, shinycustomloader, data.table, DT, magrittr, shinydashboard License: Artistic-2.0 Archs: x64 MD5sum: 8f72190f11c9a79e003c478c282e1b78 NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage, AnnotationHubSoftware Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo Rodríguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues git_url: https://git.bioconductor.org/packages/GenomicScores git_branch: RELEASE_3_16 git_last_commit: c2e29da git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicScores_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicScores_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicScores_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicScores_2.10.0.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R dependsOnMe: fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons35way.UCSC.mm39, phastCons7way.UCSC.hg38, phyloP35way.UCSC.mm39 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix dependencyCount: 107 Package: GenomicSuperSignature Version: 1.6.0 Depends: R (>= 4.1.0), SummarizedExperiment Imports: ComplexHeatmap, ggplot2, methods, S4Vectors, Biobase, ggpubr, dplyr, plotly, BiocFileCache, grid, flextable, irlba Suggests: knitr, rmarkdown, devtools, roxygen2, pkgdown, usethis, BiocStyle, testthat, forcats, stats, wordcloud, circlize, EnrichmentBrowser, clusterProfiler, msigdbr, cluster, RColorBrewer, reshape2, tibble, BiocManager, bcellViper, readr, utils License: Artistic-2.0 MD5sum: abeb255f416aa751f8bacc6665e7e390 NeedsCompilation: no Title: Interpretation of RNA-seq experiments through robust, efficient comparison to public databases Description: This package provides a novel method for interpreting new transcriptomic datasets through near-instantaneous comparison to public archives without high-performance computing requirements. Through the pre-computed index, users can identify public resources associated with their dataset such as gene sets, MeSH term, and publication. Functions to identify interpretable annotations and intuitive visualization options are implemented in this package. biocViews: Transcriptomics, SystemsBiology, PrincipalComponent, RNASeq, Sequencing, Pathways, Clustering Author: Sehyun Oh [aut, cre], Levi Waldron [aut], Sean Davis [aut] Maintainer: Sehyun Oh URL: https://github.com/shbrief/GenomicSuperSignature VignetteBuilder: knitr BugReports: https://github.com/shbrief/GenomicSuperSignature/issues git_url: https://git.bioconductor.org/packages/GenomicSuperSignature git_branch: RELEASE_3_16 git_last_commit: 6829058 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicSuperSignature_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicSuperSignature_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicSuperSignature_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicSuperSignature_1.6.0.tgz vignettes: vignettes/GenomicSuperSignature/inst/doc/Contents.html, vignettes/GenomicSuperSignature/inst/doc/Quickstart.html vignetteTitles: Introduction on RAVmodel, Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicSuperSignature/inst/doc/Contents.R, vignettes/GenomicSuperSignature/inst/doc/Quickstart.R dependencyCount: 180 Package: GenomicTuples Version: 1.32.0 Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>= 1.15.2), S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, covr License: Artistic-2.0 MD5sum: 421e3b71845113d4f878a85a2f86ddea NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès [ctb] Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: RELEASE_3_16 git_last_commit: d19d753 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenomicTuples_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenomicTuples_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenomicTuples_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenomicTuples_1.32.0.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 18 Package: genotypeeval Version: 1.30.0 Depends: R (>= 3.4.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel, graphics, stats Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE Archs: x64 MD5sum: acfadf3133965dd89112f1204fc32b09 NeedsCompilation: no Title: QA/QC of a gVCF or VCF file Description: Takes in a gVCF or VCF and reports metrics to assess quality of calls. biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation, DataImport Author: Jennifer Tom [aut, cre] Maintainer: Jennifer Tom VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/genotypeeval git_branch: RELEASE_3_16 git_last_commit: bb3a0e0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/genotypeeval_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/genotypeeval_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/genotypeeval_1.29.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/genotypeeval_1.30.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 112 Package: GenProSeq Version: 1.2.0 Depends: keras, mclust, R (>= 4.2) Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders, reticulate, stats Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 567b4bf531054a988510d8e204f205c0 NeedsCompilation: no Title: Generating Protein Sequences with Deep Generative Models Description: Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model. biocViews: Software, Proteomics Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenProSeq git_branch: RELEASE_3_16 git_last_commit: fbdb829 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GenProSeq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenProSeq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenProSeq_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenProSeq_1.2.0.tgz vignettes: vignettes/GenProSeq/inst/doc/GenProSeq.html vignetteTitles: GenProSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenProSeq/inst/doc/GenProSeq.R dependencyCount: 162 Package: GenVisR Version: 1.30.0 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis, data.table, BSgenome, GenomeInfoDb, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: 8724582b7c0d2dff1a18961ae8a3f587 NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: RELEASE_3_16 git_last_commit: ad1791a git_last_commit_date: 2022-11-01 Date/Publication: 2023-02-08 source.ver: src/contrib/GenVisR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GenVisR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GenVisR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GenVisR_1.30.0.tgz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/Upcoming_Features.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, Visualizing Small Variants, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/Upcoming_Features.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 119 Package: GeoDiff Version: 1.4.2 Depends: R (>= 4.1.0), Biobase Imports: Matrix, robust, plyr, lme4, Rcpp (>= 1.0.4.6), withr, methods, graphics, stats, testthat, GeomxTools, NanoStringNCTools LinkingTo: Rcpp, RcppArmadillo, roptim Suggests: knitr, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: dc8e22bb2cfa0ba7c2e5850a90b0990c NeedsCompilation: yes Title: Count model based differential expression and normalization on GeoMx RNA data Description: A series of statistical models using count generating distributions for background modelling, feature and sample QC, normalization and differential expression analysis on GeoMx RNA data. The application of these methods are demonstrated by example data analysis vignette. biocViews: GeneExpression, DifferentialExpression, Normalization Author: Nicole Ortogero [cre], Lei Yang [aut], Zhi Yang [aut] Maintainer: Nicole Ortogero URL: https://github.com/Nanostring-Biostats/GeoDiff VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/GeoDiff git_url: https://git.bioconductor.org/packages/GeoDiff git_branch: RELEASE_3_16 git_last_commit: 33059fc git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/GeoDiff_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeoDiff_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GeoDiff_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeoDiff_1.4.2.tgz vignettes: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.html vignetteTitles: Workflow_WTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeoDiff/inst/doc/Workflow_WTA_kidney.R dependencyCount: 144 Package: GEOexplorer Version: 1.4.0 Depends: shiny, limma, Biobase, plotly, shinyBS Imports: DT, htmltools, factoextra, heatmaply, maptools, pheatmap, scales, shinyHeatmaply, shinybusy, ggplot2, stringr, umap, GEOquery, impute, grDevices, stats, graphics, utils Suggests: rmarkdown, knitr, usethis, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 3cd384a467347c1eb15659190e903939 NeedsCompilation: no Title: GEOexplorer: an R/Bioconductor package for gene expression analysis and visualisation Description: GEOexplorer is a Shiny app that enables exploratory data analysis and differential gene expression of gene expression analysis on microarray gene expression datasets held on the GEO database. The outputs are interactive graphs that enable users to explore the results of the analysis. The development of GEOexplorer was made possible because of the excellent code provided by GEO2R (https: //www.ncbi.nlm.nih.gov/geo/geo2r/). biocViews: Software, GeneExpression, mRNAMicroarray, DifferentialExpression, Microarray, MicroRNAArray Author: Guy Hunt [aut, cre] (), Rafael Henkin [ctb, ths] (), Fabrizio Smeraldi [ctb, ths] (), Michael Barnes [ctb, ths] () Maintainer: Guy Hunt URL: https://github.com/guypwhunt/GEOexplorer/ VignetteBuilder: knitr BugReports: https://github.com/guypwhunt/GEOexplorer/issues git_url: https://git.bioconductor.org/packages/GEOexplorer git_branch: RELEASE_3_16 git_last_commit: e59cd7e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEOexplorer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOexplorer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOexplorer_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEOexplorer_1.4.0.tgz vignettes: vignettes/GEOexplorer/inst/doc/GEOexplorer.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOexplorer/inst/doc/GEOexplorer.R dependencyCount: 188 Package: GEOfastq Version: 1.6.0 Imports: xml2, rvest, stringr, RCurl, doParallel, foreach, plyr Suggests: BiocCheck, roxygen2, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e846523e38a1aab4b08338cd06a3af52 NeedsCompilation: no Title: Downloads ENA Fastqs With GEO Accessions Description: GEOfastq is used to download fastq files from the European Nucleotide Archive (ENA) starting with an accession from the Gene Expression Omnibus (GEO). To do this, sample metadata is retrieved from GEO and the Sequence Read Archive (SRA). SRA run accessions are then used to construct FTP and aspera download links for fastq files generated by the ENA. biocViews: RNASeq, DataImport Author: Alex Pickering [cre, aut] () Maintainer: Alex Pickering VignetteBuilder: knitr BugReports: https://github.com/alexvpickering/GEOfastq/issues git_url: https://git.bioconductor.org/packages/GEOfastq git_branch: RELEASE_3_16 git_last_commit: 027d084 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEOfastq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOfastq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOfastq_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEOfastq_1.6.0.tgz vignettes: vignettes/GEOfastq/inst/doc/GEOfastq.html vignetteTitles: Using the GEOfastq Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOfastq/inst/doc/GEOfastq.R dependencyCount: 40 Package: GEOmetadb Version: 1.60.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 8559e4ea98e7a6a76de2af1fc9f17250 NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: RELEASE_3_16 git_last_commit: b7f0ffd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEOmetadb_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOmetadb_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOmetadb_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEOmetadb_1.60.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R importsMe: MetaIntegrator suggestsMe: antiProfilesData, maGUI dependencyCount: 54 Package: GeomxTools Version: 3.2.0 Depends: R (>= 3.6), Biobase, NanoStringNCTools, S4Vectors Imports: BiocGenerics, rjson, readxl, EnvStats, reshape2, methods, utils, stats, data.table, lmerTest, dplyr, stringr, grDevices, graphics, GGally, rlang, ggplot2, SeuratObject Suggests: rmarkdown, knitr, testthat (>= 3.0.0), parallel, ggiraph, Seurat, SpatialExperiment (>= 1.4.0), SpatialDecon, patchwork License: MIT MD5sum: cbfe2461c03f5e985f813a7fc41111e5 NeedsCompilation: no Title: NanoString GeoMx Tools Description: Tools for NanoString Technologies GeoMx Technology. Package provides functions for reading in DCC and PKC files based on an ExpressionSet derived object. Normalization and QC functions are also included. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq, Sequencing, ExperimentalDesign, Normalization, Spatial Author: Nicole Ortogero [cre, aut], Zhi Yang [aut], Ronalyn Vitancol [aut], Maddy Griswold [aut], David Henderson [aut] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeomxTools git_branch: RELEASE_3_16 git_last_commit: c0611f4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GeomxTools_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GeomxTools_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GeomxTools_3.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GeomxTools_3.2.0.tgz vignettes: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.html, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.html, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.html vignetteTitles: Developer Introduction to the NanoStringGeoMxSet, Coercion of GeoMxSet to Seurat and SpatialExperiment Objects, Protein data using GeomxTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeomxTools/inst/doc/Developer_Introduction_to_the_NanoStringGeoMxSet.R, vignettes/GeomxTools/inst/doc/GeomxSet_coercions.R, vignettes/GeomxTools/inst/doc/Protein_in_GeomxTools.R dependsOnMe: GeoMxWorkflows importsMe: GeoDiff, SpatialDecon dependencyCount: 134 Package: GEOquery Version: 2.66.0 Depends: methods, Biobase Imports: readr (>= 1.3.1), xml2, dplyr, data.table, tidyr, magrittr, limma, curl, R.utils Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr, markdown License: MIT MD5sum: 82a4255c26baa62569c2628133127c91 NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis [aut, cre] () Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery, http://seandavi.github.io/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_16 git_last_commit: 00a954e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEOquery_2.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOquery_2.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOquery_2.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEOquery_2.66.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE103322, GSE13015, GSE62944 importsMe: bigmelon, ChIPXpress, coexnet, conclus, crossmeta, DExMA, EGAD, EpiMix, GEOexplorer, minfi, MoonlightR, phantasus, recount, SRAdb, BeadArrayUseCases, BioPlex, GSE13015, healthyControlsPresenceChecker, easyDifferentialGeneCoexpression, geneExpressionFromGEO, MetaIntegrator, seeker suggestsMe: AUCell, autonomics, ctsGE, dearseq, debCAM, diffcoexp, dyebias, EpiDISH, fgsea, FLAMES, GCSscore, GeneExpressionSignature, GenomicOZone, methylclock, multiClust, MultiDataSet, omicsPrint, PCAtools, quantiseqr, RegEnrich, RGSEA, runibic, skewr, spatialHeatmap, TargetScore, zFPKM, airway, antiProfilesData, muscData, parathyroidSE, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, AnnoProbe, BED, fdrci, maGUI, metaMA, MLML2R, NACHO, TcGSA dependencyCount: 46 Package: GEOsubmission Version: 1.50.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: a0334348ecfd798e129e9e3b6240d449 NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: RELEASE_3_16 git_last_commit: 4969f37 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEOsubmission_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEOsubmission_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEOsubmission_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEOsubmission_1.50.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 12 Package: gep2pep Version: 1.18.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: c5a807db1b0564a83f6716447a5e3144 NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: RELEASE_3_16 git_last_commit: 77d2077 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gep2pep_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gep2pep_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gep2pep_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gep2pep_1.18.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R dependencyCount: 59 Package: gespeR Version: 1.30.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: f642a7edb7753c6a5de25f1a1dc715f7 NeedsCompilation: no Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gespeR git_branch: RELEASE_3_16 git_last_commit: 6213cb5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gespeR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gespeR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gespeR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gespeR_1.30.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R dependencyCount: 117 Package: getDEE2 Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: fc5f915e31ad3c70d8d709856812ecb4 NeedsCompilation: no Title: Programmatic access to the DEE2 RNA expression dataset Description: Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022). biocViews: GeneExpression, Transcriptomics, Sequencing Author: Mark Ziemann [aut, cre], Antony Kaspi [aut] Maintainer: Mark Ziemann URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: RELEASE_3_16 git_last_commit: 22480dc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-08 source.ver: src/contrib/getDEE2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/getDEE2_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/getDEE2_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/getDEE2_1.8.0.tgz vignettes: vignettes/getDEE2/inst/doc/getDEE2.html vignetteTitles: getDEE2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R dependencyCount: 26 Package: geva Version: 1.6.0 Depends: R (>= 4.1) Imports: grDevices, graphics, methods, stats, utils, dbscan, fastcluster, matrixStats Suggests: devtools, knitr, rmarkdown, roxygen2, limma, topGO, testthat (>= 3.0.0) License: LGPL-3 MD5sum: 0fd522e0a4162834ef540dfe0f27f124 NeedsCompilation: no Title: Gene Expression Variation Analysis (GEVA) Description: Statistic methods to evaluate variations of differential expression (DE) between multiple biological conditions. It takes into account the fold-changes and p-values from previous differential expression (DE) results that use large-scale data (*e.g.*, microarray and RNA-seq) and evaluates which genes would react in response to the distinct experiments. This evaluation involves an unique pipeline of statistical methods, including weighted summarization, quantile detection, cluster analysis, and ANOVA tests, in order to classify a subset of relevant genes whose DE is similar or dependent to certain biological factors. biocViews: Classification, DifferentialExpression, GeneExpression, Microarray, MultipleComparison, RNASeq, SystemsBiology, Transcriptomics Author: Itamar José Guimarães Nunes [aut, cre] (), Murilo Zanini David [ctb], Bruno César Feltes [ctb] (), Marcio Dorn [ctb] () Maintainer: Itamar José Guimarães Nunes URL: https://github.com/sbcblab/geva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geva git_branch: RELEASE_3_16 git_last_commit: 6f2e5d9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/geva_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/geva_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/geva_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/geva_1.6.0.tgz vignettes: vignettes/geva/inst/doc/geva.pdf vignetteTitles: GEVA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geva/inst/doc/geva.R dependencyCount: 9 Package: GEWIST Version: 1.42.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: edd7e6247133caf3efeb6803922a5ad7 NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: RELEASE_3_16 git_last_commit: 4d2e1b2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GEWIST_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GEWIST_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GEWIST_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GEWIST_1.42.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 78 Package: ggbio Version: 1.46.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi, GGally, ensembldb (>= 1.99.13), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 MD5sum: 5e79193a392e44b316a48971765cc65f NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: https://lawremi.github.io/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/lawremi/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_16 git_last_commit: d9c6cb4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ggbio_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggbio_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggbio_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggbio_1.46.0.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: BOBaFIT, cageminer, derfinderPlot, FLAMES, GenomicOZone, msgbsR, R3CPET, ReportingTools, RiboProfiling, scruff, SomaticSignatures, ggcoverage, MOCHA suggestsMe: bambu, beadarray, ensembldb, gwascat, interactiveDisplay, NanoStringNCTools, Pi, regionReport, RnBeads, StructuralVariantAnnotation, universalmotif, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 156 Package: ggcyto Version: 1.26.4 Depends: methods, ggplot2(>= 3.3.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.33.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: file LICENSE MD5sum: efe65ac53a07caac9654cd73c674a8f6 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_16 git_last_commit: 114d68d git_last_commit_date: 2022-12-13 Date/Publication: 2022-12-13 source.ver: src/contrib/ggcyto_1.26.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggcyto_1.26.4.zip mac.binary.ver: bin/macosx/contrib/4.2/ggcyto_1.26.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggcyto_1.26.4.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R importsMe: CytoML suggestsMe: CATALYST, flowCore, flowStats, flowWorkspace, openCyto dependencyCount: 65 Package: ggmanh Version: 1.2.0 Depends: methods, ggplot2 Imports: gdsfmt, ggrepel, grDevices, RColorBrewer, rlang, scales, SeqArray (>= 1.32.0), stats Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0), markdown, GenomicRanges License: MIT + file LICENSE MD5sum: 5074934ed49281781bcced61d8ec68b4 NeedsCompilation: no Title: Visualization Tool for GWAS Result Description: Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints. biocViews: Visualization, GenomeWideAssociation, Genetics Author: John Lee [aut, cre], Xiuwen Zheng [ctb, dtc] Maintainer: John Lee VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggmanh git_branch: RELEASE_3_16 git_last_commit: ad27804 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ggmanh_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggmanh_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggmanh_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggmanh_1.2.0.tgz vignettes: vignettes/ggmanh/inst/doc/ggmanh.html vignetteTitles: Guide to ggmanh Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggmanh/inst/doc/ggmanh.R suggestsMe: SAIGEgds dependencyCount: 54 Package: ggmsa Version: 1.4.0 Depends: R (>= 4.1.0) Imports: Biostrings, ggplot2, magrittr, tidyr, utils, stats, aplot, RColorBrewer, ggalt, ggforce, dplyr, R4RNA, grDevices, seqmagick, grid, methods, statebins, ggtree (>= 1.17.1) Suggests: ggtreeExtra, ape, cowplot, knitr, BiocStyle, rmarkdown, readxl, ggnewscale, kableExtra, gggenes, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 4834345fed2f3deb95c4315693089506 NeedsCompilation: no Title: Plot Multiple Sequence Alignment using 'ggplot2' Description: A visual exploration tool for multiple sequence alignment and associated data. Supports MSA of DNA, RNA, and protein sequences using 'ggplot2'. Multiple sequence alignment can easily be combined with other 'ggplot2' plots, such as phylogenetic tree Visualized by 'ggtree', boxplot, genome map and so on. More features: visualization of sequence logos, sequence bundles, RNA secondary structures and detection of sequence recombinations. biocViews: Software, Visualization, Alignment, Annotation, MultipleSequenceAlignment Author: Lang Zhou [aut, cre], Guangchuang Yu [aut, ths] (), Shuangbin Xu [ctb], Huina Huang [ctb] Maintainer: Lang Zhou URL: https://doi.org/10.1093/bib/bbac222(paper), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggmsa/issues git_url: https://git.bioconductor.org/packages/ggmsa git_branch: RELEASE_3_16 git_last_commit: b158da3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ggmsa_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggmsa_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggmsa_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggmsa_1.4.0.tgz vignettes: vignettes/ggmsa/inst/doc/ggmsa.html vignetteTitles: ggmsa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggmsa/inst/doc/ggmsa.R dependencyCount: 118 Package: GGPA Version: 1.10.2 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) MD5sum: 6e93368eebe9ad5288567a8f57bd6b92 NeedsCompilation: yes Title: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture Description: Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Hang J. Kim, Carter Allen Maintainer: Dongjun Chung URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: RELEASE_3_16 git_last_commit: 0a90c7f git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/GGPA_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GGPA_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GGPA_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GGPA_1.10.2.tgz vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf vignetteTitles: GGPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R dependencyCount: 60 Package: ggspavis Version: 1.4.0 Depends: ggplot2 Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, ggside, grid, methods, stats Suggests: BiocStyle, rmarkdown, knitr, STexampleData, BumpyMatrix, scater, scran, uwot, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 7e6273e3c7e5923662d01a5ae40727fd NeedsCompilation: no Title: Visualization functions for spatially resolved transcriptomics data Description: Visualization functions for spatially resolved transcriptomics datasets stored in SpatialExperiment format. Includes functions to create several types of plots for data from from spot-based (e.g. 10x Genomics Visium) and molecule-based (e.g. seqFISH) technological platforms. biocViews: SingleCell, Transcriptomics, Spatial Author: Lukas M. Weber [aut, cre] (), Helena L. Crowell [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/ggspavis VignetteBuilder: knitr BugReports: https://github.com/lmweber/ggspavis/issues git_url: https://git.bioconductor.org/packages/ggspavis git_branch: RELEASE_3_16 git_last_commit: 70de5e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ggspavis_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggspavis_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggspavis_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggspavis_1.4.0.tgz vignettes: vignettes/ggspavis/inst/doc/ggspavis_overview.html vignetteTitles: ggspavis overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ggspavis/inst/doc/ggspavis_overview.R dependencyCount: 112 Package: ggtree Version: 3.6.2 Depends: R (>= 3.5.0) Imports: ape, aplot, dplyr, ggplot2 (> 3.3.6), grid, magrittr, methods, purrr, rlang, ggfun (>= 0.0.6), yulab.utils, tidyr, tidytree (>= 0.3.9), treeio (>= 1.8.0), utils, scales, stats, cli Suggests: emojifont, ggimage, ggplotify, shadowtext, grDevices, knitr, prettydoc, rmarkdown, testthat, tibble, glue License: Artistic-2.0 MD5sum: 5a79326c18bd6ba525cfff8fdec991a2 NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (), Lin Li [ctb], Bradley Jones [ctb], Justin Silverman [ctb], Watal M. Iwasaki [ctb], Yonghe Xia [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12628 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: RELEASE_3_16 git_last_commit: 431ec37 git_last_commit_date: 2022-11-09 Date/Publication: 2022-11-10 source.ver: src/contrib/ggtree_3.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggtree_3.6.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ggtree_3.6.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggtree_3.6.2.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R dependsOnMe: ggtreeDendro, tanggle importsMe: cardelino, cogeqc, enrichplot, ggmsa, ggtreeExtra, LinTInd, LymphoSeq, miaViz, microbiomeMarker, MicrobiotaProcess, orthogene, philr, scBubbletree, singleCellTK, sitePath, systemPipeTools, treekoR, dowser, EvoPhylo, genBaRcode, ggmotif, harrietr, numbat, Platypus, RevGadgets, scistreer, shinyTempSignal, STraTUS, Sysrecon suggestsMe: compcodeR, syntenet, TreeAndLeaf, treeio, TreeSummarizedExperiment, universalmotif, aplot, CoOL, DAISIE, deeptime, ggimage, idiogramFISH, nosoi, oppr, PCMBase, RAINBOWR, rhierbaps dependencyCount: 58 Package: ggtreeDendro Version: 1.0.0 Depends: ggtree (>= 3.5.3) Imports: ggplot2, stats, tidytree Suggests: aplot, cluster, knitr, MASS, mdendro, prettydoc, pvclust, rmarkdown, testthat (>= 3.0.0), treeio, yulab.utils License: Artistic-2.0 MD5sum: d3cf8188d0b7922c1ccb01f4e2b386d5 NeedsCompilation: no Title: Drawing 'dendrogram' using 'ggtree' Description: Offers a set of 'autoplot' methods to visualize tree-like structures (e.g., hierarchical clustering and classification/regression trees) using 'ggtree'. You can adjust graphical parameters using grammar of graphic syntax and integrate external data to the tree. biocViews: Clustering, Classification, DecisionTree, Phylogenetics, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Shuangbin Xu [ctb] (), Chuanjie Zhang [ctb] Maintainer: Guangchuang Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggtreeDendro git_branch: RELEASE_3_16 git_last_commit: 645a8eb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ggtreeDendro_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggtreeDendro_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ggtreeDendro_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggtreeDendro_1.0.0.tgz vignettes: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.html vignetteTitles: Visualizing Dendrogram using ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeDendro/inst/doc/ggtreeDendro.R dependencyCount: 59 Package: ggtreeExtra Version: 1.8.1 Imports: ggplot2, utils, rlang, ggnewscale, stats, ggtree, tidytree (>= 0.3.9), cli Suggests: treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown, testthat (>= 3.0.0), pillar License: GPL (>= 3) MD5sum: 918e2c2cab73fb6c1df0574ee8ae1fcb NeedsCompilation: no Title: An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree" Description: 'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. biocViews: Software, Visualization, Phylogenetics, Annotation Author: Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/ggtreeExtra/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues git_url: https://git.bioconductor.org/packages/ggtreeExtra git_branch: RELEASE_3_16 git_last_commit: 8cd3f86 git_last_commit_date: 2022-11-08 Date/Publication: 2022-11-08 source.ver: src/contrib/ggtreeExtra_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ggtreeExtra_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ggtreeExtra_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ggtreeExtra_1.8.1.tgz vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html vignetteTitles: ggtreeExtra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R importsMe: MicrobiotaProcess suggestsMe: enrichplot, ggmsa dependencyCount: 60 Package: GIGSEA Version: 1.16.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 859ade235dfbc37d1c425dfc2af903d6 NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: RELEASE_3_16 git_last_commit: 8784717 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GIGSEA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GIGSEA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GIGSEA_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GIGSEA_1.16.0.tgz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R suggestsMe: GIGSEAdata dependencyCount: 11 Package: girafe Version: 1.50.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1), ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings (>= 2.47.6), graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 MD5sum: d6002b60c4bfdfbc7d96d6b054399587 NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/girafe git_branch: RELEASE_3_16 git_last_commit: 18b1d36 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/girafe_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/girafe_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/girafe_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/girafe_1.50.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R dependencyCount: 53 Package: GISPA Version: 1.22.0 Depends: R (>= 3.5) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 Archs: x64 MD5sum: 2673bba202cbaddeae1ffaab1fc03c44 NeedsCompilation: no Title: GISPA: Method for Gene Integrated Set Profile Analysis Description: GISPA is a method intended for the researchers who are interested in defining gene sets with similar, a priori specified molecular profile. GISPA method has been previously published in Nucleic Acid Research (Kowalski et al., 2016; PMID: 26826710). biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GISPA git_branch: RELEASE_3_16 git_last_commit: 7cb632d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GISPA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GISPA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GISPA_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GISPA_1.22.0.tgz vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R dependencyCount: 138 Package: GLAD Version: 2.62.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 MD5sum: 7b90560d4c9ed3e1bb6875ac4c12d95d NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: RELEASE_3_16 git_last_commit: 48f2c31 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GLAD_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GLAD_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GLAD_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GLAD_2.62.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ADaCGH2, ITALICS, seqCNA importsMe: ITALICS, MANOR, snapCGH suggestsMe: RnBeads, aroma.cn, aroma.core dependencyCount: 4 Package: GladiaTOX Version: 1.14.0 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMySQL, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 Archs: x64 MD5sum: b7fc6774f1ff8048cd714b6551b90fa8 NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: RELEASE_3_16 git_last_commit: b80ea3c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GladiaTOX_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GladiaTOX_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GladiaTOX_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GladiaTOX_1.14.0.tgz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 65 Package: Glimma Version: 2.8.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr, AnnotationHub, scRNAseq, scater, scran License: GPL-3 MD5sum: 74140d23faf6a6fd0a7251a8a9ef5794 NeedsCompilation: no Title: Interactive HTML graphics Description: This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb] Maintainer: Shian Su URL: https://github.com/hasaru-k/GlimmaV2 VignetteBuilder: knitr BugReports: https://github.com/hasaru-k/GlimmaV2/issues git_url: https://git.bioconductor.org/packages/Glimma git_branch: RELEASE_3_16 git_last_commit: 09cec82 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Glimma_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Glimma_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Glimma_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Glimma_2.8.0.tgz vignettes: vignettes/Glimma/inst/doc/DESeq2.html, vignettes/Glimma/inst/doc/limma_edger.html, vignettes/Glimma/inst/doc/single_cell_edger.html vignetteTitles: DESeq2, Introduction using limma or edgeR, Single Cells with edgeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/DESeq2.R, vignettes/Glimma/inst/doc/limma_edger.R, vignettes/Glimma/inst/doc/single_cell_edger.R dependsOnMe: RNAseq123 importsMe: affycoretools dependencyCount: 113 Package: glmGamPoi Version: 1.10.2 Imports: Rcpp, DelayedMatrixStats, matrixStats, MatrixGenerics, DelayedArray, HDF5Array, SummarizedExperiment, SingleCellExperiment, BiocGenerics, methods, stats, utils, splines, rlang LinkingTo: Rcpp, RcppArmadillo, beachmat Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, beachmat, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, muscData, scran, Matrix License: GPL-3 MD5sum: 5992b12b000d0b39b4f7cac0c19ad513 NeedsCompilation: yes Title: Fit a Gamma-Poisson Generalized Linear Model Description: Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. biocViews: Regression, RNASeq, Software, SingleCell Author: Constantin Ahlmann-Eltze [aut, cre] (), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues git_url: https://git.bioconductor.org/packages/glmGamPoi git_branch: RELEASE_3_16 git_last_commit: efcb989 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/glmGamPoi_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/glmGamPoi_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/glmGamPoi_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/glmGamPoi_1.10.2.tgz vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R importsMe: BASiCStan, transformGamPoi suggestsMe: DESeq2 dependencyCount: 37 Package: glmSparseNet Version: 1.16.0 Depends: R (>= 4.1), Matrix, MultiAssayExperiment, glmnet Imports: SummarizedExperiment, biomaRt, futile.logger, futile.options, forcats, utils, dplyr, glue, readr, digest, httr, ggplot2, survminer, reshape2, stringr, parallel, methods Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils License: GPL-3 Archs: x64 MD5sum: 00af606f3239fb05105c60a29c736378 NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues git_url: https://git.bioconductor.org/packages/glmSparseNet git_branch: RELEASE_3_16 git_last_commit: 01657cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/glmSparseNet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/glmSparseNet_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/glmSparseNet_1.15.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/glmSparseNet_1.16.0.tgz vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html, vignettes/glmSparseNet/inst/doc/example_brca_survival.html, vignettes/glmSparseNet/inst/doc/example_prad_survival.html, vignettes/glmSparseNet/inst/doc/example_skcm_survival.html, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.html vignetteTitles: Example for Classification -- Breast Invasive Carcinoma, Breast survival dataset using network from STRING DB, Example for Survival Data -- Breast Invasive Carcinoma, Example for Survival Data -- Prostate Adenocarcinoma, Example for Survival Data -- Skin Melanoma, Separate 2 groups in Cox regression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R, vignettes/glmSparseNet/inst/doc/example_brca_survival.R, vignettes/glmSparseNet/inst/doc/example_prad_survival.R, vignettes/glmSparseNet/inst/doc/example_skcm_survival.R, vignettes/glmSparseNet/inst/doc/separate2GroupsCox.R dependencyCount: 172 Package: GlobalAncova Version: 4.16.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) MD5sum: dd4a24f2b8ab82cf1ff28999218b1066 NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: RELEASE_3_16 git_last_commit: 9f0109b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GlobalAncova_4.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GlobalAncova_4.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GlobalAncova_4.16.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R importsMe: miRtest dependencyCount: 82 Package: globalSeq Version: 1.26.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 Archs: x64 MD5sum: b6809aaf41e720ad6e3b0fcaaa8f8d20 NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: RELEASE_3_16 git_last_commit: ddccaba git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/globalSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/globalSeq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/globalSeq_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/globalSeq_1.26.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.52.1 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) MD5sum: 2573a81dd323c36f70a1015e997b97d9 NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari, Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: RELEASE_3_16 git_last_commit: ede9563 git_last_commit_date: 2023-03-20 Date/Publication: 2023-03-20 source.ver: src/contrib/globaltest_5.52.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/globaltest_5.52.1.zip mac.binary.ver: bin/macosx/contrib/4.2/globaltest_5.52.1.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM, miRtest, SlaPMEG suggestsMe: topGO, penalized dependencyCount: 54 Package: gmapR Version: 1.40.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: 5a9fabd1b591f665b77a2b69a3565e80 NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: RELEASE_3_16 git_last_commit: ba9a187 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gmapR_1.40.0.tar.gz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie importsMe: MMAPPR2 suggestsMe: VariantTools, VariantToolsData dependencyCount: 98 Package: GmicR Version: 1.12.0 Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel, foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics, reshape2, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE MD5sum: c585e49d51b3c37b07f968244ea1196f NeedsCompilation: no Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: RELEASE_3_16 git_last_commit: 729668d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GmicR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GmicR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GmicR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GmicR_1.12.0.tgz vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html vignetteTitles: GmicR_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R dependencyCount: 155 Package: gmoviz Version: 1.10.0 Depends: circlize, GenomicRanges, graphics, R (>= 4.0) Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics, Biostrings, GenomeInfoDb, methods, GenomicAlignments, GenomicFeatures, IRanges, rtracklayer, pracma, colorspace, S4Vectors Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle, BiocManager License: GPL-3 MD5sum: e1474f1530946f92ba8b73e31411ab47 NeedsCompilation: no Title: Seamless visualization of complex genomic variations in GMOs and edited cell lines Description: Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale. biocViews: Visualization, Sequencing, GeneticVariability, GenomicVariation, Coverage Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther Alhamdoosh [aut] (), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: RELEASE_3_16 git_last_commit: 52ebafb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gmoviz_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gmoviz_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gmoviz_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gmoviz_1.10.0.tgz vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html, vignettes/gmoviz/inst/doc/gmoviz_overview.html vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R, vignettes/gmoviz/inst/doc/gmoviz_overview.R dependencyCount: 110 Package: GMRP Version: 1.26.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: a063013e66049ad6996189ea2592b975 NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: RELEASE_3_16 git_last_commit: 712f073 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GMRP_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GMRP_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GMRP_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GMRP_1.26.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 21 Package: GNET2 Version: 1.14.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph, grDevices, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 MD5sum: 49a12c45df8be6aa099e18f6d859c09b NeedsCompilation: yes Title: Constructing gene regulatory networks from expression data through functional module inference Description: Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached. biocViews: GeneExpression, Regression, Network, NetworkInference, Software Author: Chen Chen, Jie Hou and Jianlin Cheng Maintainer: Chen Chen URL: https://github.com/chrischen1/GNET2 VignetteBuilder: knitr BugReports: https://github.com/chrischen1/GNET2/issues git_url: https://git.bioconductor.org/packages/GNET2 git_branch: RELEASE_3_16 git_last_commit: 6fe8543 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GNET2_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GNET2_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GNET2_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GNET2_1.14.0.tgz vignettes: vignettes/GNET2/inst/doc/run_gnet2.html vignetteTitles: Build functional gene modules with GNET2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R dependencyCount: 106 Package: GOexpress Version: 1.32.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) Archs: x64 MD5sum: cfb6ee0bc0be19c2b1c6a7e51a735cd8 NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: RELEASE_3_16 git_last_commit: 071bfb7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOexpress_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOexpress_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOexpress_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOexpress_1.32.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 92 Package: GOfuncR Version: 1.18.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, markdown, rmarkdown, testthat License: GPL (>= 2) MD5sum: 4157c08d56c092708fa72cc03af904eb NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (01-May-2021). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: RELEASE_3_16 git_last_commit: 4918241 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOfuncR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOfuncR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOfuncR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOfuncR_1.18.0.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R dependencyCount: 55 Package: GOpro Version: 1.24.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 Archs: x64 MD5sum: 2fdea8165698e37c4b45bca4d8b236c3 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues git_url: https://git.bioconductor.org/packages/GOpro git_branch: RELEASE_3_16 git_last_commit: 7f6cb6f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOpro_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOpro_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOpro_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOpro_1.24.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R dependencyCount: 96 Package: goProfiles Version: 1.60.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 Archs: x64 MD5sum: 05307b9d9a21a158178dfe30101b286a NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: RELEASE_3_16 git_last_commit: f8ad359 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/goProfiles_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goProfiles_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goProfiles_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/goProfiles_1.60.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R importsMe: goSorensen dependencyCount: 51 Package: GOSemSim Version: 2.24.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat, ROCR License: Artistic-2.0 MD5sum: 3c42cebf69b137c02f1e8044ba2d51d3 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Pingfan Guo [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: RELEASE_3_16 git_last_commit: ed7334f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOSemSim_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOSemSim_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOSemSim_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOSemSim_2.24.0.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome, BiSEp importsMe: clusterProfiler, DOSE, enrichplot, meshes, Rcpi, rrvgo, simplifyEnrichment, ViSEAGO suggestsMe: BioCor, epiNEM, FELLA, SemDist, genekitr, protr, rDNAse dependencyCount: 48 Package: goseq Version: 1.50.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) Archs: x64 MD5sum: 523dc7a0592832e5f6f757ce4e6b95af NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Matthew Young , Nadia Davidson git_url: https://git.bioconductor.org/packages/goseq git_branch: RELEASE_3_16 git_last_commit: f9fad23 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/goseq_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goseq_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goseq_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/goseq_1.50.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: ideal, SMITE suggestsMe: sparrow dependencyCount: 102 Package: GOSim Version: 1.36.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) MD5sum: fb033584663836b7922a42695c2f7782 NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich git_url: https://git.bioconductor.org/packages/GOSim git_branch: RELEASE_3_16 git_last_commit: 7e72cf6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOSim_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOSim_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOSim_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOSim_1.36.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R dependencyCount: 66 Package: goSorensen Version: 1.0.0 Depends: R (>= 4.2.0) Imports: GO.db, org.Hs.eg.db, goProfiles, stats, clusterProfiler, parallel Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: ebeeb3eb07dcb6cd28e1e19b0ae29a28 NeedsCompilation: no Title: Statistical inference based on the Sorensen-Dice dissimilarity and the Gene Ontology (GO) Description: This package implements inferential methods to compare gene lists (in this first release, to prove equivalence) in terms of their biological meaning as expressed in the GO. The compared gene lists are characterized by cross-tabulation frequency tables of enriched GO items. Dissimilarity between gene lists is evaluated using the Sorensen-Dice index. The fundamental guiding principle is that two gene lists are taken as similar if they share a great proportion of common enriched GO items. biocViews: Annotation, GO, GeneSetEnrichment, Software, Microarray, Pathways, GeneExpression, MultipleComparison, GraphAndNetwork, Reactome, Clustering, KEGG Author: Pablo Flores [aut, cre] (), Jordi Ocana [aut, ctb] (0000-0002-4736-699), Alexandre Sanchez-Pla [ctb] (), Miquel Salicru [ctb] () Maintainer: Pablo Flores VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSorensen git_branch: RELEASE_3_16 git_last_commit: a78ba7a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/goSorensen_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goSorensen_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goSorensen_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/goSorensen_1.0.0.tgz vignettes: vignettes/goSorensen/inst/doc/goSorensen_Introduction.html vignetteTitles: An introduction to equivalence test between feature lists using goSorensen. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSorensen/inst/doc/goSorensen_Introduction.R dependencyCount: 132 Package: goSTAG Version: 1.22.0 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 9380f7017563d69beefbbc5d3052c909 NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: RELEASE_3_16 git_last_commit: 20e7a5e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/goSTAG_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goSTAG_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goSTAG_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/goSTAG_1.22.0.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 71 Package: GOstats Version: 2.64.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2d3d3bcf7a1465b915c7733b1df3edaa NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GOstats git_branch: RELEASE_3_16 git_last_commit: 6281325 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOstats_2.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOstats_2.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOstats_2.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOstats_2.64.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA, PloGO2 importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, MIGSA, miRLAB, netZooR, pcaExplorer, scTensor, DNLC suggestsMe: a4, Category, fastLiquidAssociation, fgga, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, DGCA, maGUI, sand dependencyCount: 63 Package: GOsummaries Version: 2.34.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) MD5sum: d9d36a04f95941465e0df3aae6765284 NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries git_url: https://git.bioconductor.org/packages/GOsummaries git_branch: RELEASE_3_16 git_last_commit: c8edad8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOsummaries_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOsummaries_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOsummaries_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOsummaries_2.34.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R dependencyCount: 45 Package: GOTHiC Version: 1.34.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, GenomeInfoDb Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 Archs: x64 MD5sum: dacdf9a96961f7380b9c7af6932655d3 NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: RELEASE_3_16 git_last_commit: 58e54f9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GOTHiC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GOTHiC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GOTHiC_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GOTHiC_1.34.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R dependencyCount: 85 Package: goTools Version: 1.72.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 8f53950b070ae1b49661666205e4c8c9 NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: RELEASE_3_16 git_last_commit: 82ba948 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/goTools_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/goTools_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/goTools_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/goTools_1.72.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 47 Package: GPA Version: 1.10.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) MD5sum: 5b0def7c73be79ea5bb5471c4fb8a4f3 NeedsCompilation: yes Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation) Description: This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Emma Kortemeier, Carter Allen Maintainer: Dongjun Chung URL: http://dongjunchung.github.io/GPA/ SystemRequirements: GNU make BugReports: https://github.com/dongjunchung/GPA/issues git_url: https://git.bioconductor.org/packages/GPA git_branch: RELEASE_3_16 git_last_commit: 3b8838f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GPA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GPA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GPA_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GPA_1.10.0.tgz vignettes: vignettes/GPA/inst/doc/GPA-example.pdf vignetteTitles: GPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GPA/inst/doc/GPA-example.R dependencyCount: 80 Package: gpls Version: 1.70.0 Imports: stats Suggests: MASS License: Artistic-2.0 Archs: x64 MD5sum: bf8637d55f183d264b86a57be6f13729 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: RELEASE_3_16 git_last_commit: e33d171 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gpls_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gpls_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gpls_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gpls_1.70.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MLInterfaces dependencyCount: 1 Package: gpuMagic Version: 1.14.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 40a24b110193b39396861d94f94b5d21 NeedsCompilation: yes Title: An openCL compiler with the capacity to compile R functions and run the code on GPU Description: The package aims to help users write openCL code with little or no effort. It is able to compile an user-defined R function and run it on a device such as a CPU or a GPU. The user can also write and run their openCL code directly by calling .kernel function. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD loader For Windows user, an ICD loader is required at C:/windows/system32/OpenCL.dll (Usually it is installed by the graphic driver). For Linux user (Except mac): ocl-icd-opencl-dev package is required. For Mac user, no action is needed for the system has installed the dependency. 4. GNU make VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues git_url: https://git.bioconductor.org/packages/gpuMagic git_branch: RELEASE_3_16 git_last_commit: 22d30da git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gpuMagic_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gpuMagic_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gpuMagic_1.14.0.tgz vignettes: vignettes/gpuMagic/inst/doc/Customized-openCL-code.html, vignettes/gpuMagic/inst/doc/Quick_start_guide.html vignetteTitles: Customized_opencl_code, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpuMagic/inst/doc/Customized-openCL-code.R, vignettes/gpuMagic/inst/doc/Quick_start_guide.R dependencyCount: 61 Package: GRaNIE Version: 1.2.7 Depends: R (>= 4.2.0) Imports: futile.logger, checkmate, patchwork, reshape2, data.table, matrixStats, Matrix, GenomicRanges, RColorBrewer, ComplexHeatmap, DESeq2, circlize, progress, utils, methods, stringr, scales, igraph, S4Vectors, ggplot2, rlang, Biostrings, GenomeInfoDb (>= 1.34.8), SummarizedExperiment, forcats, gridExtra, limma, tidyselect, readr, grid, tidyr, dplyr, stats, grDevices, graphics, magrittr, tibble, viridis, colorspace, biomaRt, topGO Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Rnorvegicus.UCSC.rn7, BSgenome.Dmelanogaster.UCSC.dm6, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Rnorvegicus.UCSC.rn7.refGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dm.eg.db, IHW, clusterProfiler, ReactomePA, DOSE, BiocFileCache, ChIPseeker, testthat (>= 3.0.0), BiocStyle, csaw, BiocParallel, robust, variancePartition, purrr, EDASeq, JASPAR2022, TFBSTools, motifmatchr, rbioapi License: Artistic-2.0 Archs: x64 MD5sum: f5276b64460bd269e4d9c912b25ae89c NeedsCompilation: no Title: GRaNIE: Reconstruction cell type specific gene regulatory networks including enhancers using chromatin accessibility and RNA-seq data Description: Genetic variants associated with diseases often affect non-coding regions, thus likely having a regulatory role. To understand the effects of genetic variants in these regulatory regions, identifying genes that are modulated by specific regulatory elements (REs) is crucial. The effect of gene regulatory elements, such as enhancers, is often cell-type specific, likely because the combinations of transcription factors (TFs) that are regulating a given enhancer have celltype specific activity. This TF activity can be quantified with existing tools such as diffTF and captures differences in binding of a TF in open chromatin regions. Collectively, this forms a gene regulatory network (GRN) with cell-type and data-specific TF-RE and RE-gene links. Here, we reconstruct such a GRN using bulk RNAseq and open chromatin (e.g., using ATACseq or ChIPseq for open chromatin marks) and optionally TF activity data. Our network contains different types of links, connecting TFs to regulatory elements, the latter of which is connected to genes in the vicinity or within the same chromatin domain (TAD). We use a statistical framework to assign empirical FDRs and weights to all links using a permutation-based approach. biocViews: Software, GeneExpression, GeneRegulation, NetworkInference, GeneSetEnrichment, BiomedicalInformatics, Genetics, Transcriptomics, ATACSeq, RNASeq, GraphAndNetwork, Regression, Transcription, ChIPSeq Author: Christian Arnold [cre, aut], Judith Zaugg [aut], Rim Moussa [aut], Armando Reyes-Palomares [ctb], Giovanni Palla [ctb], Maksim Kholmatov [ctb] Maintainer: Christian Arnold URL: https://grp-zaugg.embl-community.io/GRaNIE VignetteBuilder: knitr BugReports: https://git.embl.de/grp-zaugg/GRaNIE/issues git_url: https://git.bioconductor.org/packages/GRaNIE git_branch: RELEASE_3_16 git_last_commit: 97c6ab9 git_last_commit_date: 2023-04-06 Date/Publication: 2023-04-07 source.ver: src/contrib/GRaNIE_1.2.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRaNIE_1.2.7.zip mac.binary.ver: bin/macosx/contrib/4.2/GRaNIE_1.2.7.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GRaNIE_1.2.7.tgz vignettes: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.html, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.html vignetteTitles: Package Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRaNIE/inst/doc/GRaNIE_packageDetails.R, vignettes/GRaNIE/inst/doc/GRaNIE_workflow.R dependencyCount: 138 Package: granulator Version: 1.6.0 Depends: R (>= 4.1) Imports: cowplot, e1071, epiR, dplyr, dtangle, ggplot2, ggplotify, grDevices, limSolve, magrittr, MASS, nnls, parallel, pheatmap, purrr, rlang, stats, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 916a2ba21965ecadf34bf2bf63f7e669 NeedsCompilation: no Title: Rapid benchmarking of methods for *in silico* deconvolution of bulk RNA-seq data Description: granulator is an R package for the cell type deconvolution of heterogeneous tissues based on bulk RNA-seq data or single cell RNA-seq expression profiles. The package provides a unified testing interface to rapidly run and benchmark multiple state-of-the-art deconvolution methods. Data for the deconvolution of peripheral blood mononuclear cells (PBMCs) into individual immune cell types is provided as well. biocViews: RNASeq, GeneExpression, DifferentialExpression, Transcriptomics, SingleCell, StatisticalMethod, Regression Author: Sabina Pfister [aut, cre], Vincent Kuettel [aut], Enrico Ferrero [aut] Maintainer: Sabina Pfister URL: https://github.com/xanibas/granulator VignetteBuilder: knitr BugReports: https://github.com/xanibas/granulator/issues git_url: https://git.bioconductor.org/packages/granulator git_branch: RELEASE_3_16 git_last_commit: 9b93536 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/granulator_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/granulator_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/granulator_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/granulator_1.6.0.tgz vignettes: vignettes/granulator/inst/doc/granulator.html vignetteTitles: Deconvoluting bulk RNA-seq data with granulator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/granulator/inst/doc/granulator.R suggestsMe: deconvR dependencyCount: 126 Package: graper Version: 1.14.2 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 531ef9c3c80e8114b7a43e804d7d831f NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: RELEASE_3_16 git_last_commit: 66300d5 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/graper_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/graper_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/graper_1.14.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/graper_1.14.2.tgz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 40 Package: graph Version: 1.76.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster, BiocStyle, knitr Enhances: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: badc7573679f44bb44d3479b64634b83 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R Gentleman [aut], Elizabeth Whalen [aut], W Huber [aut], S Falcon [aut], Halimat C. Atanda [ctb] (Converted 'GraphClass' vignette from Sweave to RMarkdown / HTML.), Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_16 git_last_commit: e3efc10 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/graph_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/graph_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/graph_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/graph_1.76.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf, vignettes/graph/inst/doc/GraphClass.html vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph Objects, graphBAM and MultiGraph classes, Graph Design hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, gaggle, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, pathRender, Pigengene, pkgDepTools, PoTRA, RbcBook1, RBGL, RBioinf, RCyjs, Rgraphviz, ROntoTools, SRAdb, topGO, vtpnet, SNAData, yeastExpData, cyjShiny, dlsem, gridGraphviz, GUIProfiler, hasseDiagram, PairViz, PerfMeas, SubpathwayLNCE importsMe: alpine, AnnotationHubData, BgeeDB, BiocCheck, BiocFHIR, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, bnem, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, consICA, CytoML, dce, DEGraph, DEsubs, epiNEM, EventPointer, fgga, flowCL, flowClust, flowWorkspace, gage, GeneNetworkBuilder, GenomicInteractionNodes, GOSim, GraphAT, graphite, hyperdraw, KEGGgraph, keggorthology, MIGSA, mnem, NCIgraph, NeighborNet, netresponse, OncoSimulR, ontoProc, oposSOM, OrganismDbi, pathview, PFP, PhenStat, pkgDepTools, pwOmics, qpgraph, RCy3, RGraph2js, rsbml, Rtreemix, SplicingGraphs, Streamer, trackViewer, VariantFiltering, BioPlex, abn, BayesNetBP, BiDAG, bnClustOmics, BNrich, ceg, CePa, classGraph, CodeDepends, cogmapr, dnet, eulerian, ggm, GGRidge, gRain, gRbase, gridDebug, gRim, HEMDAG, HydeNet, kpcalg, net4pg, netgsa, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, rsolr, rSpectral, SEMgraph, simPATHy, SourceSet, stablespec, topologyGSA, tpc, unifDAG, wiseR, zenplots suggestsMe: AnnotationDbi, DAPAR, DEGraph, EBcoexpress, ecolitk, gwascat, KEGGlincs, MLP, NetPathMiner, rBiopaxParser, RCX, rTRM, S4Vectors, SPIA, VariantTools, arulesViz, bnclassify, bnlearn, bnstruct, bsub, ChoR, epoc, gbutils, GeneNet, gMCP, igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, sparsebnUtils, textplot, tidygraph dependencyCount: 6 Package: GraphAlignment Version: 1.62.0 License: file LICENSE License_restricts_use: yes Archs: x64 MD5sum: edb0f719d8f2d58aae054c8ebb35b568 NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: RELEASE_3_16 git_last_commit: 9b44495 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GraphAlignment_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GraphAlignment_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GraphAlignment_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GraphAlignment_1.62.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.70.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL Archs: x64 MD5sum: 67134f039471963a570c196759c77685 NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: RELEASE_3_16 git_last_commit: bd578b4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GraphAT_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GraphAT_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GraphAT_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GraphAT_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 20 Package: graphite Version: 1.44.0 Depends: R (>= 4.2), methods Imports: AnnotationDbi, graph (>= 1.67.1), httr, rappdirs, stats, utils, graphics, rlang, purrr Suggests: checkmate, a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 MD5sum: 499ad4b02115052f093d031b238a4e2d NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from KEGG, Panther, PathBank, PharmGKB, Reactome SMPDB and WikiPathways databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, Metabolomics Author: Gabriele Sales [cre], Enrica Calura [aut], Chiara Romualdi [aut] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/graphite VignetteBuilder: R.rsp BugReports: https://github.com/sales-lab/graphite/issues git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_16 git_last_commit: 0540a6c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/graphite_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/graphite_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/graphite_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/graphite_1.44.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: PoTRA importsMe: dce, EnrichmentBrowser, mogsa, multiGSEA, ReactomePA, sSNAPPY, StarBioTrek, ICDS, netgsa suggestsMe: clipper, InterCellar, metaboliteIDmapping, SourceSet dependencyCount: 50 Package: GraphPAC Version: 1.40.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 3cc8adbc18da456971fb3470940f4593 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: RELEASE_3_16 git_last_commit: 8a87b28 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GraphPAC_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GraphPAC_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GraphPAC_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GraphPAC_1.40.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC dependencyCount: 41 Package: GRENITS Version: 1.50.2 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) MD5sum: 047e0d146c4ed25df4abc9b0add92b63 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: RELEASE_3_16 git_last_commit: 5fa4a59 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/GRENITS_1.50.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRENITS_1.50.2.zip mac.binary.ver: bin/macosx/contrib/4.2/GRENITS_1.50.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GRENITS_1.50.2.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 42 Package: GreyListChIP Version: 1.30.0 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: x64 MD5sum: 640cf3caf4da1997b529d1f8c305379f NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Gord Brown Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_16 git_last_commit: 9d65d34 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GreyListChIP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GreyListChIP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GreyListChIP_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GreyListChIP_1.30.0.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R importsMe: DiffBind, epigraHMM dependencyCount: 48 Package: GRmetrics Version: 1.24.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: 485f0c5163a6ad7414e9a4f6d4bc8525 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: RELEASE_3_16 git_last_commit: a6b7756 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GRmetrics_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRmetrics_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GRmetrics_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GRmetrics_1.24.0.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 135 Package: groHMM Version: 1.32.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 6ad4aecd0511dc9a0069c808c58e6537 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: RELEASE_3_16 git_last_commit: 6db18b7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/groHMM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/groHMM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/groHMM_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/groHMM_1.32.0.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 47 Package: GRridge Version: 1.22.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 Archs: x64 MD5sum: 5c7da0baf7039c0cf766a9b0f904c1dd NeedsCompilation: no Title: Better prediction by use of co-data: Adaptive group-regularized ridge regression Description: This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC. biocViews: Classification, Regression, Survival, Bayesian, RNASeq, GenePrediction, GeneExpression, Pathways, GeneSetEnrichment, GO, KEGG, GraphAndNetwork, ImmunoOncology Author: Mark A. van de Wiel , Putri W. Novianti Maintainer: Mark A. van de Wiel git_url: https://git.bioconductor.org/packages/GRridge git_branch: RELEASE_3_16 git_last_commit: 097a26f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GRridge_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GRridge_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GRridge_1.21.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GRridge_1.22.0.tgz vignettes: vignettes/GRridge/inst/doc/GRridge.pdf vignetteTitles: GRridge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRridge/inst/doc/GRridge.R dependencyCount: 24 Package: GSALightning Version: 1.26.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 82d44b6afdbb421deb7fc964a8a9f7c2 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: RELEASE_3_16 git_last_commit: 3a7f2a8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSALightning_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSALightning_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSALightning_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSALightning_1.26.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.32.1 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 20d4111a82f5fd4148a9feffaf5466d9 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: RELEASE_3_16 git_last_commit: 26ad543 git_last_commit_date: 2023-02-17 Date/Publication: 2023-02-19 source.ver: src/contrib/GSAR_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSAR_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/GSAR_1.32.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSAR_1.32.1.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 13 Package: GSCA Version: 2.28.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: b19c1d96f36147bc811bfa32a5cf1e52 NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: RELEASE_3_16 git_last_commit: 468da58 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSCA_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSCA_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSCA_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSCA_2.28.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 73 Package: gscreend Version: 1.12.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: 6cd53dbce19a2b7fa90a8723b0016ed0 NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: RELEASE_3_16 git_last_commit: d4cf44e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gscreend_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gscreend_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gscreend_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gscreend_1.12.0.tgz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 80 Package: GSEABase Version: 1.60.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr License: Artistic-2.0 MD5sum: 3464e4b41ae5e874ac3578b7c275d0c0 NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: RELEASE_3_16 git_last_commit: aae4e52 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSEABase_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEABase_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEABase_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSEABase_1.60.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, Cepo, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata, OSCA.basic importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, cosmosR, EnrichmentBrowser, escape, gep2pep, GISPA, GlobalAncova, GmicR, GSRI, GSVA, MIGSA, miRSM, mogsa, oppar, PanomiR, phenoTest, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, sparrow, TFutils, vissE, zenith, msigdb, SingscoreAMLMutations, clustermole, RVA suggestsMe: BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, BaseSet dependencyCount: 50 Package: GSEABenchmarkeR Version: 1.18.0 Depends: R (>= 3.5.0), Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, GSE62944, knitr, rappdirs, rmarkdown License: Artistic-2.0 MD5sum: 70aa75c38e377ede213765cd3b985a08 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR git_branch: RELEASE_3_16 git_last_commit: 144181f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSEABenchmarkeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEABenchmarkeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEABenchmarkeR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSEABenchmarkeR_1.18.0.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R dependencyCount: 133 Package: GSEAlm Version: 1.58.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 60a03344159b6cea974b408dec1f9e99 NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: RELEASE_3_16 git_last_commit: f73d225 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSEAlm_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEAlm_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEAlm_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSEAlm_1.58.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R dependencyCount: 6 Package: GSEAmining Version: 1.8.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat License: GPL-3 | file LICENSE MD5sum: ee6f82ee0e4ba8a6adf3bd8c2b676c5d NeedsCompilation: no Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs Description: Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. biocViews: GeneSetEnrichment, Clustering, Visualization Author: Oriol Arqués [aut, cre] Maintainer: Oriol Arqués VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: RELEASE_3_16 git_last_commit: c11832b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSEAmining_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSEAmining_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSEAmining_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSEAmining_1.8.0.tgz vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html vignetteTitles: GSEAmining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R dependencyCount: 53 Package: gsean Version: 1.18.0 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, knitr, plotly, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: af8351484ad5740f79512f70044db092 NeedsCompilation: yes Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: RELEASE_3_16 git_last_commit: 9d3d1fd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gsean_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gsean_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gsean_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gsean_1.18.0.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 118 Package: GSgalgoR Version: 1.8.0 Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods, Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 8a92531fef208ec8cca196ecb84545a1 NeedsCompilation: no Title: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer Description: A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high. biocViews: GeneExpression, Transcription, Clustering, Classification, Survival Author: Martin Guerrero [aut], Carlos Catania [cre] Maintainer: Carlos Catania URL: https://github.com/harpomaxx/GSgalgoR VignetteBuilder: knitr BugReports: https://github.com/harpomaxx/GSgalgoR/issues git_url: https://git.bioconductor.org/packages/GSgalgoR git_branch: RELEASE_3_16 git_last_commit: fa26b57 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSgalgoR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSgalgoR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSgalgoR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSgalgoR_1.8.0.tgz vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html, vignettes/GSgalgoR/inst/doc/GSgalgoR.html vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R, vignettes/GSgalgoR/inst/doc/GSgalgoR.R dependencyCount: 22 Package: GSReg Version: 1.32.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 MD5sum: 4a0727064122a022a0d56f314a4bcaa2 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: RELEASE_3_16 git_last_commit: b340b20 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSReg_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSReg_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSReg_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSReg_1.32.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 104 Package: GSRI Version: 2.46.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 Archs: x64 MD5sum: cf0a827380ef6694c68a9baa7d224255 NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: RELEASE_3_16 git_last_commit: d0490c8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSRI_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSRI_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSRI_2.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSRI_2.46.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 65 Package: GSVA Version: 1.46.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, Matrix (>= 1.5-0), parallel, BiocParallel, SingleCellExperiment, sparseMatrixStats, DelayedArray, DelayedMatrixStats, HDF5Array, BiocSingular Suggests: BiocGenerics, RUnit, BiocStyle, knitr, rmarkdown, limma, RColorBrewer, org.Hs.eg.db, genefilter, edgeR, GSVAdata, shiny, shinydashboard, ggplot2, data.table, plotly, future, promises, shinybusy, shinyjs License: GPL (>= 2) MD5sum: 696e724a9f07dd1204581bf1dbd0d26e NeedsCompilation: yes Title: Gene Set Variation Analysis for microarray and RNA-seq data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: FunctionalGenomics, Microarray, RNASeq, Pathways, GeneSetEnrichment Author: Robert Castelo [aut, cre], Justin Guinney [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GSVA VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GSVA/issues git_url: https://git.bioconductor.org/packages/GSVA git_branch: RELEASE_3_16 git_last_commit: 4036d9f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GSVA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GSVA_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GSVA_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GSVA_1.46.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.html vignetteTitles: Gene set variation analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R dependsOnMe: MM2S importsMe: consensusOV, EGSEA, escape, octad, oppar, signifinder, singleCellTK, TBSignatureProfiler, TNBC.CMS, autoGO, clustermole, DRviaSPCN, psSubpathway, scMappR, SIGN, SMDIC suggestsMe: decoupleR, MCbiclust, sparrow dependencyCount: 81 Package: gtrellis Version: 1.30.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, rmarkdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE Archs: x64 MD5sum: a36dfe004d7761b4375c661f75417a42 NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_16 git_last_commit: da93b30 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gtrellis_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gtrellis_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gtrellis_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gtrellis_1.30.0.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA dependencyCount: 25 Package: GUIDEseq Version: 1.28.0 Depends: R (>= 3.5.0), GenomicRanges, BiocGenerics Imports: Biostrings, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), stringr, multtest, GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr, GenomicFeatures, rio, tidyr, tools, methods, purrr, ggplot2, openxlsx, patchwork, rlang Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: a3d2591b32fc8388d12b51d36c20d742 NeedsCompilation: no Title: GUIDE-seq and PEtag-seq analysis pipeline Description: The package implements GUIDE-seq and PEtag-seq analysis workflow including functions for filtering UMI and reads with low coverage, obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: RELEASE_3_16 git_last_commit: e29dcac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GUIDEseq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GUIDEseq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GUIDEseq_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GUIDEseq_1.28.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R importsMe: crisprseekplus dependencyCount: 168 Package: Guitar Version: 2.14.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: 411da76451fe16597a333022f2ad498e NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: RELEASE_3_16 git_last_commit: 3cba68f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Guitar_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Guitar_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Guitar_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Guitar_2.14.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 114 Package: Gviz Version: 1.42.1 Depends: R (>= 4.2), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), ensembldb (>= 2.11.3), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8), graphics, grDevices, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, xml2, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 202e768610e065f9e0768e4242fcc2cb NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst [ctb], Mike Smith [ctb] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: RELEASE_3_16 git_last_commit: 3364b6a git_last_commit_date: 2023-02-17 Date/Publication: 2023-02-17 source.ver: src/contrib/Gviz_1.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Gviz_1.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/Gviz_1.42.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Gviz_1.42.1.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, coMET, cummeRbund, DMRforPairs, Pviz, methylationArrayAnalysis, rnaseqGene, csawBook importsMe: AllelicImbalance, ASpediaFI, ASpli, BindingSiteFinder, CAGEfightR, comapr, crisprViz, DMRcate, ELMER, epimutacions, extraChIPs, GenomicInteractions, maser, mCSEA, MEAL, methylPipe, motifbreakR, OGRE, PING, primirTSS, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb, fishpond, GenomicRanges, gwascat, interactiveDisplay, InterMineR, Pi, pqsfinder, QuasR, RnBeads, segmenter, SplicingGraphs, TFutils, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, chicane, RTIGER dependencyCount: 152 Package: GWAS.BAYES Version: 1.8.0 Depends: R (>= 4.0), Rcpp (>= 1.0.3), RcppEigen (>= 0.3.3.7.0), GA (>= 3.2), caret (>= 6.0-86), ggplot2 (>= 3.3.0), doParallel (>= 1.0.15), memoise (>= 1.1.0), reshape2 (>= 1.4.4), Matrix (>= 1.2-18) LinkingTo: RcppEigen (>= 0.3.3.7.0),Rcpp (>= 1.0.3) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, qqman License: GPL-2 | GPL-3 MD5sum: 7ee51b78011386e07acf41c05ce646a4 NeedsCompilation: yes Title: GWAS for Selfing Species Description: This package is built to perform GWAS analysis for selfing species. The research related to this package was supported in part by National Science Foundation Award 1853549. biocViews: AssayDomain, SNP Author: Jake Williams [aut, cre], Marco Ferreira [aut], Tieming Ji [aut] Maintainer: Jake Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: RELEASE_3_16 git_last_commit: bcda725 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GWAS.BAYES_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GWAS.BAYES_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GWAS.BAYES_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GWAS.BAYES_1.8.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.html vignetteTitles: GWAS.BAYES hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.R dependencyCount: 94 Package: gwascat Version: 2.30.0 Depends: R (>= 3.5.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation, AnnotationHub Suggests: DO.db, DT, knitr, RBGL, testthat, rmarkdown, dplyr, Gviz, Rsamtools, rtracklayer, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 Archs: x64 MD5sum: 6d61c527d10782f43d4e6f988bda593e NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: RELEASE_3_16 git_last_commit: 16acbd9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gwascat_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gwascat_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gwascat_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gwascat_2.30.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.html, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat: structuring and querying the NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet, liftOver importsMe: circRNAprofiler suggestsMe: GenomicScores, hmdbQuery, ldblock, parglms, TFutils, grasp2db dependencyCount: 135 Package: GWASTools Version: 1.44.0 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth, data.table Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation, parallel License: Artistic-2.0 MD5sum: d631af418a3325e06129f4851da3ca9a NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson, David Levine Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools git_url: https://git.bioconductor.org/packages/GWASTools git_branch: RELEASE_3_16 git_last_commit: 6788af5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GWASTools_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GWASTools_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GWASTools_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GWASTools_1.44.0.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R dependsOnMe: mBPCR, GWASdata importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 65 Package: gwasurvivr Version: 1.16.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 048a4ddf00bd11725a335df2cdbcf173 NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: RELEASE_3_16 git_last_commit: 727e03f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/gwasurvivr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/gwasurvivr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/gwasurvivr_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/gwasurvivr_1.16.0.tgz vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 124 Package: GWENA Version: 1.8.0 Depends: R (>= 4.1) Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1), ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5), tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph (>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist (>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>= 1.14.1), stringr (>= 1.4.0), cluster (>= 2.1.0), grDevices (>= 4.0.4), methods, graphics, stats, utils Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16), prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1), BiocStyle (>= 2.15.8) License: GPL-3 MD5sum: 8ab9a7f3b0a079c97b8061c02af15216 NeedsCompilation: no Title: Pipeline for augmented co-expression analysis Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. biocViews: Software, GeneExpression, Network, Clustering, GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization, RNASeq, Transcriptomics, mRNAMicroarray, Microarray, NetworkEnrichment, Sequencing, GO Author: Gwenaëlle Lemoine [aut, cre] (), Marie-Pier Scott-Boyer [ths], Arnaud Droit [fnd] Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: RELEASE_3_16 git_last_commit: 04f430c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/GWENA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/GWENA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/GWENA_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/GWENA_1.8.0.tgz vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html vignetteTitles: GWENA-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R dependencyCount: 140 Package: h5vc Version: 2.32.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 2.13.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.99.1) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics, rmarkdown License: GPL (>= 3) MD5sum: 10d015ecdf344453eaafbe0326488ae9 NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: RELEASE_3_16 git_last_commit: c234c80 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/h5vc_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/h5vc_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/h5vc_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/h5vc_2.32.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R suggestsMe: h5vcData dependencyCount: 89 Package: hapFabia Version: 1.40.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: x64 MD5sum: fc124056a638d8a5b30e968685db730b NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: RELEASE_3_16 git_last_commit: 4ccfc31 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hapFabia_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hapFabia_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hapFabia_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hapFabia_1.40.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 8 Package: Harman Version: 1.26.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, Ckmeans.1d.dp, parallel, methods, matrixStats LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE Archs: x64 MD5sum: 3ec4d88d55b80175e86c3c12773853ce NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Yalchin Oytam [aut], Josh Bowden [aut], Jason Ross [aut, cre] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_16 git_last_commit: 1acc258 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Harman_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Harman_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Harman_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Harman_1.26.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser suggestsMe: HarmanData dependencyCount: 11 Package: Harshlight Version: 1.70.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: x64 MD5sum: 4fe7814f1f94baa36e407afa1fdfe559 NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ git_url: https://git.bioconductor.org/packages/Harshlight git_branch: RELEASE_3_16 git_last_commit: 2758cf8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Harshlight_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Harshlight_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Harshlight_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Harshlight_1.70.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R dependencyCount: 27 Package: hca Version: 1.6.0 Depends: R (>= 4.1) Imports: httr, jsonlite, dplyr, tibble, tidyr, readr, BiocFileCache, tools, utils, digest, shiny, miniUI, DT Suggests: LoomExperiment, SummarizedExperiment, SingleCellExperiment, S4Vectors, methods, testthat (>= 3.0.0), knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 1e06854ef70c75472d67f9002a5731e7 NeedsCompilation: no Title: Exploring the Human Cell Atlas Data Coordinating Platform Description: This package provides users with the ability to query the Human Cell Atlas data repository for single-cell experiment data. The `projects()`, `files()`, `samples()` and `bundles()` functions retrieve summary information on each of these indexes; corresponding `*_details()` are available for individual entries of each index. File-based resources can be downloaded using `files_download()`. Advanced use of the package allows the user to page through large result sets, and to flexibly query the 'list-of-lists' structure representing query responses. biocViews: Software, SingleCell Author: Maya McDaniel [aut], Martin Morgan [aut, cre] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hca git_branch: RELEASE_3_16 git_last_commit: 435e261 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hca_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hca_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hca_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hca_1.6.0.tgz vignettes: vignettes/hca/inst/doc/hca_manifest_vignette.html, vignettes/hca/inst/doc/hca_vignette.html vignetteTitles: Working With Human Cell Atlas Manifests, Accessing Human Cell Atlas Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hca/inst/doc/hca_manifest_vignette.R, vignettes/hca/inst/doc/hca_vignette.R dependencyCount: 84 Package: HDF5Array Version: 1.26.0 Depends: R (>= 3.4), methods, DelayedArray (>= 0.15.16), rhdf5 (>= 2.31.6) Imports: utils, stats, tools, Matrix, rhdf5filters, BiocGenerics (>= 0.31.5), S4Vectors, IRanges LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, zellkonverter, GenomicFeatures, RUnit, SingleCellExperiment License: Artistic-2.0 MD5sum: fee08903ae4e10af009d8efce66f8df9 NeedsCompilation: yes Title: HDF5 backend for DelayedArray objects Description: Implement the HDF5Array, H5SparseMatrix, H5ADMatrix, and TENxMatrix classes, 4 convenient and memory-efficient array-like containers for representing and manipulating either: (1) a conventional (a.k.a. dense) HDF5 dataset, (2) an HDF5 sparse matrix (stored in CSR/CSC/Yale format), (3) the central matrix of an h5ad file (or any matrix in the /layers group), or (4) a 10x Genomics sparse matrix. All these containers are DelayedArray extensions and thus support all operations (delayed or block-processed) supported by DelayedArray objects. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array SystemRequirements: GNU make BugReports: https://github.com/Bioconductor/HDF5Array/issues git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: RELEASE_3_16 git_last_commit: 38b7bd6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HDF5Array_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HDF5Array_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HDF5Array_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HDF5Array_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: compartmap, MAGAR, restfulSEData, TENxBrainData, TENxPBMCData importsMe: biscuiteer, bsseq, Cepo, clusterExperiment, cytomapper, DelayedTensor, DropletUtils, FRASER, GenomicScores, glmGamPoi, GSVA, LoomExperiment, methrix, minfi, MOFA2, netSmooth, NxtIRFcore, recountmethylation, scmeth, scry, signatureSearch, SpliceWiz, transformGamPoi, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, curatedTCGAData, HCAData, imcdatasets, MethylSeqData, SingleCellMultiModal, TabulaMurisSenisData, ebvcube suggestsMe: beachmat, BiocSklearn, cellxgenedp, DelayedArray, DelayedMatrixStats, iSEE, MAST, mbkmeans, metabolomicsWorkbenchR, MuData, MultiAssayExperiment, PDATK, QFeatures, SCArray, scMerge, scran, spatialHeatmap, SummarizedExperiment, TENxIO, zellkonverter, digitalDLSorteR dependencyCount: 19 Package: HDTD Version: 1.32.2 Depends: R (>= 4.1) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: ed48521770117aadd3331c010256b113 NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut] (), John C. Marioni [aut] (), Simon Tavar\'{e} [aut] () Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: RELEASE_3_16 git_last_commit: 38c9851 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/HDTD_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/HDTD_1.32.2.zip mac.binary.ver: bin/macosx/contrib/4.2/HDTD_1.32.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HDTD_1.32.2.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: heatmaps Version: 1.22.0 Depends: R (>= 3.5.0) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, GenomeInfoDb Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: 680cf9ae34857c5410121394ec01e3be NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: RELEASE_3_16 git_last_commit: b83e12b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/heatmaps_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/heatmaps_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/heatmaps_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/heatmaps_1.22.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 64 Package: Heatplus Version: 3.6.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 01c603979a9227a2ce481bb7e4e81296 NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: RELEASE_3_16 git_last_commit: 5f059d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Heatplus_3.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Heatplus_3.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Heatplus_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Heatplus_3.6.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: phenoTest, tRanslatome, heatmapFlex suggestsMe: mtbls2, RforProteomics dependencyCount: 4 Package: HelloRanges Version: 1.24.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment, BiocIO Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: cffb7f1b83857131e5d804be315850c2 NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: RELEASE_3_16 git_last_commit: b43be74 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HelloRanges_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HelloRanges_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HelloRanges_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HelloRanges_1.24.0.tgz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 99 Package: HELP Version: 1.56.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: dd76775bf66ded83bc31d9fc481bd9c7 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: RELEASE_3_16 git_last_commit: 0077e3e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HELP_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HELP_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HELP_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HELP_1.56.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 7 Package: HEM Version: 1.70.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) MD5sum: abcb0d74441d558cb52b2f91b6ef0efa NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: RELEASE_3_16 git_last_commit: 54c3119 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HEM_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HEM_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HEM_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HEM_1.70.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: hermes Version: 1.2.0 Depends: ggfortify, R (>= 4.1), SummarizedExperiment (>= 1.16) Imports: assertthat, biomaRt, Biobase, BiocGenerics, checkmate (>= 2.1), circlize, ComplexHeatmap, DESeq2, dplyr, edgeR, EnvStats, forcats, GenomicRanges, ggplot2, ggrepel (>= 0.9), IRanges, lifecycle, limma, magrittr, matrixStats, methods, MultiAssayExperiment, purrr, R6, Rdpack, rlang, stats, S4Vectors, tidyr, utils Suggests: BiocStyle, DelayedArray, DT, grid, httr, knitr, rmarkdown, statmod, testthat (>= 2.0), vdiffr, withr License: Apache License 2.0 | file LICENSE Archs: x64 MD5sum: 0372b41dcaaeab8e37c2b3bd22c4b42a NeedsCompilation: no Title: Preprocessing, analyzing, and reporting of RNA-seq data Description: Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed RNA-seq data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from BioMart. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including `cpm`, `rpkm` and `tpm` can be used, and `DESeq2` as well as `voom` differential expression analyses are available. biocViews: RNASeq, DifferentialExpression, Normalization, Preprocessing, QualityControl Author: Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff Luong [aut], Lyndsee Midori Zhang [aut], Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N. Paulson [aut] Maintainer: Daniel Sabanés Bové URL: https://github.com/insightsengineering/hermes/ VignetteBuilder: knitr BugReports: https://github.com/insightsengineering/hermes/issues git_url: https://git.bioconductor.org/packages/hermes git_branch: RELEASE_3_16 git_last_commit: e704cda git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hermes_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hermes_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hermes_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hermes_1.2.0.tgz vignettes: vignettes/hermes/inst/doc/introduction.html vignetteTitles: Introduction to `hermes` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hermes/inst/doc/introduction.R dependencyCount: 134 Package: Herper Version: 1.8.1 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, seqCNA License: GPL-3 MD5sum: df1308a4d08c4116fbafe996c0e77a63 NeedsCompilation: no Title: The Herper package is a simple toolset to install and manage conda packages and environments from R Description: Many tools for data analysis are not available in R, but are present in public repositories like conda. The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages. For people developing packages with python conda dependencies we recommend using basilisk (https://bioconductor.org/packages/release/bioc/html/basilisk.html) to internally support these system requirments pre-hoc. biocViews: Infrastructure, Software Author: Matt Paul [aut] (), Thomas Carroll [aut, cre] (), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: RELEASE_3_16 git_last_commit: 9eb0291 git_last_commit_date: 2022-11-07 Date/Publication: 2022-11-10 source.ver: src/contrib/Herper_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Herper_1.7.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Herper_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Herper_1.8.1.tgz vignettes: vignettes/Herper/inst/doc/QuickStart.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Herper/inst/doc/QuickStart.R dependencyCount: 18 Package: HGC Version: 1.6.0 Depends: R (>= 4.1.0) Imports: Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 2060f0b2d7abd9839cea6014ee8b81d1 NeedsCompilation: yes Title: A fast hierarchical graph-based clustering method Description: HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. biocViews: SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Author: Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer: XGlab SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HGC git_branch: RELEASE_3_16 git_last_commit: 920e76a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HGC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HGC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HGC_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HGC_1.6.0.tgz vignettes: vignettes/HGC/inst/doc/HGC.html vignetteTitles: HGC package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HGC/inst/doc/HGC.R dependencyCount: 50 Package: hiAnnotator Version: 1.32.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics, markdown License: GPL (>= 2) Archs: x64 MD5sum: c3fbc36754fb405d62a9371fb6380777 NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: RELEASE_3_16 git_last_commit: 146822c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hiAnnotator_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hiAnnotator_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hiAnnotator_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hiAnnotator_1.32.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/Intro.html vignetteTitles: Using hiAnnotator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R dependsOnMe: hiReadsProcessor dependencyCount: 79 Package: HIBAG Version: 1.34.1 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel (>= 5.0.0) Suggests: parallel, ggplot2, reshape2, gdsfmt, SNPRelate, SeqArray, knitr, markdown, rmarkdown License: GPL-3 MD5sum: 92c0fbdfb7aeaf551f2c63baea6cd21d NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (), Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/HIBAG, https://hibag.s3.amazonaws.com/index.html SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: RELEASE_3_16 git_last_commit: 0d04d4e git_last_commit_date: 2022-12-16 Date/Publication: 2022-12-18 source.ver: src/contrib/HIBAG_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/HIBAG_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.2/HIBAG_1.34.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HIBAG_1.34.1.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html, vignettes/HIBAG/inst/doc/HLA_Association.html, vignettes/HIBAG/inst/doc/Implementation.html vignetteTitles: HIBAG vignette html, HLA association vignette html, HIBAG algorithm implementation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R, vignettes/HIBAG/inst/doc/HLA_Association.R, vignettes/HIBAG/inst/doc/Implementation.R dependencyCount: 2 Package: HiCBricks Version: 1.16.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, GenomeInfoDb, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 41d76d19ccfdd562e6836af0287ef3fb NeedsCompilation: no Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: RELEASE_3_16 git_last_commit: 79aabbb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiCBricks_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCBricks_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCBricks_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiCBricks_1.16.0.tgz vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html vignetteTitles: IntroductionToHiCBricks.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R dependencyCount: 85 Package: HiCcompare Version: 1.20.0 Depends: R (>= 3.5.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: debebb27c908fb236b6da324f9ba4140 NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield , Kellen Cresswell , Mikhail Dozmorov Maintainer: John Stansfield , Mikhail Dozmorov URL: https://github.com/dozmorovlab/HiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/HiCcompare/issues git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: RELEASE_3_16 git_last_commit: 508f805 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiCcompare_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCcompare_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCcompare_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiCcompare_1.20.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, SpectralTAD, TADCompare dependencyCount: 96 Package: HiCDCPlus Version: 1.6.0 Imports: Rcpp,InteractionSet,GenomicInteractions,bbmle,pscl,BSgenome,data.table,dplyr,tidyr,GenomeInfoDb,rlang,splines,MASS,GenomicRanges,IRanges,tibble,R.utils,Biostrings,rtracklayer,methods,S4Vectors LinkingTo: Rcpp Suggests: BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, RUnit, BiocGenerics, knitr, rmarkdown, HiTC, DESeq2, Matrix, BiocFileCache, rappdirs Enhances: parallel License: GPL-3 MD5sum: 017a77de2affcfbf7a4aa16547c95aca NeedsCompilation: yes Title: Hi-C Direct Caller Plus Description: Systematic 3D interaction calls and differential analysis for Hi-C and HiChIP. The HiC-DC+ (Hi-C/HiChIP direct caller plus) package enables principled statistical analysis of Hi-C and HiChIP data sets – including calling significant interactions within a single experiment and performing differential analysis between conditions given replicate experiments – to facilitate global integrative studies. HiC-DC+ estimates significant interactions in a Hi-C or HiChIP experiment directly from the raw contact matrix for each chromosome up to a specified genomic distance, binned by uniform genomic intervals or restriction enzyme fragments, by training a background model to account for random polymer ligation and systematic sources of read count variation. biocViews: HiC, DNA3DStructure, Software, Normalization Author: Merve Sahin [cre, aut] () Maintainer: Merve Sahin SystemRequirements: JRE 8+ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCDCPlus git_branch: RELEASE_3_16 git_last_commit: 65cf744 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiCDCPlus_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCDCPlus_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCDCPlus_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiCDCPlus_1.6.0.tgz vignettes: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.html vignetteTitles: Analyzing Hi-C and HiChIP data with HiCDCPlus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDCPlus/inst/doc/HiCDCPlus.R dependencyCount: 165 Package: HiCDOC Version: 1.0.0 Depends: InteractionSet, GenomicRanges, SummarizedExperiment, R (>= 4.1.0) Imports: methods, zlibbioc, ggplot2, Rcpp (>= 0.12.8), stats, S4Vectors, gtools, pbapply, BiocParallel, BiocGenerics, rhdf5, grid, ggpubr, gridExtra, ggExtra, data.table, multiHiCcompare, GenomeInfoDb LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, BiocManager License: LGPL (>= 3) Archs: x64 MD5sum: c84a415d83a6add2fb78aaa0bb4810d5 NeedsCompilation: yes Title: A/B compartment detection and differential analysis Description: HiCDOC normalizes intrachromosomal Hi-C matrices, uses unsupervised learning to predict A/B compartments from multiple replicates, and detects significant compartment changes between experiment conditions. It provides a collection of functions assembled into a pipeline to filter and normalize the data, predict the compartments and visualize the results. It accepts several type of data: tabular `.tsv` files, Cooler `.cool` or `.mcool` files, Juicer `.hic` files or HiC-Pro `.matrix` and `.bed` files. biocViews: HiC, DNA3DStructure, Normalization, Sequencing, Software, Clustering Author: Kurylo Cyril [aut], Zytnicki Matthias [aut], Foissac Sylvain [aut], Maigné Élise [aut, cre] Maintainer: Maigné Élise URL: https://github.com/mzytnicki/HiCDOC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/mzytnicki/HiCDOC/issues git_url: https://git.bioconductor.org/packages/HiCDOC git_branch: RELEASE_3_16 git_last_commit: 8b0d83d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiCDOC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiCDOC_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiCDOC_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiCDOC_1.0.0.tgz vignettes: vignettes/HiCDOC/inst/doc/HiCDOC.html vignetteTitles: HiCDOC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiCDOC/inst/doc/HiCDOC.R dependencyCount: 178 Package: HiContacts Version: 1.0.0 Depends: R (>= 4.2) Imports: HiContactsData, InteractionSet, GenomicInteractions, GenomicRanges, IRanges, GenomeInfoDb, S4Vectors, BiocGenerics, methods, rhdf5, Matrix, vroom, tibble, tidyr, dplyr, glue, stringr, reticulate, ggplot2, ggrastr, scales Suggests: cowplot, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: a3069f9a5529217a4955ab7626fa4d27 NeedsCompilation: no Title: HiContacts: R interface to cool files Description: HiContacts: R interface to (m)cool files and other Hi-C processed file formats. HiContacts provides a collection of tools to analyse and visualize Hi-C datasets. It can import data from pairs or (m)cool files. biocViews: HiC, DNA3DStructure, DataImport Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/HiContacts VignetteBuilder: knitr BugReports: https://github.com/js2264/HiContacts/issues git_url: https://git.bioconductor.org/packages/HiContacts git_branch: RELEASE_3_16 git_last_commit: ad3c78e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiContacts_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiContacts_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiContacts_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiContacts_1.0.0.tgz vignettes: vignettes/HiContacts/inst/doc/HiContacts.html vignetteTitles: Introduction to HiContacts hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiContacts/inst/doc/HiContacts.R dependencyCount: 188 Package: hierGWAS Version: 1.28.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 321facf0c58650dc44cd8f8fc90ee0e6 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: RELEASE_3_16 git_last_commit: fd9e7d3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hierGWAS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hierGWAS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hierGWAS_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hierGWAS_1.28.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 19 Package: hierinf Version: 1.16.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: 17a4777e29c751cd0a1a20f80e42ba2e NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: RELEASE_3_16 git_last_commit: fc6a528 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hierinf_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hierinf_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hierinf_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hierinf_1.16.0.tgz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 19 Package: HilbertCurve Version: 1.28.0 Depends: R (>= 3.6.0), grid Imports: methods, utils, HilbertVis, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong, rmarkdown License: MIT + file LICENSE MD5sum: 59b6c5df11b45ff6735def4a27510c6f NeedsCompilation: no Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_16 git_last_commit: 7708201 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HilbertCurve_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HilbertCurve_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HilbertCurve_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HilbertCurve_1.28.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: Making 2D Hilbert Curve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R suggestsMe: InteractiveComplexHeatmap dependencyCount: 27 Package: HilbertVis Version: 1.56.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) MD5sum: 0ed88549e8392ed30c43e61ec54570c6 NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: RELEASE_3_16 git_last_commit: f3bdedf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HilbertVis_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HilbertVis_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HilbertVis_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HilbertVis_1.56.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR, HilbertCurve dependencyCount: 6 Package: HilbertVisGUI Version: 1.56.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: bf19ae216b55d12ba9fed073a2ffe6fa NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: RELEASE_3_16 git_last_commit: 68554bb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HilbertVisGUI_1.56.0.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.12.0 Depends: R(>= 4.1), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 01ba11900b8d1f792b7176ce88b9f8aa NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.0.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: RELEASE_3_16 git_last_commit: be845b5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiLDA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiLDA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiLDA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiLDA_1.12.0.tgz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R importsMe: selectKSigs dependencyCount: 122 Package: hipathia Version: 2.14.0 Depends: R (>= 3.6), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 Archs: x64 MD5sum: 50201a1eec92a1a35ccb8d95ac8b0785 NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: RELEASE_3_16 git_last_commit: 491ab66 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hipathia_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hipathia_2.13.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hipathia_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hipathia_2.14.0.tgz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R dependencyCount: 122 Package: HIPPO Version: 1.10.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 440881b568ba30fe6703bf4d3cad51d1 NeedsCompilation: no Title: Heterogeneity-Induced Pre-Processing tOol Description: For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering. biocViews: Sequencing, SingleCell, GeneExpression, DifferentialExpression, Clustering Author: Tae Kim [aut, cre], Mengjie Chen [aut] Maintainer: Tae Kim URL: https://github.com/tk382/HIPPO VignetteBuilder: knitr BugReports: https://github.com/tk382/HIPPO/issues git_url: https://git.bioconductor.org/packages/HIPPO git_branch: RELEASE_3_16 git_last_commit: a7c28ba git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HIPPO_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HIPPO_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HIPPO_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HIPPO_1.10.0.tgz vignettes: vignettes/HIPPO/inst/doc/example.html vignetteTitles: Example analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIPPO/inst/doc/example.R dependencyCount: 78 Package: hiReadsProcessor Version: 1.34.0 Depends: R (>= 3.5.0), Biostrings, GenomicAlignments, BiocParallel, hiAnnotator Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat, markdown License: GPL-3 MD5sum: c45844b579da958016faa0e663008e83 NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiReadsProcessor git_branch: RELEASE_3_16 git_last_commit: 07376e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hiReadsProcessor_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/hiReadsProcessor_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hiReadsProcessor_1.34.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html vignetteTitles: Using hiReadsProcessor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R dependencyCount: 87 Package: HIREewas Version: 1.16.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) MD5sum: 6a4452795d33a86c8b9ad06eefa4fe33 NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: RELEASE_3_16 git_last_commit: 5c88390 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HIREewas_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HIREewas_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HIREewas_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HIREewas_1.16.0.tgz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 10 Package: HiTC Version: 1.42.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 Archs: x64 MD5sum: f86018caf28a4ec792fc6caa3b45c224 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: RELEASE_3_16 git_last_commit: 2ee10bd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HiTC_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HiTC_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HiTC_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HiTC_1.42.0.tgz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R suggestsMe: HiCDCPlus, HiCDataHumanIMR90, adjclust dependencyCount: 47 Package: hmdbQuery Version: 1.18.0 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat, rmarkdown License: Artistic-2.0 MD5sum: dcaea8a5904329def19b35ea940b0cde NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: RELEASE_3_16 git_last_commit: e88a377 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hmdbQuery_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hmdbQuery_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hmdbQuery_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hmdbQuery_1.18.0.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R dependencyCount: 8 Package: HMMcopy Version: 1.40.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 MD5sum: fd62e5a9b3780e0035d32338f47945be NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_16 git_last_commit: 47a99fe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HMMcopy_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HMMcopy_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HMMcopy_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HMMcopy_1.40.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: hopach Version: 2.58.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: d2c7ce02853621fd1bc705c4e0bdd4b6 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: RELEASE_3_16 git_last_commit: ac9c39d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hopach_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hopach_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hopach_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hopach_2.58.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify, treekoR suggestsMe: MicrobiotaProcess, seqArchR dependencyCount: 8 Package: HPAanalyze Version: 1.16.0 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, markdown, devtools, BiocStyle License: GPL-3 + file LICENSE Archs: x64 MD5sum: c7b7037d0905c6785ba4bf7afd0d6c5e NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran URL: https://github.com/anhtr/HPAanalyze VignetteBuilder: knitr BugReports: https://github.com/anhtr/HPAanalyze/issues git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: RELEASE_3_16 git_last_commit: 53c7767 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HPAanalyze_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HPAanalyze_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HPAanalyze_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HPAanalyze_1.16.0.tgz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. Quick-start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function with HPAanalyze", "2. In-depth: Working with Human Protein Atlas (HPA) data in R with HPAanalyze", "3. Tutorial: Combine HPAanalyze with your Human Protein Atlas (HPA) queries", "4. Tutorial: Working with Human Protein Atlas (HPA) xml files offline", "5. Tutorial: Export Human Protein Atlas (HPA) data as JSON", "6. Tutorial: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 45 Package: hpar Version: 1.40.0 Depends: R (>= 3.5.0) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 647bbfaf65eb2c67f1d185ef542f716c NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto [cre, aut] (), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: RELEASE_3_16 git_last_commit: 8dc8379 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hpar_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hpar_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hpar_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hpar_1.40.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R importsMe: MetaboSignal suggestsMe: HPAStainR, pRoloc, RforProteomics dependencyCount: 1 Package: HPAStainR Version: 1.8.0 Depends: R (>= 4.1.0), dplyr, tidyr Imports: utils, stats, scales, stringr, tibble, shiny, data.table Suggests: knitr, BiocManager, qpdf, hpar, testthat, rmarkdown License: Artistic-2.0 MD5sum: 85c3f50819fed8bce11ac343a6047601 NeedsCompilation: no Title: Queries the Human Protein Atlas Staining Data for Multiple Proteins and Genes Description: This package is built around the HPAStainR function. The purpose of the HPAStainR function is to query the visual staining data in the Human Protein Atlas to return a table of staining ranked cell types. The function also has multiple arguments to personalize to output as well to include cancer data, csv readable names, modify the confidence levels of the results and more. The other functions exist exclusively to easily acquire the data required to run HPAStainR. biocViews: GeneExpression, GeneSetEnrichment Author: Tim O. Nieuwenhuis [aut, cre] () Maintainer: Tim O. Nieuwenhuis SystemRequirements: 4GB of RAM VignetteBuilder: knitr BugReports: https://github.com/tnieuwe/HPAstainR git_url: https://git.bioconductor.org/packages/HPAStainR git_branch: RELEASE_3_16 git_last_commit: 0ede632 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HPAStainR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HPAStainR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HPAStainR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HPAStainR_1.8.0.tgz vignettes: vignettes/HPAStainR/inst/doc/HPAStainR.html vignetteTitles: HPAStainR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HPAStainR/inst/doc/HPAStainR.R dependencyCount: 59 Package: HPiP Version: 1.4.3 Depends: R (>= 4.1) Imports: dplyr (>= 1.0.6), httr (>= 1.4.2), readr, tidyr, tibble, utils, stringr, magrittr, caret, corrplot, ggplot2, pROC, PRROC, igraph, graphics, stats, purrr, grDevices, protr, MCL Suggests: rmarkdown, colorspace, e1071, kernlab, ranger, SummarizedExperiment, Biostrings, randomForest, gprofiler2, gridExtra, ggthemes, BiocStyle, BiocGenerics, RUnit, tools, knitr License: MIT + file LICENSE MD5sum: cb99c65d64a5ae07de9c9f5e8b60d082 NeedsCompilation: no Title: Host-Pathogen Interaction Prediction Description: HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations. biocViews: Proteomics, SystemsBiology, NetworkInference, StructuralPrediction, GenePrediction, Network Author: Matineh Rahmatbakhsh [aut, trl, cre], Mohan Babu [led] Maintainer: Matineh Rahmatbakhsh URL: https://github.com/mrbakhsh/HPiP VignetteBuilder: knitr BugReports: https://github.com/mrbakhsh/HPiP/issues git_url: https://git.bioconductor.org/packages/HPiP git_branch: RELEASE_3_16 git_last_commit: 3ed3d60 git_last_commit_date: 2023-04-04 Date/Publication: 2023-04-05 source.ver: src/contrib/HPiP_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/HPiP_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.2/HPiP_1.4.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HPiP_1.4.3.tgz vignettes: vignettes/HPiP/inst/doc/HPiP_tutorial.html vignetteTitles: Introduction to HPiP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPiP/inst/doc/HPiP_tutorial.R dependencyCount: 108 Package: HTqPCR Version: 1.52.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 429523d4a681c7f29347d1c4680f201c NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: RELEASE_3_16 git_last_commit: 4349f7d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HTqPCR_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HTqPCR_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HTqPCR_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HTqPCR_1.52.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR dependencyCount: 20 Package: HTSeqGenie Version: 4.28.1 Depends: R (>= 3.5.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment, methods Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db License: Artistic-2.0 MD5sum: e6363af0033ee063e1abb63a481c0082 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder git_url: https://git.bioconductor.org/packages/HTSeqGenie git_branch: RELEASE_3_16 git_last_commit: fb9d357 git_last_commit_date: 2023-01-20 Date/Publication: 2023-01-20 source.ver: src/contrib/HTSeqGenie_4.28.1.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R dependencyCount: 112 Package: HTSFilter Version: 1.38.0 Depends: R (>= 4.0.0) Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: b2c78f385bd578d77004db56b2cee715 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau [cre, aut] (), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: RELEASE_3_16 git_last_commit: fdc6147 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HTSFilter_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HTSFilter_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HTSFilter_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HTSFilter_1.38.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html vignetteTitles: HTSFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq suggestsMe: HTSCluster dependencyCount: 93 Package: HubPub Version: 1.6.0 Imports: available, usethis, biocthis, dplyr, aws.s3, fs, BiocManager, utils Suggests: AnnotationHubData, ExperimentHubData, testthat, knitr, rmarkdown, BiocStyle, License: Artistic-2.0 MD5sum: f996aed5ee39abacca57b821fe1e49dd NeedsCompilation: no Title: Utilities to create and use Bioconductor Hubs Description: HubPub provides users with functionality to help with the Bioconductor Hub structures. The package provides the ability to create a skeleton of a Hub style package that the user can then populate with the necessary information. There are also functions to help add resources to the Hub package metadata files as well as publish data to the Bioconductor S3 bucket. biocViews: DataImport, Infrastructure, Software, ThirdPartyClient Author: Kayla Interdonato [aut, cre], Martin Morgan [aut] Maintainer: Kayla Interdonato VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HubPub/issues git_url: https://git.bioconductor.org/packages/HubPub git_branch: RELEASE_3_16 git_last_commit: c40347e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HubPub_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HubPub_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HubPub_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HubPub_1.6.0.tgz vignettes: vignettes/HubPub/inst/doc/CreateAHubPackage.html, vignettes/HubPub/inst/doc/HubPub.html vignetteTitles: Creating A Hub Package: ExperimentHub or AnnotationHub, HubPub: Help with publication of Hub packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HubPub/inst/doc/CreateAHubPackage.R, vignettes/HubPub/inst/doc/HubPub.R suggestsMe: AnnotationHub, AnnotationHubData, ExperimentHub, ExperimentHubData dependencyCount: 77 Package: HumanTranscriptomeCompendium Version: 1.14.0 Depends: R (>= 3.6) Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport, dplyr, magrittr, BiocFileCache, testthat, rhdf5client, rmarkdown License: Artistic-2.0 MD5sum: 4a8495aa25207670ee958c978758ba66 NeedsCompilation: no Title: Tools to work with a Compendium of 181000 human transcriptome sequencing studies Description: Provide tools for working with a compendium of human transcriptome sequences (originally htxcomp). biocViews: Transcriptomics, Infrastructure Author: Sean Davis, Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HumanTranscriptomeCompendium git_branch: RELEASE_3_16 git_last_commit: 4b78250 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HumanTranscriptomeCompendium_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HumanTranscriptomeCompendium_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HumanTranscriptomeCompendium_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HumanTranscriptomeCompendium_1.14.0.tgz vignettes: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.html vignetteTitles: HumanTranscriptomeCompendium -- a cloud-resident collection of sequenced human transcriptomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.R dependencyCount: 71 Package: hummingbird Version: 1.8.0 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 4b1b40926e9cff88e4850cd0c1975e4f NeedsCompilation: yes Title: Bayesian Hidden Markov Model for the detection of differentially methylated regions Description: A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups. biocViews: HiddenMarkovModel, Bayesian, DNAMethylation, BiomedicalInformatics, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut] Maintainer: Eleni Adam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: RELEASE_3_16 git_last_commit: 5731efc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hummingbird_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hummingbird_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hummingbird_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hummingbird_1.8.0.tgz vignettes: vignettes/hummingbird/inst/doc/hummingbird.html vignetteTitles: hummingbird hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R dependencyCount: 26 Package: HybridMTest Version: 1.42.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later Archs: x64 MD5sum: 6fec564c3b2aace09ab2d284b0717ff6 NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: RELEASE_3_16 git_last_commit: be026ec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/HybridMTest_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/HybridMTest_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/HybridMTest_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/HybridMTest_1.42.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R importsMe: APAlyzer dependencyCount: 14 Package: hypeR Version: 1.14.0 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr, scales, rlang, httr, openxlsx, htmltools, reshape2, reactable, msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE MD5sum: 4121a78b4bf75afdd01b761be0f2be1a NeedsCompilation: no Title: An R Package For Geneset Enrichment Workflows Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut, cre], Stefano Monti [aut] Maintainer: Anthony Federico URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues git_url: https://git.bioconductor.org/packages/hypeR git_branch: RELEASE_3_16 git_last_commit: 8787f21 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hypeR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hypeR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hypeR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hypeR_1.14.0.tgz vignettes: vignettes/hypeR/inst/doc/hypeR.html vignetteTitles: hypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hypeR/inst/doc/hypeR.R dependencyCount: 105 Package: hyperdraw Version: 1.50.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 086dc68fc216e4df6df9d02bf0a671f5 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: RELEASE_3_16 git_last_commit: ab3b966 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hyperdraw_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hyperdraw_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hyperdraw_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hyperdraw_1.50.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR dependencyCount: 11 Package: hypergraph Version: 1.70.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: fc5ed8a8b82b888e5095eaadeeb4248a NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: RELEASE_3_16 git_last_commit: a5ffeaf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/hypergraph_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/hypergraph_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/hypergraph_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/hypergraph_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs importsMe: BiGGR, hyperdraw dependencyCount: 7 Package: iASeq Version: 1.42.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 70c4ed4cbd87b48866ba38d354db6ef8 NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: RELEASE_3_16 git_last_commit: d601b24 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iASeq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iASeq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iASeq_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iASeq_1.42.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.16.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 4e7b70c5bb5336bb67be2e6f67d3c9c3 NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: RELEASE_3_16 git_last_commit: 5898dd3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iasva_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iasva_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iasva_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iasva_1.16.0.tgz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 37 Package: iBBiG Version: 1.42.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 MD5sum: 07d01bd3815117cde546ce8ef86a5bec NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: RELEASE_3_16 git_last_commit: 027350a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iBBiG_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iBBiG_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iBBiG_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iBBiG_1.42.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R importsMe: miRSM dependencyCount: 57 Package: ibh Version: 1.46.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) Archs: x64 MD5sum: 0c3b54690c0d0286df8ccc842faea7ce NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: RELEASE_3_16 git_last_commit: e65ea17 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ibh_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ibh_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ibh_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ibh_1.46.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.38.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 MD5sum: a056821653db5459109fc3e172fb5ac9 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: RELEASE_3_16 git_last_commit: 7d09076 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iBMQ_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iBMQ_1.38.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 37 Package: iCARE Version: 1.26.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: x64 MD5sum: f3a3bfed9bde5598dc4d7a00d6789675 NeedsCompilation: yes Title: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to compute Individualized Coherent Absolute Risk Estimators. biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Paige Maas, Parichoy Pal Choudhury, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/iCARE git_branch: RELEASE_3_16 git_last_commit: d081560 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iCARE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCARE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCARE_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iCARE_1.26.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 76 Package: Icens Version: 1.70.0 Depends: survival Imports: graphics License: Artistic-2.0 Archs: x64 MD5sum: 0cf1c275f889bcc3bc8d37e552144513 NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: RELEASE_3_16 git_last_commit: afbcb50 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Icens_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Icens_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Icens_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Icens_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, interval importsMe: PROcess, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.16.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE MD5sum: d4d77e311de5bed6ac1c6f8f0e97be26 NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: RELEASE_3_16 git_last_commit: d148550 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/icetea_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/icetea_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/icetea_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/icetea_1.16.0.tgz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 131 Package: iCheck Version: 1.28.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) MD5sum: 6109fae95ba18dde28e766750ad6f2cf NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/iCheck git_branch: RELEASE_3_16 git_last_commit: 2b0b9cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iCheck_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCheck_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCheck_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iCheck_1.28.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R dependencyCount: 185 Package: iChip Version: 1.52.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) MD5sum: 6b935119adc7fcfab78edc9a18efb0e5 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: RELEASE_3_16 git_last_commit: 7465688 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iChip_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iChip_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iChip_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iChip_1.52.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 6 Package: iClusterPlus Version: 1.34.3 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: x64 MD5sum: 446df9647ffb7593135e37ec7681d6ee NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: RELEASE_3_16 git_last_commit: 527db6a git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/iClusterPlus_1.34.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/iClusterPlus_1.34.3.zip mac.binary.ver: bin/macosx/contrib/4.2/iClusterPlus_1.34.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iClusterPlus_1.34.3.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 1 Package: iCNV Version: 1.18.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 Archs: x64 MD5sum: f8cd3706a753d57d45c13c9afd7f1fba NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: RELEASE_3_16 git_last_commit: 940d8a3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iCNV_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCNV_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCNV_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iCNV_1.18.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 91 Package: iCOBRA Version: 1.26.0 Depends: R (>= 4.0) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR, markdown Suggests: knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 855ad8f9d8bae650ca48651a6e224425 NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. Various types of performance plots can be generated programmatically. The package also contains a shiny application for interactive exploration of results. biocViews: Classification, Visualization Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/iCOBRA VignetteBuilder: knitr BugReports: https://github.com/csoneson/iCOBRA/issues git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: RELEASE_3_16 git_last_commit: 57a837f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iCOBRA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iCOBRA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iCOBRA_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iCOBRA_1.26.0.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: muscat, SummarizedBenchmark dependencyCount: 90 Package: ideal Version: 1.22.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, pcaExplorer, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, rlang, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, base64enc, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE MD5sum: 1eb9bcd77d4a94a823ba6db7631fa52f NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues git_url: https://git.bioconductor.org/packages/ideal git_branch: RELEASE_3_16 git_last_commit: fba1b51 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ideal_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/ideal_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ideal_1.22.0.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 205 Package: IdeoViz Version: 1.34.0 Depends: R (>= 3.5.0), Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer, graphics, GenomeInfoDb License: GPL-2 MD5sum: 3e95583d655f8d6d52936d698662ec56 NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: RELEASE_3_16 git_last_commit: 5d46b59 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IdeoViz_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IdeoViz_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IdeoViz_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IdeoViz_1.34.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R dependencyCount: 47 Package: idiogram Version: 1.74.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 7d51b576734737dee8d48c0cf872e22c NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: RELEASE_3_16 git_last_commit: 15ae01e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/idiogram_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/idiogram_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/idiogram_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/idiogram_1.74.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependencyCount: 50 Package: idpr Version: 1.8.0 Depends: R (>= 4.1.0) Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr (>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings (>= 2.56.0), methods (>= 4.0.0) Suggests: knitr, rmarkdown, msa, ape, testthat, seqinr License: LGPL (>= 3) MD5sum: 98d09f4004b057311727fe4ad89753c2 NeedsCompilation: no Title: Profiling and Analyzing Intrinsically Disordered Proteins in R Description: ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: RELEASE_3_16 git_last_commit: f34f081 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/idpr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/idpr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/idpr_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/idpr_1.8.0.tgz vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html, vignettes/idpr/inst/doc/disorderedMatrices-vignette.html, vignettes/idpr/inst/doc/idpr-vignette.html, vignettes/idpr/inst/doc/iupred-vignette.html, vignettes/idpr/inst/doc/sequenceMAP-vignette.html, vignettes/idpr/inst/doc/structuralTendency-vignette.html vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices Vignette, idpr Package Overview Vignette, IUPred Vignette, Sequence Map Vignette, Structural Tendency Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R, vignettes/idpr/inst/doc/disorderedMatrices-vignette.R, vignettes/idpr/inst/doc/idpr-vignette.R, vignettes/idpr/inst/doc/iupred-vignette.R, vignettes/idpr/inst/doc/sequenceMAP-vignette.R, vignettes/idpr/inst/doc/structuralTendency-vignette.R dependencyCount: 54 Package: idr2d Version: 1.12.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, grid, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 111faa3f883d17b9de57ea471163f1a8 NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (), David Gifford [ths, cph] () Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: RELEASE_3_16 git_last_commit: b85356d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/idr2d_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/idr2d_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/idr2d_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/idr2d_1.12.0.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 65 Package: iGC Version: 1.28.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: adc544778d006a630e94b1ab819d2148 NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: RELEASE_3_16 git_last_commit: 67a0daf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iGC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iGC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iGC_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iGC_1.28.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.12.0 Depends: R (>= 4.2.0) Imports: methods, reshape2 (>= 1.4.3), Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools (>= 2.2.0), SummarizedExperiment LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, gridExtra, ggrepel License: MIT + file LICENSE MD5sum: 0c9efbfe192d2309fa0ddad0e9892659 NeedsCompilation: yes Title: Differential gene usage in immune repertoires Description: Detection of biases in the usage of immunoglobulin (Ig) genes is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage between biological conditions using a probabilistic model which is analyzed computationally by Bayes inference. With this IgGeneUsage also avoids some common problems related to the current practice of null-hypothesis significance testing. biocViews: DifferentialExpression, Regression, Genetics, Bayesian, BiomedicalInformatics, ImmunoOncology, MathematicalBiology Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/IgGeneUsage SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: RELEASE_3_16 git_last_commit: d1136a7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IgGeneUsage_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IgGeneUsage_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IgGeneUsage_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IgGeneUsage_1.12.0.tgz vignettes: vignettes/IgGeneUsage/inst/doc/User_Manual.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/User_Manual.R dependencyCount: 77 Package: igvR Version: 1.18.1 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.17.1) Imports: methods, BiocGenerics, httpuv, utils, rtracklayer, VariantAnnotation, RColorBrewer, httr Suggests: RUnit, BiocStyle, knitr, rmarkdown, MotifDb, seqLogo License: MIT + file LICENSE Archs: x64 MD5sum: c470f1474704efcfff03c9b843f904f6 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_16 git_last_commit: 490c903 git_last_commit_date: 2023-03-06 Date/Publication: 2023-03-07 source.ver: src/contrib/igvR_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/igvR_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/igvR_1.18.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/igvR_1.18.1.tgz vignettes: vignettes/igvR/inst/doc/v00.basicIntro.html, vignettes/igvR/inst/doc/v01.stockGenome.html, vignettes/igvR/inst/doc/v02.customGenome.html, vignettes/igvR/inst/doc/v03.ctcfChIP.html, vignettes/igvR/inst/doc/v04.pairedEnd.html, vignettes/igvR/inst/doc/v05.ucscTableBrowser.html, vignettes/igvR/inst/doc/v06.annotationHub.html, vignettes/igvR/inst/doc/v07.gwas.html vignetteTitles: "Introduction: a simple demo", "Use a Stock Genome", "Use a Custom Genome", "Explore CTCF ChIP-seq alignments,, MACS2 narrowPeaks,, Motif Matching and H3K4me3 methylation", "Paired-end Interaction Tracks", Obtain and Display H3K4Me3 K562 track from UCSC table browser, "Obtain and Display H3K27ac K562 track from the AnnotationHub", "GWAS Tracks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/v00.basicIntro.R, vignettes/igvR/inst/doc/v01.stockGenome.R, vignettes/igvR/inst/doc/v02.customGenome.R, vignettes/igvR/inst/doc/v03.ctcfChIP.R, vignettes/igvR/inst/doc/v04.pairedEnd.R, vignettes/igvR/inst/doc/v05.ucscTableBrowser.R, vignettes/igvR/inst/doc/v06.annotationHub.R, vignettes/igvR/inst/doc/v07.gwas.R dependencyCount: 104 Package: IHW Version: 1.26.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: c8c7ad439df987dc6ae230cf7ed1059f NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: RELEASE_3_16 git_last_commit: d37e3c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IHW_1.26.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/IHW_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IHW_1.26.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal suggestsMe: DEWSeq, GRaNIE, metagenomeSeq, SummarizedBenchmark, BloodCancerMultiOmics2017, BisRNA, DGEobj.utils dependencyCount: 9 Package: illuminaio Version: 0.40.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 MD5sum: 09720bf90cecb5bdf6ce8a459e293341 NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: RELEASE_3_16 git_last_commit: 9c71d09 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/illuminaio_0.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/illuminaio_0.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/illuminaio_0.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/illuminaio_0.40.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123 importsMe: beadarray, bigmelon, crlmm, methylumi, minfi, sesame suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.8.0 Depends: R (>= 4.0.0) Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM, ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster, parallelDist, cluster, dendextend, DescTools, plyr, scales, pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils Suggests: knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 31b0968a90c4489b5afcfee87035a6d4 NeedsCompilation: no Title: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data Description: ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Transcriptomics, DataRepresentation, DifferentialExpression, Transcription, GeneExpression Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S Venäläinen [aut], Laura L Elo [aut] Maintainer: Johannes Smolander URL: https://github.com/elolab/ILoReg VignetteBuilder: knitr BugReports: https://github.com/elolab/ILoReg/issues git_url: https://git.bioconductor.org/packages/ILoReg git_branch: RELEASE_3_16 git_last_commit: 4d1cbcf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ILoReg_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ILoReg_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ILoReg_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ILoReg_1.8.0.tgz vignettes: vignettes/ILoReg/inst/doc/ILoReg.html vignetteTitles: ILoReg package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R dependencyCount: 124 Package: imageHTS Version: 1.48.0 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: 59a274d156e657c237e0ceaff50e0da9 NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: ImmunoOncology, Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry git_url: https://git.bioconductor.org/packages/imageHTS git_branch: RELEASE_3_16 git_last_commit: b9c2829 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/imageHTS_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/imageHTS_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/imageHTS_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/imageHTS_1.48.0.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependencyCount: 117 Package: IMAS Version: 1.22.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 MD5sum: da261c59ed59184de8982382a9a2a72b NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IMAS git_branch: RELEASE_3_16 git_last_commit: 662ff82 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IMAS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IMAS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IMAS_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IMAS_1.22.0.tgz vignettes: vignettes/IMAS/inst/doc/IMAS.pdf vignetteTitles: IMAS : Integrative analysis of Multi-omics data for Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMAS/inst/doc/IMAS.R dependencyCount: 138 Package: imcRtools Version: 1.4.2 Depends: R (>= 4.1), SpatialExperiment Imports: S4Vectors, stats, utils, SummarizedExperiment, methods, pheatmap, scuttle, stringr, readr, EBImage, cytomapper, abind, BiocParallel, viridis, dplyr, magrittr, DT, igraph, SingleCellExperiment, vroom, BiocNeighbors, RTriangle, ggraph, tidygraph, ggplot2, data.table, sf, concaveman, tidyselect, distances, MatrixGenerics Suggests: CATALYST, grid, BiocStyle, knitr, rmarkdown, markdown, testthat License: GPL-3 MD5sum: dbaaa6bce7b568377e37d74fd92ae79d NeedsCompilation: no Title: Methods for imaging mass cytometry data analysis Description: This R package supports the handling and analysis of imaging mass cytometry and other highly multiplexed imaging data. The main functionality includes reading in single-cell data after image segmentation and measurement, data formatting to perform channel spillover correction and a number of spatial analysis approaches. First, cell-cell interactions are detected via spatial graph construction; these graphs can be visualized with cells representing nodes and interactions representing edges. Furthermore, per cell, its direct neighbours are summarized to allow spatial clustering. Per image/grouping level, interactions between types of cells are counted, averaged and compared against random permutations. In that way, types of cells that interact more (attraction) or less (avoidance) frequently than expected by chance are detected. biocViews: ImmunoOncology, SingleCell, Spatial, DataImport, Clustering Author: Nils Eling [aut, cre] (), Tobias Hoch [ctb], Vito Zanotelli [ctb], Jana Fischer [ctb], Daniel Schulz [ctb], Lasse Meyer [ctb] Maintainer: Nils Eling URL: https://github.com/BodenmillerGroup/imcRtools VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/imcRtools/issues git_url: https://git.bioconductor.org/packages/imcRtools git_branch: RELEASE_3_16 git_last_commit: bef2371 git_last_commit_date: 2022-11-23 Date/Publication: 2022-11-24 source.ver: src/contrib/imcRtools_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/imcRtools_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/imcRtools_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/imcRtools_1.4.2.tgz vignettes: vignettes/imcRtools/inst/doc/imcRtools.html vignetteTitles: "Tools for IMC data analysis" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imcRtools/inst/doc/imcRtools.R dependencyCount: 193 Package: IMMAN Version: 1.18.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: d37339ec4b7f34450fa6f1341d352482 NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: RELEASE_3_16 git_last_commit: 05ffbe6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IMMAN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IMMAN_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IMMAN_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IMMAN_1.18.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 62 Package: ImmuneSpaceR Version: 1.26.1 Depends: R (>= 3.5.0) Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.3.1), Biobase, pheatmap, ggplot2 (>= 3.2.0), scales, stats, gplots, plotly, heatmaply (>= 0.7.0), jsonlite, rmarkdown, preprocessCore, flowCore, flowWorkspace, digest Suggests: knitr, testthat, covr, withr License: GPL-2 MD5sum: d092c8d110782396dd73cc05243f5026 NeedsCompilation: no Title: A Thin Wrapper around the ImmuneSpace Data and Tools Portal Description: Provides a convenient API for accessing data sets within ImmuneSpace Data and Tools Portal (datatools.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). biocViews: DataImport, DataRepresentation, ThirdPartyClient Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong Kim [aut], Lauren Wolfe [aut], Helen Miller [aut], Raphael Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph] Maintainer: ImmuneSpace Package Maintainer URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues git_url: https://git.bioconductor.org/packages/ImmuneSpaceR git_branch: RELEASE_3_16 git_last_commit: 23b2211 git_last_commit_date: 2022-12-14 Date/Publication: 2022-12-15 source.ver: src/contrib/ImmuneSpaceR_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ImmuneSpaceR_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ImmuneSpaceR_1.26.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ImmuneSpaceR_1.26.1.tgz vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.html, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html vignetteTitles: Downloading Datasets with getDataset, Handling Expression Matrices with ImmuneSpaceR, interactive_netrc() Function Walkthrough, An Introduction to the ImmuneSpaceR Package, SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts, SDY180: Abundance of Plasmablasts Measured by Multiparameter Flow Cytometry, SDY269: Correlating HAI with Flow Cytometry and ELISPOT Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.R, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R dependencyCount: 130 Package: immunoClust Version: 1.30.0 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils, testthat License: Artistic-2.0 MD5sum: af6686f550a3addb168fba5586b32265 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples. biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays, ImmunoOncology Author: Till Soerensen [aut, cre] Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: RELEASE_3_16 git_last_commit: be507cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/immunoClust_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/immunoClust_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/immunoClust_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/immunoClust_1.30.0.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 19 Package: immunotation Version: 1.5.0 Depends: R (>= 4.1) Imports: stringr, ontologyIndex, curl, ggplot2, readr, rvest, tidyr, xml2, maps, rlang Suggests: BiocGenerics, rmarkdown, BiocStyle, knitr, testthat, DT License: GPL-3 Archs: x64 MD5sum: 6928e1ab1489d2c4dfa125038c86bdc8 NeedsCompilation: no Title: Tools for working with diverse immune genes Description: MHC (major histocompatibility complex) molecules are cell surface complexes that present antigens to T cells. The repertoire of antigens presented in a given genetic background largely depends on the sequence of the encoded MHC molecules, and thus, in humans, on the highly variable HLA (human leukocyte antigen) genes of the hyperpolymorphic HLA locus. More than 28,000 different HLA alleles have been reported, with significant differences in allele frequencies between human populations worldwide. Reproducible and consistent annotation of HLA alleles in large-scale bioinformatics workflows remains challenging, because the available reference databases and software tools often use different HLA naming schemes. The package immunotation provides tools for consistent annotation of HLA genes in typical immunoinformatics workflows such as for example the prediction of MHC-presented peptides in different human donors. Converter functions that provide mappings between different HLA naming schemes are based on the MHC restriction ontology (MRO). The package also provides automated access to HLA alleles frequencies in worldwide human reference populations stored in the Allele Frequency Net Database. biocViews: Software, ImmunoOncology, BiomedicalInformatics, Genetics, Annotation Author: Katharina Imkeller [cre, aut] Maintainer: Katharina Imkeller VignetteBuilder: knitr BugReports: https://github.com/imkeller/immunotation/issues git_url: https://git.bioconductor.org/packages/immunotation git_branch: master git_last_commit: b6daf54 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/immunotation_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/immunotation_1.5.0.zip mac.binary.ver: bin/macosx/contrib/4.2/immunotation_1.5.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/immunotation_1.6.0.tgz vignettes: vignettes/immunotation/inst/doc/immunotation_vignette.html vignetteTitles: User guide immunotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunotation/inst/doc/immunotation_vignette.R dependencyCount: 66 Package: IMPCdata Version: 1.34.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 5d0033583a1d7f2f45a027682244133f NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: RELEASE_3_16 git_last_commit: 1e796b2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IMPCdata_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IMPCdata_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IMPCdata_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IMPCdata_1.34.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: impute Version: 1.72.3 Depends: R (>= 2.10) License: GPL-2 MD5sum: baa1f0961301290fe70dd79e274790cc NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: RELEASE_3_16 git_last_commit: 10f9b0d git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/impute_1.72.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/impute_1.72.3.zip mac.binary.ver: bin/macosx/contrib/4.2/impute_1.72.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/impute_1.72.3.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, iC10, imputeLCMD, moduleColor, snpReady, swamp importsMe: biscuiteer, CancerSubtypes, cola, DExMA, doppelgangR, EGAD, EpiMix, fastLiquidAssociation, genefu, genomation, GEOexplorer, MAGAR, MatrixQCvis, MEAT, methylclock, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, armada, DIscBIO, FAMT, lilikoi, mi4p, PINSPlus, Rnmr1D, samr, speaq, WGCNA suggestsMe: BioNet, DAPAR, graphite, mbOmic, MethPed, MsCoreUtils, QFeatures, qmtools, RnBeads, scp, TBSignatureProfiler, TCGAutils, DDPNA, DGCA, GSA, maGUI, metabolomicsR, MetChem, romic dependencyCount: 0 Package: INDEED Version: 2.12.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 8b1f51b45f593dbc5aa278ace607d6dd NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: RELEASE_3_16 git_last_commit: 0f4116b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/INDEED_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/INDEED_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/INDEED_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/INDEED_2.12.0.tgz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 108 Package: infercnv Version: 1.14.2 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, phyclust, Matrix, fastcluster, parallelDist, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, RANN, igraph, reshape2, rjags, fitdistrplus, future, foreach, doParallel, Seurat, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: c7e0ea1ae020eb31ede9655c0634615a NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: RELEASE_3_16 git_last_commit: 1cf5a95 git_last_commit_date: 2023-03-08 Date/Publication: 2023-03-08 source.ver: src/contrib/infercnv_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/infercnv_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.2/infercnv_1.14.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/infercnv_1.14.2.tgz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R suggestsMe: SCpubr dependencyCount: 192 Package: infinityFlow Version: 1.8.0 Depends: R (>= 4.0.0), flowCore Imports: stats, grDevices, utils, graphics, pbapply, matlab, png, raster, grid, uwot, gtools, Biobase, generics, parallel, methods, xgboost Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071 License: GPL-3 MD5sum: ca6052aa905a58bc1f4c810b7b0769f2 NeedsCompilation: no Title: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions Description: Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates). biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics Author: Etienne Becht [cre, aut] Maintainer: Etienne Becht VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: RELEASE_3_16 git_last_commit: b44bdd7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/infinityFlow_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/infinityFlow_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/infinityFlow_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/infinityFlow_1.8.0.tgz vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html vignetteTitles: Basic usage of the infinityFlow package, Training non default regression models hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R dependencyCount: 39 Package: Informeasure Version: 1.8.0 Depends: R (>= 4.0) Imports: entropy Suggests: knitr, rmarkdown, testthat, SummarizedExperiment License: GPL-3 MD5sum: e6e6b60623b78a76f05c5b748f48ace2 NeedsCompilation: no Title: R implementation of Information measures Description: This package compiles most of the information measures currently available: mutual information, conditional mutual information, interaction information, partial information decomposition and part mutual information. All of these estimators can be used to quantify nonlinear dependence between variables in (biological regulatory) network inference. The first estimator is used to infer bivariate networks while the last four estimators are dedicated to analysis of trivariate networks. biocViews: GeneExpression, NetworkInference, Network, Software Author: Chu Pan [aut, cre] Maintainer: Chu Pan URL: https://github.com/chupan1218/Informeasure VignetteBuilder: knitr BugReports: https://github.com/chupan1218/Informeasure/issues git_url: https://git.bioconductor.org/packages/Informeasure git_branch: RELEASE_3_16 git_last_commit: 96a4428 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Informeasure_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Informeasure_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Informeasure_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Informeasure_1.8.0.tgz vignettes: vignettes/Informeasure/inst/doc/Informeasure.html vignetteTitles: Informeasure: a tool to quantify nonlinear dependence between variables in biological regulatory networks from an information theory perspective hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R dependencyCount: 1 Package: InPAS Version: 2.6.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi,batchtools,Biobase,Biostrings,BSgenome,cleanUpdTSeq, depmixS4,dplyr,flock,future,future.apply,GenomeInfoDb,GenomicRanges, GenomicFeatures, ggplot2, IRanges, limma, magrittr,methods,parallelly, plyranges, preprocessCore, readr,reshape2, RSQLite, stats,S4Vectors, utils Suggests: BiocGenerics,BiocManager, BiocStyle, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.UCSC.hg19, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, knitr, markdown, rmarkdown, rtracklayer, RUnit, grDevices, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL (>= 2) MD5sum: 0cb50ec2d6ad871617d663b318772bcb NeedsCompilation: no Title: Identify Novel Alternative PolyAdenylation Sites (PAS) from RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites. biocViews: Alternative Polyadenylation, Differential Polyadenylation Site Usage, RNA-seq, Gene Regulation, Transcription Author: Jianhong Ou [aut, cre], Haibo Liu [aut], Lihua Julie Zhu [aut], Sungmi M. Park [aut], Michael R. Green [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: RELEASE_3_16 git_last_commit: 69d5e3b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/InPAS_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InPAS_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InPAS_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/InPAS_2.6.0.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 149 Package: INPower Version: 1.34.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 028b8592df8fc63d4909754d9ff6bbd0 NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: RELEASE_3_16 git_last_commit: d783431 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/INPower_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/INPower_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/INPower_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/INPower_1.34.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 3 Package: INSPEcT Version: 1.28.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, gdata, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, rtracklayer, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: fd76bc5656c0125fc89a25ffe65893b3 NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: RELEASE_3_16 git_last_commit: aff3971 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/INSPEcT_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/INSPEcT_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/INSPEcT_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/INSPEcT_1.28.0.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 140 Package: InTAD Version: 1.18.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 2c792fc59b1a96d7befa8a4a4274ef12 NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals (i.e. enhancers obtained from ChIP-seq data) either within topologically associated domains (TADs) or between chromatin contact loop anchors. Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: RELEASE_3_16 git_last_commit: 3bfaca4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/InTAD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InTAD_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InTAD_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/InTAD_1.18.0.tgz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 130 Package: intansv Version: 1.38.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: d99a77be86cd36cba32a835c584a224c NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: RELEASE_3_16 git_last_commit: 9557011 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/intansv_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/intansv_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/intansv_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/intansv_1.38.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R dependencyCount: 157 Package: interacCircos Version: 1.8.0 Depends: R (>= 4.1) Imports: RColorBrewer, htmlwidgets, plyr, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b1bab11b02ff1f3cb0a217c13971c18d NeedsCompilation: no Title: The Generation of Interactive Circos Plot Description: Implement in an efficient approach to display the genomic data, relationship, information in an interactive circular genome(Circos) plot. 'interacCircos' are inspired by 'circosJS', 'BioCircos.js' and 'NG-Circos' and we integrate the modules of 'circosJS', 'BioCircos.js' and 'NG-Circos' into this R package, based on 'htmlwidgets' framework. biocViews: Visualization Author: Zhe Cui [aut, cre] Maintainer: Zhe Cui VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interacCircos git_branch: RELEASE_3_16 git_last_commit: a0aedac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/interacCircos_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/interacCircos_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/interacCircos_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/interacCircos_1.8.0.tgz vignettes: vignettes/interacCircos/inst/doc/interacCircos.html vignetteTitles: interacCircos hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interacCircos/inst/doc/interacCircos.R dependencyCount: 40 Package: InteractionSet Version: 1.26.1 Depends: GenomicRanges, SummarizedExperiment Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12), IRanges, GenomeInfoDb LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 3717bbec61eaa705ad643ca30de03317 NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: RELEASE_3_16 git_last_commit: af8ff71 git_last_commit_date: 2023-01-27 Date/Publication: 2023-01-27 source.ver: src/contrib/InteractionSet_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/InteractionSet_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/InteractionSet_1.26.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/InteractionSet_1.26.1.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Genomic interaction classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, GenomicInteractions, HiCDOC, sevenC, nullrangesData importsMe: CAGEfightR, ChIPpeakAnno, extraChIPs, HiCcompare, HiContacts, nullranges, trackViewer suggestsMe: plotgardener, updateObject, CAGEWorkflow dependencyCount: 26 Package: InteractiveComplexHeatmap Version: 1.6.0 Depends: R (>= 4.0.0), ComplexHeatmap (>= 2.11.0) Imports: grDevices, stats, shiny, grid, GetoptLong, S4Vectors (>= 0.26.1), digest, IRanges, kableExtra (>= 1.3.1), utils, svglite, htmltools, clisymbols, jsonlite, RColorBrewer, fontawesome Suggests: knitr, rmarkdown, testthat, EnrichedHeatmap, GenomicRanges, data.table, circlize, GenomicFeatures, tidyverse, tidyHeatmap, cluster, org.Hs.eg.db, simplifyEnrichment, GO.db, SC3, GOexpress, SingleCellExperiment, scater, gplots, pheatmap, airway, DESeq2, DT, cola, BiocManager, gridtext, HilbertCurve (>= 1.21.1), shinydashboard, SummarizedExperiment, pkgndep, ks License: MIT + file LICENSE Archs: x64 MD5sum: b133888faaefa74bd293705ee885dde8 NeedsCompilation: no Title: Make Interactive Complex Heatmaps Description: This package can easily make heatmaps which are produced by the ComplexHeatmap package into interactive applications. It provides two types of interactivities: 1. on the interactive graphics device, and 2. on a Shiny app. It also provides functions for integrating the interactive heatmap widgets for more complex Shiny app development. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/InteractiveComplexHeatmap VignetteBuilder: knitr BugReports: https://github.com/jokergoo/InteractiveComplexHeatmap/issues git_url: https://git.bioconductor.org/packages/InteractiveComplexHeatmap git_branch: RELEASE_3_16 git_last_commit: b1f49d1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/InteractiveComplexHeatmap_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InteractiveComplexHeatmap_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InteractiveComplexHeatmap_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/InteractiveComplexHeatmap_1.6.0.tgz vignettes: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.html, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.html, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.html, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.html, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.html, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.html, vignettes/InteractiveComplexHeatmap/inst/doc/share.html, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.html vignetteTitles: 4. Decorations on heatmaps, 6. A Shiny app for visualizing DESeq2 results, 7. Implement interactive heatmap from scratch, 2. How interactive complex heatmap is implemented, 5. Interactivate heatmaps indirectly generated by pheatmap(),, heatmap.2() and heatmap(), 1. How to visualize heatmaps interactively, 8. Share interactive heatmaps to collaborators, 3. Functions for Shiny app development hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InteractiveComplexHeatmap/inst/doc/decoration.R, vignettes/InteractiveComplexHeatmap/inst/doc/deseq2_app.R, vignettes/InteractiveComplexHeatmap/inst/doc/from_scratch.R, vignettes/InteractiveComplexHeatmap/inst/doc/implementation.R, vignettes/InteractiveComplexHeatmap/inst/doc/interactivate_indirect.R, vignettes/InteractiveComplexHeatmap/inst/doc/InteractiveComplexHeatmap.R, vignettes/InteractiveComplexHeatmap/inst/doc/share.R, vignettes/InteractiveComplexHeatmap/inst/doc/shiny_dev.R suggestsMe: simplifyEnrichment dependencyCount: 97 Package: interactiveDisplay Version: 1.36.0 Depends: R (>= 3.5.0), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: fbedb786975f7b2801af9064bda55a1c NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: RELEASE_3_16 git_last_commit: 43249e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/interactiveDisplay_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/interactiveDisplay_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/interactiveDisplay_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/interactiveDisplay_1.36.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq dependencyCount: 113 Package: interactiveDisplayBase Version: 1.36.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr, markdown Enhances: rstudioapi License: Artistic-2.0 MD5sum: 8e4b897fd6e3150c333d7fcf7e47710d NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData, ShinyApps Author: Bioconductor Package Maintainer [cre], Shawn Balcome [aut], Marc Carlson [ctb], Marcel Ramos [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: RELEASE_3_16 git_last_commit: 79a0552 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/interactiveDisplayBase_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/interactiveDisplayBase_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/interactiveDisplayBase_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/interactiveDisplayBase_1.36.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: AnnotationHub, interactiveDisplay suggestsMe: recount3 dependencyCount: 52 Package: InterCellar Version: 2.4.0 Depends: R (>= 4.1) Imports: config, golem, shiny, DT, shinydashboard, shinyFiles, shinycssloaders, data.table, fs, dplyr, tidyr, circlize, colourpicker, dendextend, factoextra, ggplot2, plotly, plyr, shinyFeedback, shinyalert, tibble, umap, visNetwork, wordcloud2, readxl, htmlwidgets, colorspace, signal, scales, htmltools, ComplexHeatmap, grDevices, stats, tools, utils, biomaRt, rlang, fmsb, igraph Suggests: testthat (>= 3.0.0), knitr, rmarkdown, glue, graphite, processx, attempt, BiocStyle, httr License: MIT + file LICENSE MD5sum: f5726b7e9214c90c33089b6eeb5dec6a NeedsCompilation: no Title: InterCellar: an R-Shiny app for interactive analysis and exploration of cell-cell communication in single-cell transcriptomics Description: InterCellar is implemented as an R/Bioconductor Package containing a Shiny app that allows users to interactively analyze cell-cell communication from scRNA-seq data. Starting from precomputed ligand-receptor interactions, InterCellar provides filtering options, annotations and multiple visualizations to explore clusters, genes and functions. Finally, based on functional annotation from Gene Ontology and pathway databases, InterCellar implements data-driven analyses to investigate cell-cell communication in one or multiple conditions. biocViews: Software, SingleCell, Visualization, GO, Transcriptomics Author: Marta Interlandi [cre, aut] () Maintainer: Marta Interlandi URL: https://github.com/martaint/InterCellar VignetteBuilder: knitr BugReports: https://github.com/martaint/InterCellar/issues git_url: https://git.bioconductor.org/packages/InterCellar git_branch: RELEASE_3_16 git_last_commit: 884f4d6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/InterCellar_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InterCellar_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InterCellar_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/InterCellar_2.4.0.tgz vignettes: vignettes/InterCellar/inst/doc/user_guide.html vignetteTitles: InterCellar User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterCellar/inst/doc/user_guide.R dependencyCount: 208 Package: IntEREst Version: 1.22.2 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 MD5sum: a787d32dada4fc5fccb10a90ce7e998d NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian , Mikko Frilander VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IntEREst git_branch: RELEASE_3_16 git_last_commit: 4c44b57 git_last_commit_date: 2022-11-07 Date/Publication: 2022-11-07 source.ver: src/contrib/IntEREst_1.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/IntEREst_1.22.2.zip mac.binary.ver: bin/macosx/contrib/4.2/IntEREst_1.22.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IntEREst_1.22.2.tgz vignettes: vignettes/IntEREst/inst/doc/IntEREst.html vignetteTitles: IntEREst,, Intron Exon Retention Estimator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R dependencyCount: 131 Package: InterMineR Version: 1.20.0 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GO.db, org.Hs.eg.db License: LGPL MD5sum: 87c23f38bcf693c73c1008fbd646ad3b NeedsCompilation: no Title: R Interface with InterMine-Powered Databases Description: Databases based on the InterMine platform such as FlyMine, modMine (modENCODE), RatMine, YeastMine, HumanMine and TargetMine are integrated databases of genomic, expression and protein data for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. This R package provides interfaces with these databases through webservices. It makes most from the correspondence of the data frame object in R and the table object in databases, while hiding the details of data exchange through XML or JSON. biocViews: GeneExpression, SNP, GeneSetEnrichment, DifferentialExpression, GeneRegulation, GenomeAnnotation, GenomeWideAssociation, FunctionalPrediction, AlternativeSplicing, ComparativeGenomics, FunctionalGenomics, Proteomics, SystemsBiology, Microarray, MultipleComparison, Pathways, GO, KEGG, Reactome, Visualization Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis, Celia Sanchez Maintainer: InterMine Team VignetteBuilder: knitr BugReports: https://github.com/intermine/intermineR/issues git_url: https://git.bioconductor.org/packages/InterMineR git_branch: RELEASE_3_16 git_last_commit: 054eb84 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/InterMineR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/InterMineR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/InterMineR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/InterMineR_1.20.0.tgz vignettes: vignettes/InterMineR/inst/doc/InterMineR.html vignetteTitles: InterMineR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterMineR/inst/doc/InterMineR.R dependencyCount: 60 Package: IntramiRExploreR Version: 1.20.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: gProfileR, topGO, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: fe7a435c3419bf8efaf6eaa12f344403 NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/IntramiRExploreR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/IntramiRExploreR git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: RELEASE_3_16 git_last_commit: 7b49d57 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IntramiRExploreR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IntramiRExploreR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IntramiRExploreR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IntramiRExploreR_1.20.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR.pdf, vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR.pdf, IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 37 Package: IONiseR Version: 2.22.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 49239175d1845e21c822674b5627465b NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: RELEASE_3_16 git_last_commit: 2484a93 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IONiseR_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IONiseR_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IONiseR_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IONiseR_2.22.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R dependencyCount: 88 Package: iPAC Version: 1.42.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: e565549812ba9c1b2c83be4784d280c4 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/iPAC git_branch: RELEASE_3_16 git_last_commit: bc96a8f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iPAC_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iPAC_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iPAC_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iPAC_1.42.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC dependencyCount: 29 Package: iPath Version: 1.4.2 Depends: R (>= 4.1), mclust, BiocParallel, survival Imports: Rcpp (>= 1.0.5), matrixStats, ggpubr, ggplot2, survminer, stats LinkingTo: Rcpp, RcppArmadillo Suggests: rmarkdown, BiocStyle, knitr License: GPL-2 MD5sum: 5b62b57187bc027b6abaabcd7b74fe56 NeedsCompilation: yes Title: iPath pipeline for detecting perturbed pathways at individual level Description: iPath is the Bioconductor package used for calculating personalized pathway score and test the association with survival outcomes. Abundant single-gene biomarkers have been identified and used in the clinics. However, hundreds of oncogenes or tumor-suppressor genes are involved during the process of tumorigenesis. We believe individual-level expression patterns of pre-defined pathways or gene sets are better biomarkers than single genes. In this study, we devised a computational method named iPath to identify prognostic biomarker pathways, one sample at a time. To test its utility, we conducted a pan-cancer analysis across 14 cancer types from The Cancer Genome Atlas and demonstrated that iPath is capable of identifying highly predictive biomarkers for clinical outcomes, including overall survival, tumor subtypes, and tumor stage classifications. We found that pathway-based biomarkers are more robust and effective than single genes. biocViews: Pathways, Software, GeneExpression, Survival Author: Kenong Su [aut, cre], Zhaohui Qin [aut] Maintainer: Kenong Su SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/suke18/iPath/issues git_url: https://git.bioconductor.org/packages/iPath git_branch: RELEASE_3_16 git_last_commit: 90947df git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/iPath_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/iPath_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/iPath_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iPath_1.4.2.tgz vignettes: vignettes/iPath/inst/doc/iPath.html vignetteTitles: The iPath User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPath/inst/doc/iPath.R dependencyCount: 123 Package: ipdDb Version: 1.16.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 Archs: x64 MD5sum: c08642bd043f91cf7af2083633f63b11 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation,AnnotationHubSoftware Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: RELEASE_3_16 git_last_commit: 7fe38ec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ipdDb_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ipdDb_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ipdDb_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ipdDb_1.16.0.tgz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 97 Package: IPO Version: 1.24.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: a10fbd6f9bca925b3d6218a7af4851f1 NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Lieb Maintainer: Thomas Lieb URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new git_url: https://git.bioconductor.org/packages/IPO git_branch: RELEASE_3_16 git_last_commit: df36c63 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IPO_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IPO_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IPO_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IPO_1.24.0.tgz vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R dependencyCount: 135 Package: IRanges Version: 2.32.0 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.39.2), S4Vectors (>= 0.33.3) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 4cace1518b529ce898b7e9f84fefa428 NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre], Patrick Aboyoun [aut], Michael Lawrence [aut] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_16 git_last_commit: 2b5c9fc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IRanges_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IRanges_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IRanges_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IRanges_2.32.0.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP, LiebermanAidenHiC2009 importsMe: ALDEx2, AllelicImbalance, alpine, amplican, AneuFinder, annmap, annotatr, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACCoGAPS, ATACseqQC, ATACseqTFEA, atena, ballgown, bamsignals, BBCAnalyzer, beadarray, BiocOncoTK, biovizBase, biscuiteer, BiSeq, BitSeq, bnbc, BPRMeth, branchpointer, breakpointR, BRGenomics, BSgenome, bsseq, BUMHMM, BumpyMatrix, BUSpaRse, CAGEfightR, cageminer, CAGEr, cBioPortalData, cfDNAPro, ChIC, ChIPanalyser, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, ChromSCape, chromstaR, chromswitch, chromVAR, cicero, CINdex, circRNAprofiler, CircSeqAlignTk, cleanUpdTSeq, cleaver, cn.mops, CNEr, CNVfilteR, CNVMetrics, CNVPanelizer, CNVRanger, CNVrd2, COCOA, comapr, coMET, coMethDMR, compEpiTools, ComplexHeatmap, CompoundDb, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprBase, crisprBowtie, crisprDesign, crisprScore, CRISPRseek, CrispRVariants, crisprViz, csaw, dada2, DAMEfinder, dasper, debrowser, DECIPHER, deconvR, DegNorm, DelayedMatrixStats, deltaCaptureC, derfinder, derfinderHelper, derfinderPlot, DEScan2, DiffBind, diffHic, diffUTR, DMRcate, DMRScan, dmrseq, DNAfusion, DominoEffect, dpeak, DRIMSeq, DropletUtils, dStruct, easyRNASeq, EDASeq, eisaR, ELMER, enhancerHomologSearch, EnrichedHeatmap, enrichTF, ensembldb, EpiCompare, epidecodeR, epigraHMM, EpiMix, epimutacions, epistack, EpiTxDb, epivizr, epivizrData, erma, esATAC, EventPointer, extraChIPs, factR, FastqCleaner, fastseg, fcScan, FilterFFPE, FindIT2, fishpond, FRASER, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, genomation, GenomAutomorphism, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractionNodes, GenomicInteractions, GenomicOZone, GenomicScores, GenomicTuples, genotypeeval, GenVisR, ggbio, girafe, gmapR, gmoviz, GOfuncR, GOpro, GOTHiC, GSVA, GUIDEseq, gwascat, h5vc, HDF5Array, heatmaps, hermes, HiCBricks, HiCcompare, HiContacts, HilbertCurve, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, InPAS, INSPEcT, intansv, InteractionSet, InteractiveComplexHeatmap, IntEREst, InterMineR, ipdDb, iSEEu, IsoformSwitchAnalyzeR, isomiRs, IVAS, karyoploteR, katdetectr, LinTInd, LOLA, m6Aboost, MADSEQ, maser, MatrixRider, mCSEA, MDTS, MEAL, MEDIPS, MesKit, metagene, metagene2, metaseqR2, MethCP, methimpute, methInheritSim, MethReg, methrix, methylCC, methylInheritance, methylKit, methylPipe, MethylSeekR, methylSig, methylumi, mia, microbiomeMarker, minfi, MinimumDistance, MIRA, missMethyl, MMAPPR2, Modstrings, monaLisa, mosaics, MOSim, Motif2Site, motifbreakR, motifmatchr, MouseFM, msa, MSA2dist, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsExperiment, msgbsR, MSnbase, MultiAssayExperiment, MultiDataSet, mumosa, MungeSumstats, musicatk, MutationalPatterns, NanoStringNCTools, ncRNAtools, normr, nucleoSim, nucleR, nullranges, NxtIRFcore, ODER, OGRE, oligoClasses, OmaDB, OMICsPCA, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, panelcn.mops, pcaExplorer, pdInfoBuilder, PhIPData, Pi, PICS, PING, plethy, plotgardener, podkat, polyester, pqsfinder, pram, prebs, preciseTAD, primirTSS, proActiv, profileplyr, ProteoDisco, PureCN, Pviz, QDNAseq, QFeatures, qpgraph, qPLEXanalyzer, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RaggedExperiment, ramr, RareVariantVis, Rcade, RCAS, recount, recoup, REDseq, regioneR, regutools, REMP, Repitools, ReportingTools, RESOLVE, rfaRm, rfPred, RgnTX, RiboCrypt, RiboDiPA, RiboProfiling, riboSeqR, ribosomeProfilingQC, RIPAT, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, roar, rprimer, Rqc, Rsamtools, RSVSim, RTN, rtracklayer, sarks, SCAN.UPC, scanMiR, scanMiRApp, SCArray, scDblFinder, scHOT, scPipe, segmenter, segmentSeq, SeqArray, seqCAT, seqPattern, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, signifinder, SimFFPE, SingleMoleculeFootprinting, sitadela, SMITE, snapcount, SNPhood, soGGi, SomaticSignatures, SparseSignatures, spatzie, Spectra, spicyR, spiky, SpliceWiz, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, StructuralVariantAnnotation, SummarizedExperiment, SynExtend, TAPseq, target, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, TreeSummarizedExperiment, TRESS, tricycle, tRNA, tRNAdbImport, tRNAscanImport, tscR, TVTB, txcutr, tximeta, UMI4Cats, Uniquorn, universalmotif, VanillaICE, VarCon, VariantAnnotation, VariantExperiment, VariantFiltering, VaSP, VDJdive, wavClusteR, wiggleplotr, xcms, xcore, XNAString, XVector, yamss, fitCons.UCSC.hg19, GenomicState, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, leeBamViews, MethylSeqData, pd.atdschip.tiling, sesameData, SomaticCancerAlterations, spatialLIBD, ActiveDriverWGS, alakazam, crispRdesignR, ExomeDepth, geneHapR, geno2proteo, ggcoverage, HiCfeat, hoardeR, ICAMS, lolliplot, LoopRig, MAAPER, MitoHEAR, MOCHA, noisyr, numbat, oncoPredict, PACVr, RapidoPGS, RTIGER, Signac, simMP, STRMPS, tidygenomics, utr.annotation, VALERIE, xQTLbiolinks suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, GWASTools, HilbertVis, HilbertVisGUI, maftools, martini, MiRaGE, multicrispr, regionReport, RTCGA, S4Vectors, SigsPack, splatter, svaNUMT, svaRetro, systemPipeR, TFutils, systemPipeRdata, xcoredata, yeastRNASeq, cancerTiming, fuzzyjoin, gkmSVM, MARVEL, pagoo, Platypus, polyRAD, rliger, scPloidy, seqmagick, Seurat, sigminer, SNPassoc, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: IRISFGM Version: 1.6.0 Depends: R (>= 4.1) Imports: Rcpp (>= 1.0.0), MCL, anocva, Polychrome, RColorBrewer, colorspace, AnnotationDbi, ggplot2, org.Hs.eg.db, org.Mm.eg.db, pheatmap, AdaptGauss, DEsingle,DrImpute, Matrix, Seurat, SingleCellExperiment, clusterProfiler, ggpubr, ggraph, igraph, mixtools, scater, scran, stats, methods, grDevices, graphics, utils, knitr LinkingTo: Rcpp Suggests: rmarkdown License: GPL-2 Archs: x64 MD5sum: 36095c043bb604c283f4c51c3df5ac91 NeedsCompilation: yes Title: Comprehensive Analysis of Gene Interactivity Networks Based on Single-Cell RNA-Seq Description: Single-cell RNA-Seq data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its availability is limited to a C implementation, and its applicative power is affected by only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (integrative scRNA-Seq interpretation system for functional gene module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can identify co-expressed and co-regulated FGMs, predict types/clusters, identify differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM also applies Seurat objects that can be easily used in the Seurat vignettes. biocViews: Software, GeneExpression, SingleCell, Clustering, DifferentialExpression, Preprocessing, DimensionReduction, Visualization, Normalization, DataImport Author: Yuzhou Chang [aut, cre], Qin Ma [aut], Carter Allen [aut], Dongjun Chung [aut] Maintainer: Yuzhou Chang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IRISFGM git_branch: RELEASE_3_16 git_last_commit: c2d45f0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IRISFGM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IRISFGM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IRISFGM_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IRISFGM_1.6.0.tgz vignettes: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.html vignetteTitles: IRIS-FGM vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRISFGM/inst/doc/IRISFGM_Rpackage.R dependencyCount: 294 Package: ISAnalytics Version: 1.8.3 Depends: R (>= 4.2) Imports: utils, dplyr, readr, tidyr, purrr, rlang, tibble, stringr, fs, lubridate, lifecycle, ggplot2, ggrepel, stats, readxl, tools, grDevices, forcats, glue, shiny, shinyWidgets, datamods, bslib, DT Suggests: testthat, covr, knitr, BiocStyle, sessioninfo, rmarkdown, roxygen2, vegan, withr, extraDistr, ggalluvial, scales, gridExtra, R.utils, RefManageR, flexdashboard, circlize, plotly, gtools, eulerr, openxlsx, jsonlite, pheatmap, BiocParallel, progressr, future, doFuture, foreach, psych, data.table, Rcapture License: CC BY 4.0 Archs: x64 MD5sum: 5e8ad9a16d045331a84bbaba7c85e04d NeedsCompilation: no Title: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies Description: In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo. biocViews: BiomedicalInformatics, Sequencing, SingleCell Author: Giulia Pais [aut, cre] (), Andrea Calabria [aut], Giulio Spinozzi [aut] Maintainer: Giulia Pais URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues git_url: https://git.bioconductor.org/packages/ISAnalytics git_branch: RELEASE_3_16 git_last_commit: 314ab5b git_last_commit_date: 2023-04-03 Date/Publication: 2023-04-03 source.ver: src/contrib/ISAnalytics_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/ISAnalytics_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.2/ISAnalytics_1.8.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ISAnalytics_1.8.1.tgz vignettes: vignettes/ISAnalytics/inst/doc/ISAnalytics.html, vignettes/ISAnalytics/inst/doc/workflow_start.html vignetteTitles: ISAnalytics, workflow_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/ISAnalytics.R, vignettes/ISAnalytics/inst/doc/workflow_start.R dependencyCount: 111 Package: iSEE Version: 2.10.0 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, ggrepel, colourpicker, igraph, vipor, mgcv, graphics, grDevices, viridisLite, shinyWidgets, ComplexHeatmap, circlize, grid Suggests: testthat, covr, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools License: MIT + file LICENSE MD5sum: 546c5e997807b948d022c86d3c02bf87 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (), Federico Marini [aut] (), Charlotte Soneson [aut] (), Aaron Lun [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEE VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: RELEASE_3_16 git_last_commit: f091a7c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iSEE_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSEE_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSEE_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iSEE_2.10.0.tgz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/bigdata.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/links.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data, 3. Configuring iSEE apps, 5. Deploying custom panels, 4. The ExperimentColorMap Class, 2. Sharing information across panels, 7. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/bigdata.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R, vignettes/iSEE/inst/doc/voice.R dependsOnMe: iSEEhex, iSEEu, OSCA.advanced importsMe: iSEEhub suggestsMe: schex, DuoClustering2018, HCAData, TabulaMurisData, TabulaMurisSenisData dependencyCount: 117 Package: iSEEhex Version: 1.0.0 Depends: SummarizedExperiment, iSEE Imports: ggplot2, hexbin, methods, shiny Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), scRNAseq, scater License: Artistic-2.0 MD5sum: 15702faef4cdba4d1c99d6cfc2a3bea3 NeedsCompilation: no Title: iSEE extension for summarising data points in hexagonal bins Description: This package provides panels summarising data points in hexagonal bins for `iSEE`. It is part of `iSEEu`, the iSEE universe of panels that extend the `iSEE` package. biocViews: Software, Infrastructure Author: Kevin Rue-Albrecht [aut, cre] (), Charlotte Soneson [aut] (), Federico Marini [aut] (), Aaron Lun [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhex VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhex git_url: https://git.bioconductor.org/packages/iSEEhex git_branch: RELEASE_3_16 git_last_commit: 1e2f7ce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iSEEhex_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSEEhex_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSEEhex_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iSEEhex_1.0.0.tgz vignettes: vignettes/iSEEhex/inst/doc/iSEEhex.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhex/inst/doc/iSEEhex.R dependsOnMe: iSEEu dependencyCount: 119 Package: iSEEhub Version: 1.0.0 Depends: SummarizedExperiment, SingleCellExperiment, ExperimentHub Imports: AnnotationHub, BiocManager, DT, iSEE, methods, rintrojs, S4Vectors, shiny, shinydashboard, shinyjs, utils Suggests: BiocStyle, covr, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0), nullrangesData Enhances: BioPlex, biscuiteerData, bodymapRat, CLLmethylation, CopyNeutralIMA, curatedAdipoArray, curatedAdipoChIP, curatedMetagenomicData, curatedTCGAData, DMRcatedata, DuoClustering2018, easierData, emtdata, epimutacionsData, FieldEffectCrc, GenomicDistributionsData, GSE103322, GSE13015, GSE62944, HDCytoData, HMP16SData, HumanAffyData, imcdatasets, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, MethylSeqData, muscData, NxtIRFdata, ObMiTi, quantiseqr, restfulSEData, RLHub, sesameData, SimBenchData, SingleCellMultiModal, SingleMoleculeFootprintingData, spatialDmelxsim, STexampleData, TabulaMurisData, TabulaMurisSenisData, TENxVisiumData, tissueTreg, VectraPolarisData, xcoredata License: Artistic-2.0 MD5sum: b9ab8001f392152a6e2f975b55e634cb NeedsCompilation: no Title: iSEE for the Bioconductor ExperimentHub Description: This package defines a custom landing page for an iSEE app interfacing with the Bioconductor ExperimentHub. The landing page allows users to browse the ExperimentHub, select a data set, download and cache it, and import it directly into a Bioconductor iSEE app. biocViews: Software, Infrastructure, DataImport, SingleCell, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEhub VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/iSEEhub git_url: https://git.bioconductor.org/packages/iSEEhub git_branch: RELEASE_3_16 git_last_commit: b704dd0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iSEEhub_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSEEhub_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSEEhub_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iSEEhub_1.0.0.tgz vignettes: vignettes/iSEEhub/inst/doc/contributing.html, vignettes/iSEEhub/inst/doc/iSEEhub.html vignetteTitles: Contributing to iSEEhub, Introduction to iSEEhub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSEEhub/inst/doc/contributing.R, vignettes/iSEEhub/inst/doc/iSEEhub.R dependencyCount: 145 Package: iSEEu Version: 1.10.1 Depends: iSEE, iSEEhex Imports: methods, S4Vectors, IRanges, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2, DT, stats, colourpicker, shinyAce Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi, org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown, BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr License: MIT + file LICENSE MD5sum: 1f20cf472035d3056d883463bb82e325 NeedsCompilation: no Title: iSEE Universe Description: iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (), Charlotte Soneson [aut] (), Federico Marini [aut] (), Aaron Lun [aut] (), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEu VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEEu/issues git_url: https://git.bioconductor.org/packages/iSEEu git_branch: RELEASE_3_16 git_last_commit: da7a208 git_last_commit_date: 2023-03-21 Date/Publication: 2023-03-22 source.ver: src/contrib/iSEEu_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSEEu_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/iSEEu_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iSEEu_1.10.1.tgz vignettes: vignettes/iSEEu/inst/doc/iSEEu.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/iSEEu.R dependencyCount: 120 Package: iSeq Version: 1.50.0 Depends: R (>= 2.10.0) License: GPL (>= 2) MD5sum: 55d0b7391fbff7a965eaff42e4c762c5 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: RELEASE_3_16 git_last_commit: ee9f52a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iSeq_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iSeq_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iSeq_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iSeq_1.50.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: ISLET Version: 1.0.0 Depends: R(>= 4.1.0), Matrix, parallel, BiocParallel, SummarizedExperiment, BiocGenerics Imports: stats, methods Suggests: BiocStyle, knitr, rmarkdown, htmltools, RUnit License: GPL-2 Archs: x64 MD5sum: c4d134f388541417611a33e41843ead9 NeedsCompilation: no Title: Individual-Specific ceLl typE referencing Tool Description: ISLET is a method to conduct signal deconvolution for general -omics data. It can estimate the individual-specific and cell-type-specific reference panels, when there are multiple samples observed from each subject. It takes the input of the observed mixture data (feature by sample matrix), and the cell type mixture proportions (sample by cell type matrix), and the sample-to-subject information. It can solve for the reference panel on the individual-basis. It can also conduct test to identify cell-type-specific differential expression (csDE) genes. biocViews: Software, RNASeq, Transcriptomics, Transcription, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Hao Feng [aut, cre], Qian Li [aut] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ISLET git_branch: RELEASE_3_16 git_last_commit: 6289465 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ISLET_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ISLET_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ISLET_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ISLET_1.0.0.tgz vignettes: vignettes/ISLET/inst/doc/ISLET.html vignetteTitles: Individual-specific and cell-type-specific deconvolution using ISLET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISLET/inst/doc/ISLET.R dependencyCount: 35 Package: isobar Version: 1.44.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: 4ca2c276ba2edfd43c57bd939739db00 NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: RELEASE_3_16 git_last_commit: 87ee415 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/isobar_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/isobar_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/isobar_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/isobar_1.44.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R suggestsMe: RforProteomics dependencyCount: 92 Package: IsoCorrectoR Version: 1.16.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2b085e251e417eaa32adf60808eb0d8e NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as ultra-high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing correction results to Excel files may currently not work on Windows. However, writing results to csv works as before. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: RELEASE_3_16 git_last_commit: d88e521 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IsoCorrectoR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoCorrectoR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoCorrectoR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IsoCorrectoR_1.16.0.tgz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 41 Package: IsoCorrectoRGUI Version: 1.14.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 5083f847eeece4e4c1a1eb6d5114e273 NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: RELEASE_3_16 git_last_commit: 8139b3c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IsoCorrectoRGUI_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoCorrectoRGUI_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoCorrectoRGUI_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IsoCorrectoRGUI_1.14.0.tgz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 44 Package: IsoformSwitchAnalyzeR Version: 1.20.0 Depends: R (>= 3.6), limma, DEXSeq, ggplot2 Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, DRIMSeq, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, GenomeInfoDb, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble, XVector, BiocGenerics, RCurl, Biobase Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, rmarkdown License: GPL (>= 2) MD5sum: 2efe2bf8079453afa4e6e8a84ee737fd NeedsCompilation: yes Title: Identify, Annotate and Visualize Alternative Splicing and Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data. Description: Analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, StringTie, Cufflinks/Cuffdiff etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup [cre, aut] () Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR git_branch: RELEASE_3_16 git_last_commit: bde4595 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IsoformSwitchAnalyzeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IsoformSwitchAnalyzeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IsoformSwitchAnalyzeR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IsoformSwitchAnalyzeR_1.20.0.tgz vignettes: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html vignetteTitles: IsoformSwitchAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R dependencyCount: 167 Package: ISoLDE Version: 1.26.0 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: x64 MD5sum: 97f2847503cc9dd23922fa63aa4affec NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: RELEASE_3_16 git_last_commit: 52fe91a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ISoLDE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ISoLDE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ISoLDE_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ISoLDE_1.26.0.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.26.0 Depends: R (>= 4.0), SummarizedExperiment Imports: AnnotationDbi, assertive.sets, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, rmarkdown, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE Archs: x64 MD5sum: 36e1c4d3cbfb4726daa48661e5bbedda NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] (CIBERESP - CIBER Epidemiologia y Salud Publica) Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: RELEASE_3_16 git_last_commit: 8d72245 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/isomiRs_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/isomiRs_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/isomiRs_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/isomiRs_1.26.0.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R dependencyCount: 147 Package: ITALICS Version: 2.58.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: c4aa57d40fd156f65b7e4e3789fe1670 NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: RELEASE_3_16 git_last_commit: 331d965 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ITALICS_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ITALICS_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ITALICS_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ITALICS_2.58.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 60 Package: iterativeBMA Version: 1.56.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 32ca318882c77a1d89db66bb2ca93f6b NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: RELEASE_3_16 git_last_commit: b3e7c8d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iterativeBMA_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iterativeBMA_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iterativeBMA_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iterativeBMA_1.56.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 21 Package: iterativeBMAsurv Version: 1.56.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 642bb275dd445cc856909ad88b9cc9bb NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: RELEASE_3_16 git_last_commit: d6486c9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iterativeBMAsurv_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iterativeBMAsurv_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iterativeBMAsurv_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iterativeBMAsurv_1.56.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: iterClust Version: 1.20.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE MD5sum: d930534e998ab24935b2061ec8e407b8 NeedsCompilation: no Title: Iterative Clustering Description: A framework for performing clustering analysis iteratively. biocViews: StatisticalMethod, Clustering Author: Hongxu Ding and Andrea Califano Maintainer: Hongxu Ding URL: https://github.com/hd2326/iterClust BugReports: https://github.com/hd2326/iterClust/issues git_url: https://git.bioconductor.org/packages/iterClust git_branch: RELEASE_3_16 git_last_commit: 6e85b98 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/iterClust_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/iterClust_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/iterClust_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/iterClust_1.20.0.tgz vignettes: vignettes/iterClust/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iterClust/inst/doc/introduction.R dependencyCount: 8 Package: IVAS Version: 2.18.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: b19151888f26e7f8961f68c6afb66aef NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: RELEASE_3_16 git_last_commit: 64c905d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IVAS_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IVAS_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IVAS_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IVAS_2.18.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependsOnMe: IMAS importsMe: ASpediaFI dependencyCount: 136 Package: ivygapSE Version: 1.20.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat, rmarkdown License: Artistic-2.0 MD5sum: efc34e82eebaaa523f9b062c273949e3 NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: RELEASE_3_16 git_last_commit: 4e457e0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ivygapSE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ivygapSE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ivygapSE_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ivygapSE_1.20.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 157 Package: IWTomics Version: 1.22.0 Depends: R (>= 3.5.0), GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 3fdd79bd52764b1a4c9ed66363150c2d NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: RELEASE_3_16 git_last_commit: 036af05 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/IWTomics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/IWTomics_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/IWTomics_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/IWTomics_1.22.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 69 Package: karyoploteR Version: 1.24.0 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 MD5sum: 0569368f4d39b2370a8c2f618fc04681 NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: RELEASE_3_16 git_last_commit: 442c9e7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/karyoploteR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/karyoploteR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/karyoploteR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/karyoploteR_1.24.0.tgz vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R dependsOnMe: CopyNumberPlots importsMe: CNVfilteR, CNViz, multicrispr, RIPAT suggestsMe: Category, EpiMix, MitoHEAR dependencyCount: 150 Package: katdetectr Version: 1.0.0 Depends: R (>= 4.2) Imports: BiocParallel (>= 1.26.2), changepoint (>= 2.2.3), changepoint.np (>= 1.0.3), checkmate (>= 2.0.0), dplyr (>= 1.0.8), GenomicRanges (>= 1.44.0), GenomeInfoDb (>= 1.28.4), IRanges (>= 2.26.0), maftools (>= 2.10.5), methods (>= 4.1.3), rlang (>= 1.0.2), S4Vectors (>= 0.30.2), tibble (>= 3.1.6), VariantAnnotation (>= 1.38.0), Biobase (>= 2.54.0), Rdpack (>= 2.3.1), ggplot2 (>= 3.3.5), tidyr (>= 1.2.0), BSgenome (>= 1.62.0), ggtext (>= 0.1.1), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.4), plyranges (>= 1.17.0) Suggests: scales (>= 1.2.0), knitr (>= 1.37), rmarkdown (>= 2.13), testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: f64e184697904a747bd4c2dde5987ac4 NeedsCompilation: no Title: Detection, Characterization and Visualization of Kataegis in Sequencing Data Description: Kataegis refers to the occurrence of regional hypermutation and is a phenomenon observed in a wide range of malignancies. Using changepoint detection katdetectr aims to identify putative kataegis foci from common data-formats housing genomic variants. Katdetectr has shown to be a robust package for the detection, characterization and visualization of kataegis. biocViews: WholeGenome, Software, SNP, Sequencing, Classification, VariantAnnotation Author: Daan Hazelaar [aut, cre] (), Job van Riet [aut] (), Harmen van de Werken [ths] () Maintainer: Daan Hazelaar URL: https://github.com/ErasmusMC-CCBC/katdetectr VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/katdetectr/issues git_url: https://git.bioconductor.org/packages/katdetectr git_branch: RELEASE_3_16 git_last_commit: 6401f53 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/katdetectr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/katdetectr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/katdetectr_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/katdetectr_1.0.0.tgz vignettes: vignettes/katdetectr/inst/doc/General_overview.html vignetteTitles: Overview_katdetectr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/katdetectr/inst/doc/General_overview.R dependencyCount: 133 Package: KBoost Version: 1.6.0 Depends: R (>= 4.1), stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 | GPL-3 MD5sum: 0a9650f874663e169cb961542c27dfd2 NeedsCompilation: no Title: Inference of gene regulatory networks from gene expression data Description: Reconstructing gene regulatory networks and transcription factor activity is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-art algorithm are often not able to handle large amounts of data. Furthermore, many of the present methods predict numerous false positives and are unable to integrate other sources of information such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. KBoost can also use a prior network built on previously known transcription factor targets. We have benchmarked KBoost using three different datasets against other high performing algorithms. The results show that our method compares favourably to other methods across datasets. biocViews: Network, GraphAndNetwork, Bayesian, NetworkInference, GeneRegulation, Transcriptomics, SystemsBiology, Transcription, GeneExpression, Regression, PrincipalComponent Author: Luis F. Iglesias-Martinez [aut, cre] (), Barbara de Kegel [aut], Walter Kolch [aut] Maintainer: Luis F. Iglesias-Martinez URL: https://github.com/Luisiglm/KBoost VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KBoost git_branch: RELEASE_3_16 git_last_commit: c5fb05a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/KBoost_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KBoost_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KBoost_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KBoost_1.6.0.tgz vignettes: vignettes/KBoost/inst/doc/KBoost.html vignetteTitles: KBoost hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KBoost/inst/doc/KBoost.R dependencyCount: 2 Package: KCsmart Version: 2.56.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: da0ce6a92f03b3830b227763cbdebc31 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: RELEASE_3_16 git_last_commit: 416f9bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/KCsmart_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KCsmart_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KCsmart_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KCsmart_2.56.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 18 Package: kebabs Version: 1.32.0 Depends: R (>= 3.3.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix (>= 1.5-0), XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) MD5sum: cfb970625761896bd8971487895fd3ac NeedsCompilation: yes Title: Kernel-Based Analysis Of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: RELEASE_3_16 git_last_commit: 559f77b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/kebabs_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/kebabs_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/kebabs_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/kebabs_1.32.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: odseq suggestsMe: spiky dependencyCount: 29 Package: KEGGgraph Version: 1.58.3 Depends: R (>= 3.5.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl, Rgraphviz Suggests: RBGL, testthat, RColorBrewer, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) Archs: x64 MD5sum: fb4f53cd677272ac5729ff2e051bb24c NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon and Hervé Pagès Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: RELEASE_3_16 git_last_commit: 483863c git_last_commit_date: 2022-12-18 Date/Publication: 2022-12-18 source.ver: src/contrib/KEGGgraph_1.58.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/KEGGgraph_1.58.3.zip mac.binary.ver: bin/macosx/contrib/4.2/KEGGgraph_1.58.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KEGGgraph_1.58.3.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, PFP, iCARH, kangar00, pathfindR suggestsMe: DEGraph, GenomicRanges, maGUI, rags2ridges dependencyCount: 13 Package: KEGGlincs Version: 1.24.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: 9ec567d1ddb633385b7d7309604a73b5 NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: RELEASE_3_16 git_last_commit: a4333f7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/KEGGlincs_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KEGGlincs_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KEGGlincs_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KEGGlincs_1.24.0.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 62 Package: keggorthology Version: 2.50.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: 1c115179d4ba4bf8fcf198f0d7c0cce7 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: RELEASE_3_16 git_last_commit: d843d06 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/keggorthology_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/keggorthology_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/keggorthology_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/keggorthology_2.50.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 49 Package: KEGGREST Version: 1.38.0 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr, markdown License: Artistic-2.0 MD5sum: 310e736e684002207adf8ee1350f2526 NeedsCompilation: no Title: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Description: A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum [aut], Jeremy Volkening [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_16 git_last_commit: 4dfbff9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/KEGGREST_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KEGGREST_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KEGGREST_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KEGGREST_1.38.0.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: ROntoTools, Hiiragi2013 importsMe: ADAM, adSplit, AnnotationDbi, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, gage, MetaboSignal, MWASTools, PADOG, pairkat, pathview, SBGNview, SMITE, transomics2cytoscape, YAPSA, pathfindR suggestsMe: Category, categoryCompare, GenomicRanges, globaltest, iSEEu, MLP, padma, rGREAT, RTopper, CALANGO, ggpicrust2, maGUI, ptm, scDiffCom dependencyCount: 27 Package: KinSwingR Version: 1.16.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 9e48afd16af58f4ec324e96b867996e7 NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: RELEASE_3_16 git_last_commit: 2d798eb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/KinSwingR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KinSwingR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KinSwingR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KinSwingR_1.16.0.tgz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 36 Package: kissDE Version: 1.18.0 Imports: aods3, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel, shiny, shinycssloaders, ade4, factoextra, DT Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 4c70aa04daa2c8f4050831fc629d5d4c NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot git_url: https://git.bioconductor.org/packages/kissDE git_branch: RELEASE_3_16 git_last_commit: c137078 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/kissDE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/kissDE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/kissDE_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/kissDE_1.18.0.tgz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R dependencyCount: 214 Package: KnowSeq Version: 1.12.0 Depends: R (>= 4.0), cqn (>= 1.28.1) Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab, rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML, praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3), limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>= 4.4.0), gridExtra Suggests: knitr License: GPL (>=2) MD5sum: a2347eabff59ff1e8447d7e0939263a7 NeedsCompilation: no Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline Description: KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Alignment, Pathways, SystemsBiology, GO, ImmunoOncology Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb] Maintainer: Daniel Castillo-Secilla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: RELEASE_3_16 git_last_commit: 74e466f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/KnowSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/KnowSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/KnowSeq_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/KnowSeq_1.12.0.tgz vignettes: vignettes/KnowSeq/inst/doc/KnowSeq.html vignetteTitles: The KnowSeq users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KnowSeq/inst/doc/KnowSeq.R dependencyCount: 173 Package: LACE Version: 2.2.0 Depends: R (>= 4.1.0) Imports: curl, igraph, foreach, doParallel, sortable, dplyr, data.tree, graphics, grDevices, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils, purrr, stringi, stringr, Matrix, tidyr, jsonlite, readr, configr, DT, tools, fs, data.table, htmltools, htmlwidgets, bsplus, shinyvalidate, shiny, shinythemes, shinyFiles, shinyjs, shinyBS, shinydashboard, biomaRt, callr, logr Suggests: BiocGenerics, BiocStyle, testthat, knitr, rmarkdown License: file LICENSE MD5sum: 1b5587f268daf05f93cacac41fbeb8b1 NeedsCompilation: no Title: Longitudinal Analysis of Cancer Evolution (LACE) Description: LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points. biocViews: BiomedicalInformatics, SingleCell, SomaticMutation Author: Daniele Ramazzotti [aut] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] (), Gianluca Ascolani [aut] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/LACE VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/LACE git_url: https://git.bioconductor.org/packages/LACE git_branch: RELEASE_3_16 git_last_commit: b65bac8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LACE_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LACE_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LACE_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LACE_2.2.0.tgz vignettes: vignettes/LACE/inst/doc/LACE1.pdf, vignettes/LACE/inst/doc/LACE2.pdf vignetteTitles: LACE, LACE 2.0 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/LACE1.R, vignettes/LACE/inst/doc/LACE2.R dependencyCount: 147 Package: lapmix Version: 1.64.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: fed4c097468e8ec0fbb5a3fc29364518 NeedsCompilation: no Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch git_url: https://git.bioconductor.org/packages/lapmix git_branch: RELEASE_3_16 git_last_commit: 20eb284 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lapmix_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lapmix_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lapmix_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lapmix_1.64.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R dependencyCount: 8 Package: LBE Version: 1.66.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: c1cbdc3a3dcdf9f7e36030bb664c3586 NeedsCompilation: no Title: Estimation of the false discovery rate. Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: RELEASE_3_16 git_last_commit: 4c62935 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LBE_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LBE_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LBE_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LBE_1.66.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependsOnMe: PhViD dependencyCount: 5 Package: ldblock Version: 1.28.0 Depends: R (>= 3.5), methods, rlang Imports: BiocGenerics (>= 0.25.1), httr, Matrix Suggests: RUnit, knitr, BiocStyle, gwascat, rmarkdown, snpStats, VariantAnnotation, GenomeInfoDb, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6) License: Artistic-2.0 MD5sum: d9007dd36c9a31da7126f72e09da0b1e NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: RELEASE_3_16 git_last_commit: 11779a3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ldblock_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ldblock_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ldblock_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ldblock_1.28.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R dependencyCount: 19 Package: LEA Version: 3.10.2 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 MD5sum: 6d7e5b285c8ee983f8bbd47cea5c2491 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to population genomics, landscape genomics and genotype-environment association tests. LEA can run analyses of population structure and genome-wide tests for local adaptation, and also performs imputation of missing genotypes. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm2). In addition, LEA computes values of genetic offset based on new or predicted environments. The package includes factor methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). LEA is mainly based on optimized programs that can scale with the dimensions of large data sets. biocViews: Software, Statistical Method, Clustering, Regression Author: Eric Frichot , Olivier Francois , Clement Gain Maintainer: Olivier Francois , Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: RELEASE_3_16 git_last_commit: 3f79292 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/LEA_3.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/LEA_3.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/LEA_3.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LEA_3.10.2.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R dependencyCount: 4 Package: LedPred Version: 1.32.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE Archs: x64 MD5sum: ef273f5e578acd4de2ece11d890718e7 NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: RELEASE_3_16 git_last_commit: 166c431 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LedPred_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LedPred_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LedPred_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LedPred_1.32.0.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 74 Package: lefser Version: 1.8.0 Depends: SummarizedExperiment, R (>= 4.0.0) Imports: coin, MASS, ggplot2, stats, methods Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, testthat, pkgdown, covr, withr License: Artistic-2.0 MD5sum: 1b8bd9edb5471f8868e8b227c19a8a3e NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is an implementation in R of the popular "LDA Effect Size (LEfSe)" method for microbiome biomarker discovery. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers of groups and sub-groups. biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Asya Khleborodova [cre, aut], Ludwig Geistlinger [ctb], Marcel Ramos [ctb] (), Levi Waldron [ctb] Maintainer: Asya Khleborodova URL: https://github.com/waldronlab/lefser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/lefser/issues git_url: https://git.bioconductor.org/packages/lefser git_branch: RELEASE_3_16 git_last_commit: 6fd31b2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lefser_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lefser_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lefser_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lefser_1.8.0.tgz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Introduction to the lefser R implementation of the popular LEfSE software for biomarker discovery in microbiome analysis. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R importsMe: ggpicrust2 dependencyCount: 63 Package: les Version: 1.48.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 90402854310292fa4a78300bdf61d20a NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: RELEASE_3_16 git_last_commit: 8339e16 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/les_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/les_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/les_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/les_1.48.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 13 Package: levi Version: 1.16.0 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods, rmarkdown LinkingTo: Rcpp Suggests: rmarkdown License: GPL (>= 2) MD5sum: 5d04bde0ec7292683758db61369402db NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Rafael Pilan [aut], Isabelle Silva [ctb], Agnes Takeda [ctb], Jose Rybarczyk Filho [ctb, cre, ths] Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: RELEASE_3_16 git_last_commit: 9669dee git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/levi_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/levi_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/levi_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/levi_1.16.0.tgz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 100 Package: lfa Version: 1.28.2 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: x64 MD5sum: 83b9da84c5b1c85673d6635aeab0b167 NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: LFA is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. biocViews: SNP, DimensionReduction, PrincipalComponent Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: RELEASE_3_16 git_last_commit: 7572f2f git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/lfa_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/lfa_1.28.2.zip mac.binary.ver: bin/macosx/contrib/4.2/lfa_1.28.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lfa_1.28.2.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest dependencyCount: 2 Package: limma Version: 3.54.2 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: x64 MD5sum: b669f0f8b3155c7b0ff778398af2243e NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_16 git_last_commit: 6641c25 git_last_commit_date: 2023-02-28 Date/Publication: 2023-02-28 source.ver: src/contrib/limma_3.54.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/limma_3.54.2.zip mac.binary.ver: bin/macosx/contrib/4.2/limma_3.54.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/limma_3.54.2.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, BLMA, cghMCR, codelink, convert, Cormotif, deco, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, GEOexplorer, HTqPCR, IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq, metaseqR2, mpra, NanoTube, NeuCA, octad, protGear, qpcrNorm, qusage, RBM, Ringo, RnBeads, splineTimeR, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, methylationArrayAnalysis, RNAseq123, OSCA.advanced, OSCA.basic, OSCA.workflows, BALLI, BioInsight, CEDA, countTransformers, cp4p, DAAGbio, DRomics, fmt, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, artMS, ASpediaFI, ATACseqQC, ATACseqTFEA, attract, autonomics, AWFisher, ballgown, BatchQC, beadarray, benchdamic, biotmle, BloodGen3Module, bnem, bsseq, BubbleTree, bumphunter, CancerSubtypes, casper, ChAMP, clusterExperiment, CNVRanger, coexnet, combi, compcodeR, consensusDE, consensusOV, crlmm, crossmeta, csaw, cTRAP, ctsGE, DAMEfinder, DaMiRseq, debrowser, DEP, derfinderPlot, DEsubs, DExMA, DiffBind, diffcyt, diffHic, diffUTR, distinct, DMRcate, Doscheda, DRIMSeq, eegc, EGAD, EGSEA, eisaR, EnrichmentBrowser, epigraHMM, EpiMix, erccdashboard, EventPointer, EWCE, ExploreModelMatrix, extraChIPs, flowBin, gCrisprTools, GDCRNATools, genefu, GeneSelectMMD, GEOquery, Glimma, GOsummaries, GRaNIE, hermes, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, limmaGUI, Linnorm, lipidr, lmdme, mAPKL, MatrixQCvis, MBECS, MBQN, mCSEA, MEAL, methylKit, MethylMix, microbiomeExplorer, microbiomeMarker, MIGSA, miloR, minfi, miRLAB, missMethyl, MLSeq, moanin, monocle, MoonlightR, msImpute, msqrob2, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, NanoMethViz, nethet, nondetects, NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, PanomiR, PathoStat, pcaExplorer, PECA, pepStat, phantasus, phenomis, phenoTest, PhosR, polyester, POMA, POWSC, projectR, psichomics, pwrEWAS, qPLEXanalyzer, qsea, RegEnrich, regsplice, Ringo, RNAinteract, ROSeq, RTCGAToolbox, RTN, RTopper, satuRn, scClassify, scone, scran, ScreenR, SEPIRA, seqsetvis, shinyepico, SimBindProfiles, SingleCellSignalR, singleCellTK, snapCGH, sparrow, spatialHeatmap, SPsimSeq, standR, STATegRa, sva, timecourse, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsclust, vsn, weitrix, Wrench, yamss, yarn, zenith, BeadArrayUseCases, DmelSGI, signatureSearchData, spatialLIBD, ExpHunterSuite, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, batchtma, BPM, Cascade, CIDER, cinaR, DiPALM, dsb, easyDifferentialGeneCoexpression, ggpicrust2, GWASbyCluster, INCATome, lilikoi, limorhyde2, lipidomeR, metaMA, mi4p, MiDA, miRtest, MKmisc, MKomics, MSclassifR, nlcv, Patterns, plfMA, promor, RANKS, RPPanalyzer, scBio, scRNAtools, scROSHI, ssizeRNA, statVisual, treediff, wrProteo suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocSet, BioNet, BioQC, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, CONSTANd, cydar, DAPAR, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, fgsea, fishpond, gage, geva, glmGamPoi, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, methylumi, MLP, npGSEA, NxtIRFcore, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, qmtools, qsvaR, randRotation, Rcade, recountmethylation, ribosomeProfilingQC, rtracklayer, signifinder, SpliceWiz, stageR, subSeq, SummarizedBenchmark, systemPipeR, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, GeuvadisTranscriptExpr, mammaPrintData, msigdb, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, AnnoProbe, aroma.affymetrix, canvasXpress, COCONUT, corncob, DGEobj.utils, dnet, hexbin, limorhyde, LPS, maGUI, NACHO, Platypus, protti, seqgendiff, Seurat, simphony, st, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 5 Package: limmaGUI Version: 1.74.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) Archs: x64 MD5sum: 714b5b58d0e19751378cc6d599aa71a1 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: RELEASE_3_16 git_last_commit: e9ad06b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/limmaGUI_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/limmaGUI_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/limmaGUI_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/limmaGUI_1.74.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 10 Package: LineagePulse Version: 1.18.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 MD5sum: e2083dfd56710c44ae5e602e5ca155d4 NeedsCompilation: no Title: Differential expression analysis and model fitting for single-cell RNA-seq data Description: LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays, SingleCell Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/LineagePulse/issues git_url: https://git.bioconductor.org/packages/LineagePulse git_branch: RELEASE_3_16 git_last_commit: 5e33489 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LineagePulse_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LineagePulse_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LineagePulse_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LineagePulse_1.18.0.tgz vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html vignetteTitles: LineagePulse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R dependencyCount: 87 Package: lineagespot Version: 1.2.0 Imports: VariantAnnotation, MatrixGenerics, SummarizedExperiment, data.table, stringr, httr, utils Suggests: BiocStyle, RefManageR, rmarkdown, knitr, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: a9cfcdf3ffb4afe7e02df0649eacf367 NeedsCompilation: no Title: Detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing Description: Lineagespot is a framework written in R, and aims to identify SARS-CoV-2 related mutations based on a single (or a list) of variant(s) file(s) (i.e., variant calling format). The method can facilitate the detection of SARS-CoV-2 lineages in wastewater samples using next generation sequencing, and attempts to infer the potential distribution of the SARS-CoV-2 lineages. biocViews: VariantDetection, VariantAnnotation, Sequencing Author: Nikolaos Pechlivanis [aut, cre] (), Maria Tsagiopoulou [aut], Maria Christina Maniou [aut], Anastasis Togkousidis [aut], Evangelia Mouchtaropoulou [aut], Taxiarchis Chassalevris [aut], Serafeim Chaintoutis [aut], Chrysostomos Dovas [aut], Maria Petala [aut], Margaritis Kostoglou [aut], Thodoris Karapantsios [aut], Stamatia Laidou [aut], Elisavet Vlachonikola [aut], Aspasia Orfanou [aut], Styliani-Christina Fragkouli [aut], Sofoklis Keisaris [aut], Anastasia Chatzidimitriou [aut], Agis Papadopoulos [aut], Nikolaos Papaioannou [aut], Anagnostis Argiriou [aut], Fotis E. Psomopoulos [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/lineagespot VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/lineagespot/issues git_url: https://git.bioconductor.org/packages/lineagespot git_branch: RELEASE_3_16 git_last_commit: e6fe834 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lineagespot_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lineagespot_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lineagespot_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lineagespot_1.2.0.tgz vignettes: vignettes/lineagespot/inst/doc/lineagespot.html vignetteTitles: lineagespot User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lineagespot/inst/doc/lineagespot.R dependencyCount: 99 Package: LinkHD Version: 1.12.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: d76e67fc5d9d72967bfa831e3417dca3 NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: RELEASE_3_16 git_last_commit: 6ece33c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LinkHD_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LinkHD_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LinkHD_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LinkHD_1.12.0.tgz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 136 Package: Linnorm Version: 2.22.2 Depends: R(>= 4.1.0) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, markdown, gplots, RColorBrewer, moments, testthat License: MIT + file LICENSE MD5sum: 666483ec9e91c2ce79583496d4499718 NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: RELEASE_3_16 git_last_commit: d611946 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/Linnorm_2.22.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/Linnorm_2.22.2.zip mac.binary.ver: bin/macosx/contrib/4.2/Linnorm_2.22.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Linnorm_2.22.2.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem dependencyCount: 67 Package: LinTInd Version: 1.2.0 Depends: R (>= 4.0), ggplot2, parallel, stats, S4Vectors Imports: data.tree, reshape2, networkD3, stringdist, purrr, ape, cowplot, ggnewscale, stringr, dplyr, rlist, pheatmap, Biostrings, IRanges, BiocGenerics(>= 0.36.1), ggtree Suggests: knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 975f273e819bc5adb8a76c3aa9f60b1c NeedsCompilation: no Title: Lineage tracing by indels Description: When we combine gene-editing technology and sequencing technology, we need to reconstruct a lineage tree from alleles generated and calculate the similarity between each pair of groups. FindIndel() and IndelForm() function will help you align each read to reference sequence and generate scar form strings respectively. IndelIdents() function will help you to define a scar form for each cell or read. IndelPlot() function will help you to visualize the distribution of deletion and insertion. TagProcess() function will help you to extract indels for each cell or read. TagDist() function will help you to calculate the similarity between each pair of groups across the indwells they contain. BuildTree() function will help you to reconstruct a tree. PlotTree() function will help you to visualize the tree. biocViews: SingleCell, CRISPR, Alignment Author: Luyue Wang [aut, cre], Bin Xiang [ctb], Hengxin Liu [ctb], Wu Wei [ths] Maintainer: Luyue Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinTInd git_branch: RELEASE_3_16 git_last_commit: d004cad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LinTInd_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LinTInd_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LinTInd_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LinTInd_1.2.0.tgz vignettes: vignettes/LinTInd/inst/doc/tutorial.html vignetteTitles: LinTInd - tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LinTInd/inst/doc/tutorial.R dependencyCount: 104 Package: lionessR Version: 1.12.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: f4d319b98b7645b6646d50c4a7cb4dd4 NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (), Ping-Han Hsieh [cre] () Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: RELEASE_3_16 git_last_commit: 5c18727 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lionessR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lionessR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lionessR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lionessR_1.12.0.tgz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 25 Package: lipidr Version: 2.12.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, iheatmapr, spelling, testthat License: MIT + file LICENSE MD5sum: 4098a17c293c4e67d0042820b4f76235 NeedsCompilation: no Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ git_url: https://git.bioconductor.org/packages/lipidr git_branch: RELEASE_3_16 git_last_commit: f09fb7a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lipidr_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lipidr_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lipidr_2.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lipidr_2.12.0.tgz vignettes: vignettes/lipidr/inst/doc/workflow.html vignetteTitles: lipidr_workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lipidr/inst/doc/workflow.R suggestsMe: rgoslin dependencyCount: 125 Package: LiquidAssociation Version: 1.52.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: d5482c9c1e1f6e15722f1ea614518cb6 NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: RELEASE_3_16 git_last_commit: d578601 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LiquidAssociation_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LiquidAssociation_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LiquidAssociation_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LiquidAssociation_1.52.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 66 Package: lisaClust Version: 1.6.3 Depends: R (>= 4.0) Imports: ggplot2, class, concaveman, grid, BiocParallel, spatstat.explore, spatstat.geom, BiocGenerics, S4Vectors, methods, spicyR, purrr, stats, data.table, dplyr, tidyr, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, pheatmap Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 7aa111af5d8c32b065bb484f65534b95 NeedsCompilation: no Title: lisaClust: Clustering of Local Indicators of Spatial Association Description: lisaClust provides a series of functions to identify and visualise regions of tissue where spatial associations between cell-types is similar. This package can be used to provide a high-level summary of cell-type colocalization in multiplexed imaging data that has been segmented at a single-cell resolution. biocViews: SingleCell, CellBasedAssays, Spatial Author: Ellis Patrick [aut, cre], Nicolas Canete [aut] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/lisaClust/issues git_url: https://git.bioconductor.org/packages/lisaClust git_branch: RELEASE_3_16 git_last_commit: e11a3fa git_last_commit_date: 2023-02-08 Date/Publication: 2023-02-09 source.ver: src/contrib/lisaClust_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/lisaClust_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/lisaClust_1.6.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lisaClust_1.6.3.tgz vignettes: vignettes/lisaClust/inst/doc/lisaClust.html vignetteTitles: "Inroduction to lisaClust" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lisaClust/inst/doc/lisaClust.R dependencyCount: 164 Package: lmdme Version: 1.40.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 4800afe9abb10aa3f0d1a42d719b06b4 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: RELEASE_3_16 git_last_commit: ce29beb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lmdme_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lmdme_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lmdme_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lmdme_1.40.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 8 Package: LOBSTAHS Version: 1.24.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: 04fe4db1bafa2094d2365d712327d078 NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Henry Holm [aut], Benjamin Van Mooy [aut], Daniel Lowenstein [aut] Maintainer: Henry Holm , Daniel Lowenstein , James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new git_url: https://git.bioconductor.org/packages/LOBSTAHS git_branch: RELEASE_3_16 git_last_commit: 80bee93 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LOBSTAHS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LOBSTAHS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LOBSTAHS_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LOBSTAHS_1.24.0.tgz vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R dependsOnMe: PtH2O2lipids dependencyCount: 133 Package: loci2path Version: 1.18.0 Depends: R (>= 3.5.0) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 14bba5d24c5bcdd0bf8efdffbf5577eb NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: RELEASE_3_16 git_last_commit: 0e8f94a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/loci2path_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/loci2path_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/loci2path_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/loci2path_1.18.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 45 Package: logicFS Version: 2.18.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 7d8fba23d13fb0b34f40fd6b474bd1d5 NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: RELEASE_3_16 git_last_commit: 98698e3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/logicFS_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/logicFS_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/logicFS_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/logicFS_2.18.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: logitT Version: 1.56.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) MD5sum: 4c6ae36074150dde94b426d46d1bdb00 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/logitT git_branch: RELEASE_3_16 git_last_commit: 9ee622f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/logitT_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/logitT_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/logitT_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/logitT_1.56.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R dependencyCount: 12 Package: LOLA Version: 1.28.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 Archs: x64 MD5sum: e087859471c8aa8ec992d1d1c11d6a1a NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: RELEASE_3_16 git_last_commit: 00d8699 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LOLA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LOLA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LOLA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LOLA_1.28.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, DeepBlueR, MAGAR, MIRA, ramr dependencyCount: 28 Package: LoomExperiment Version: 1.16.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, BiocIO Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, reticulate License: Artistic-2.0 MD5sum: da09acea19bde3869b0156d47b8d63f5 NeedsCompilation: no Title: LoomExperiment container Description: The LoomExperiment package provide a means to easily convert the Bioconductor "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: RELEASE_3_16 git_last_commit: 3fd5f92 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LoomExperiment_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LoomExperiment_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LoomExperiment_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LoomExperiment_1.16.0.tgz vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html vignetteTitles: An introduction to the LoomExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R dependsOnMe: OSCA.intro suggestsMe: hca dependencyCount: 39 Package: LowMACA Version: 1.28.0 Depends: R (>= 2.10) Imports: cBioPortalData, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr, grid, gridBase, plyr Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 00d852771a79338dfdd5f41318672af1 NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Giorgio Melloni , Stefano de Pretis Maintainer: Giorgio Melloni , Stefano de Pretis SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LowMACA git_branch: RELEASE_3_16 git_last_commit: 8b8fea5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LowMACA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LowMACA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LowMACA_1.28.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.html vignetteTitles: Bioconductor style for HTML documents hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R dependencyCount: 182 Package: LPE Version: 1.72.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: d9444a8cb6eb53f557c137770ce62d6b NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: RELEASE_3_16 git_last_commit: 7f90772 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LPE_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LPE_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LPE_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LPE_1.72.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray dependencyCount: 1 Package: LPEadj Version: 1.58.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 584ded64033e6f6027a17014bcc2c48c NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie git_url: https://git.bioconductor.org/packages/LPEadj git_branch: RELEASE_3_16 git_last_commit: 18d55e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LPEadj_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LPEadj_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LPEadj_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LPEadj_1.58.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R dependencyCount: 2 Package: lpNet Version: 2.30.0 Depends: lpSolve License: Artistic License 2.0 MD5sum: 4a2ede12ed4410b3e3e3f162b91f6b02 NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: RELEASE_3_16 git_last_commit: 8110d3a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lpNet_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lpNet_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lpNet_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lpNet_2.30.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 1 Package: lpsymphony Version: 1.26.3 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL MD5sum: 625446c976c475cc3d7e4f203f6ff0d0 NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb] Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: RELEASE_3_16 git_last_commit: 0bda503 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/lpsymphony_1.26.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/lpsymphony_1.26.3.zip mac.binary.ver: bin/macosx/contrib/4.2/lpsymphony_1.26.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lpsymphony_1.26.3.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW, Maaslin2 suggestsMe: oppr, prioritizr dependencyCount: 0 Package: LRBaseDbi Version: 2.8.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: testthat, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: 480f81a7f4837f195832f4c2ec6e716a NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: RELEASE_3_16 git_last_commit: c40667e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LRBaseDbi_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LRBaseDbi_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LRBaseDbi_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LRBaseDbi_2.8.0.tgz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R suggestsMe: scTensor dependencyCount: 46 Package: LRcell Version: 1.6.0 Depends: R (>= 4.1), ExperimentHub, AnnotationHub Imports: BiocParallel, dplyr, ggplot2, ggrepel, magrittr, stats, utils Suggests: LRcellTypeMarkers, BiocStyle, knitr, rmarkdown, roxygen2, testthat License: MIT + file LICENSE MD5sum: b698cae5f254406d6f498bbcd32e08fa NeedsCompilation: no Title: Differential cell type change analysis using Logistic/linear Regression Description: The goal of LRcell is to identify specific sub-cell types that drives the changes observed in a bulk RNA-seq differential gene expression experiment. To achieve this, LRcell utilizes sets of cell marker genes acquired from single-cell RNA-sequencing (scRNA-seq) as indicators for various cell types in the tissue of interest. Next, for each cell type, using its marker genes as indicators, we apply Logistic Regression on the complete set of genes with differential expression p-values to calculate a cell-type significance p-value. Finally, these p-values are compared to predict which one(s) are likely to be responsible for the differential gene expression pattern observed in the bulk RNA-seq experiments. LRcell is inspired by the LRpath[@sartor2009lrpath] algorithm developed by Sartor et al., originally designed for pathway/gene set enrichment analysis. LRcell contains three major components: LRcell analysis, plot generation and marker gene selection. All modules in this package are written in R. This package also provides marker genes in the Prefrontal Cortex (pFC) human brain region, human PBMC and nine mouse brain regions (Frontal Cortex, Cerebellum, Globus Pallidus, Hippocampus, Entopeduncular, Posterior Cortex, Striatum, Substantia Nigra and Thalamus). biocViews: SingleCell, GeneSetEnrichment, Sequencing, Regression, GeneExpression, DifferentialExpression Author: Wenjing Ma [cre, aut] () Maintainer: Wenjing Ma VignetteBuilder: knitr BugReports: https://github.com/marvinquiet/LRcell/issues git_url: https://git.bioconductor.org/packages/LRcell git_branch: RELEASE_3_16 git_last_commit: dd108d3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LRcell_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LRcell_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LRcell_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LRcell_1.6.0.tgz vignettes: vignettes/LRcell/inst/doc/LRcell-vignette.html vignetteTitles: LRcell Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LRcell/inst/doc/LRcell-vignette.R suggestsMe: LRcellTypeMarkers dependencyCount: 123 Package: lumi Version: 2.50.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: 75798958630e07dd0a3553e8e95156a9 NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Lei Huang git_url: https://git.bioconductor.org/packages/lumi git_branch: RELEASE_3_16 git_last_commit: 8711b77 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/lumi_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/lumi_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/lumi_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/lumi_2.50.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, MAQCsubsetILM, mvoutData importsMe: arrayMvout, ffpe, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre, maGUI dependencyCount: 160 Package: LymphoSeq Version: 1.26.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: 0023046aac324a818c3aa01ce1e7c032 NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: RELEASE_3_16 git_last_commit: 2aa5858 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/LymphoSeq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/LymphoSeq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/LymphoSeq_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/LymphoSeq_1.26.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 92 Package: M3C Version: 1.20.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: ea89b608a44d9edf0e26e583f9abb42d NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: RELEASE_3_16 git_last_commit: 9fec8cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/M3C_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/M3C_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/M3C_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/M3C_1.20.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R importsMe: HCV, lilikoi suggestsMe: metabolomicsR, parameters dependencyCount: 60 Package: M3Drop Version: 1.24.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods Suggests: ROCR, knitr, M3DExampleData, scater, SingleCellExperiment, monocle, Seurat, Biobase License: GPL (>=2) MD5sum: 6cc8e23c85dfa49d27da4b66e21aabfe NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a Michaelis-Menten model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: RELEASE_3_16 git_last_commit: 4e29540 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/M3Drop_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/M3Drop_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/M3Drop_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/M3Drop_1.24.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 108 Package: m6Aboost Version: 1.4.0 Depends: S4Vectors, adabag, GenomicRanges, R (>= 4.1) Imports: dplyr, rtracklayer, BSgenome, Biostrings, utils, methods, IRanges, ExperimentHub Suggests: knitr, rmarkdown, bookdown, testthat, BiocStyle, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 MD5sum: 7ea5d5c6ff5998fb5276e41ae7df09b1 NeedsCompilation: no Title: m6Aboost Description: This package can help user to run the m6Aboost model on their own miCLIP2 data. The package includes functions to assign the read counts and get the features to run the m6Aboost model. The miCLIP2 data should be stored in a GRanges object. More details can be found in the vignette. biocViews: Sequencing, Epigenetics, Genetics, ExperimentHubSoftware Author: You Zhou [aut, cre] (), Kathi Zarnack [aut] () Maintainer: You Zhou URL: https://github.com/ZarnackGroup/m6Aboost VignetteBuilder: knitr BugReports: https://github.com/ZarnackGroup/m6Aboost/issues git_url: https://git.bioconductor.org/packages/m6Aboost git_branch: RELEASE_3_16 git_last_commit: 3b70603 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/m6Aboost_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/m6Aboost_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/m6Aboost_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/m6Aboost_1.4.0.tgz vignettes: vignettes/m6Aboost/inst/doc/m6AboosVignettes.html vignetteTitles: m6Aboost Vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/m6Aboost/inst/doc/m6AboosVignettes.R dependencyCount: 174 Package: maanova Version: 1.68.2 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) MD5sum: 1c6c588f351f987b5cb1fb20a3a8a086 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill git_url: https://git.bioconductor.org/packages/maanova git_branch: RELEASE_3_16 git_last_commit: c576b38 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/maanova_1.68.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/maanova_1.68.2.zip mac.binary.ver: bin/macosx/contrib/4.2/maanova_1.68.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maanova_1.68.2.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 7 Package: Maaslin2 Version: 1.12.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl, lme4 Suggests: knitr, testthat (>= 2.1.0), rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 5382ad81bbed778dd4ed9d62d8187647 NeedsCompilation: no Title: "Multivariable Association Discovery in Population-scale Meta-omics Studies" Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut, cre] Maintainer: Lauren McIver URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: RELEASE_3_16 git_last_commit: db35cac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Maaslin2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Maaslin2_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Maaslin2_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Maaslin2_1.12.0.tgz vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html vignetteTitles: MaAsLin2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R importsMe: Macarron, MMUPHin, ggpicrust2 dependencyCount: 137 Package: Macarron Version: 1.2.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: BiocParallel, DelayedArray, WGCNA, ff, data.table, dynamicTreeCut, Maaslin2, plyr, stats, psych, xml2, RCurl, RJSONIO, logging, methods, utils Suggests: knitr, BiocStyle, optparse, testthat (>= 2.1.0), rmarkdown, markdown License: MIT + file LICENSE MD5sum: 349a19f62c6681aaeade9cde514a50bc NeedsCompilation: no Title: Prioritization of potentially bioactive metabolic features from epidemiological and environmental metabolomics datasets Description: Macarron is a workflow for the prioritization of potentially bioactive metabolites from metabolomics experiments. Prioritization integrates strengths of evidences of bioactivity such as covariation with a known metabolite, abundance relative to a known metabolite and association with an environmental or phenotypic indicator of bioactivity. Broadly, the workflow consists of stratified clustering of metabolic spectral features which co-vary in abundance in a condition, transfer of functional annotations, estimation of relative abundance and differential abundance analysis to identify associations between features and phenotype/condition. biocViews: Sequencing, Metabolomics, Coverage, FunctionalPrediction, Clustering Author: Amrisha Bhosle [aut], Ludwig Geistlinger [aut], Sagun Maharjan [aut, cre] Maintainer: Sagun Maharjan URL: http://huttenhower.sph.harvard.edu/macarron VignetteBuilder: knitr BugReports: https://forum.biobakery.org/c/microbial-community-profiling/macarron git_url: https://git.bioconductor.org/packages/Macarron git_branch: RELEASE_3_16 git_last_commit: 5d638db git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Macarron_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Macarron_1.1.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Macarron_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Macarron_1.2.0.tgz vignettes: vignettes/Macarron/inst/doc/MACARRoN.html vignetteTitles: Macarron hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Macarron/inst/doc/MACARRoN.R dependencyCount: 208 Package: macat Version: 1.72.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: 1096a9c3d1bf2b0ad6e691c1dd8aaf37 NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling git_url: https://git.bioconductor.org/packages/macat git_branch: RELEASE_3_16 git_last_commit: 388a02b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/macat_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/macat_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/macat_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/macat_1.72.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R dependencyCount: 49 Package: maCorrPlot Version: 1.68.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: a63b315e6d01de109ddf01556211ae69 NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: RELEASE_3_16 git_last_commit: 5efe654 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maCorrPlot_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maCorrPlot_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maCorrPlot_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maCorrPlot_1.68.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACSQuantifyR Version: 1.12.0 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: c2c457fbf33b0ddab74b1507899fa3ac NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: RELEASE_3_16 git_last_commit: fd077c9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MACSQuantifyR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MACSQuantifyR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MACSQuantifyR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MACSQuantifyR_1.12.0.tgz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 86 Package: MACSr Version: 1.6.0 Depends: R (>= 4.1.0) Imports: utils, reticulate, S4Vectors, methods, basilisk, ExperimentHub, AnnotationHub Suggests: testthat, knitr, rmarkdown, BiocStyle, MACSdata License: BSD_3_clause + file LICENSE MD5sum: b8631d20e9b2bb9773294433427d871a NeedsCompilation: no Title: MACS: Model-based Analysis for ChIP-Seq Description: The Model-based Analysis of ChIP-Seq (MACS) is a widely used toolkit for identifying transcript factor binding sites. This package is an R wrapper of the lastest MACS3. biocViews: Software, ChIPSeq, ATACSeq, ImmunoOncology Author: Qiang Hu [aut, cre] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSr git_branch: RELEASE_3_16 git_last_commit: 39839d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MACSr_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/MACSr_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MACSr_1.6.0.tgz vignettes: vignettes/MACSr/inst/doc/MACSr.html vignetteTitles: MACSr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MACSr/inst/doc/MACSr.R dependencyCount: 107 Package: made4 Version: 1.72.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 73dcfb9909aeec72eec4682335e52eed NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4. biocViews: Clustering, Classification, DimensionReduction, PrincipalComponent,Transcriptomics, MultipleComparison, GeneExpression, Sequencing, Microarray Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: RELEASE_3_16 git_last_commit: 4f008b4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/made4_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/made4_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/made4_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/made4_1.72.0.tgz vignettes: vignettes/made4/inst/doc/introduction.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R importsMe: deco, omicade4 dependencyCount: 37 Package: MADSEQ Version: 1.24.0 Depends: R (>= 3.5.0), rjags (>= 4.6) Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) MD5sum: 5ab12d023da164c0754fe94bf4bad8db NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues git_url: https://git.bioconductor.org/packages/MADSEQ git_branch: RELEASE_3_16 git_last_commit: 698f059 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MADSEQ_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MADSEQ_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MADSEQ_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MADSEQ_1.24.0.tgz vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html vignetteTitles: R Package MADSEQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R dependencyCount: 116 Package: maftools Version: 2.14.0 Depends: R (>= 3.3) Imports: data.table, grDevices, methods, RColorBrewer, Rhtslib, survival, DNAcopy LinkingTo: Rhtslib, zlibbioc Suggests: berryFunctions, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomicRanges, IRanges, knitr, mclust, MultiAssayExperiment, NMF, R.utils, RaggedExperiment, rmarkdown, S4Vectors, pheatmap, curl License: MIT + file LICENSE MD5sum: c96cd10b50d6f0ed8296c5f0f93a5052 NeedsCompilation: yes Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] () Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: RELEASE_3_16 git_last_commit: 50c4d4c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maftools_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maftools_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maftools_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maftools_2.14.0.tgz vignettes: vignettes/maftools/inst/doc/cancer_hotspots.html, vignettes/maftools/inst/doc/cnv_analysis.html, vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 03: Cancer report, 04: Copy number analysis, 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/cancer_hotspots.R, vignettes/maftools/inst/doc/cnv_analysis.R, vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R importsMe: CIMICE, katdetectr, musicatk, TCGAbiolinksGUI, oncoPredict, pathwayTMB, PMAPscore, Rediscover, sigminer, SMDIC suggestsMe: GenomicDataCommons, MultiAssayExperiment, survtype, TCGAbiolinks dependencyCount: 15 Package: MAGAR Version: 1.6.0 Depends: R (>= 4.1), HDF5Array, RnBeads, snpStats, crlmm Imports: doParallel, igraph, bigstatsr, rjson, plyr, data.table, UpSetR, reshape2, jsonlite, methods, ff, argparse, impute, RnBeads.hg19, utils, stats Suggests: gridExtra, VennDiagram, qqman, LOLA, RUnit, rmutil, rmarkdown, JASPAR2018, TFBSTools, seqLogo, knitr, devtools, BiocGenerics, BiocManager License: GPL-3 Archs: x64 MD5sum: d3bf94d2015c392cb7860f4e82b1b72d NeedsCompilation: no Title: MAGAR: R-package to compute methylation Quantitative Trait Loci (methQTL) from DNA methylation and genotyping data Description: "Methylation-Aware Genotype Association in R" (MAGAR) computes methQTL from DNA methylation and genotyping data from matched samples. MAGAR uses a linear modeling stragety to call CpGs/SNPs that are methQTLs. MAGAR accounts for the local correlation structure of CpGs. biocViews: Regression, Epigenetics, DNAMethylation, SNP, GeneticVariability, MethylationArray, Microarray, CpGIsland, MethylSeq, Sequencing, mRNAMicroarray, Preprocessing, CopyNumberVariation, TwoChannel, ImmunoOncology, DifferentialMethylation, BatchEffect, QualityControl, DataImport, Network, Clustering, GraphAndNetwork Author: Michael Scherer [cre, aut] () Maintainer: Michael Scherer URL: https://github.com/MPIIComputationalEpigenetics/MAGAR VignetteBuilder: knitr BugReports: https://github.com/MPIIComputationalEpigenetics/MAGAR/issues git_url: https://git.bioconductor.org/packages/MAGAR git_branch: RELEASE_3_16 git_last_commit: 0a820cb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MAGAR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAGAR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAGAR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MAGAR_1.6.0.tgz vignettes: vignettes/MAGAR/inst/doc/MAGAR.html vignetteTitles: MAGAR: Methylation-Aware Genotype Association in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGAR/inst/doc/MAGAR.R dependencyCount: 195 Package: MAGeCKFlute Version: 2.2.0 Depends: R (>= 4.1) Imports: Biobase, gridExtra, ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils, DOSE, clusterProfiler, pathview, enrichplot, msigdbr, depmap Suggests: biomaRt, BiocStyle, dendextend, graphics, knitr, pheatmap, png, scales, sva, BiocManager License: GPL (>=3) MD5sum: 603f82fd64bae38b73a12bf5ea8286b0 NeedsCompilation: no Title: Integrative Analysis Pipeline for Pooled CRISPR Functional Genetic Screens Description: CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. biocViews: FunctionalGenomics, CRISPR, PooledScreens, QualityControl, Normalization, GeneSetEnrichment, Pathways, Visualization, GeneTarget, KEGG Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu Maintainer: Wubing Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MAGeCKFlute git_branch: RELEASE_3_16 git_last_commit: 7a0cfe1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MAGeCKFlute_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAGeCKFlute_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAGeCKFlute_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MAGeCKFlute_2.2.0.tgz vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.html, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html vignetteTitles: MAGeCKFlute_enrichment.Rmd, MAGeCKFlute.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.R, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R dependencyCount: 172 Package: magrene Version: 1.0.0 Depends: R (>= 4.2.0) Imports: utils, stats, BiocParallel Suggests: BiocStyle, covr, knitr, rmarkdown, ggplot2, sessioninfo, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5ff36ac0d223dfb2748c00675385e43a NeedsCompilation: no Title: Motif Analysis In Gene Regulatory Networks Description: magrene allows the identification and analysis of graph motifs in (duplicated) gene regulatory networks (GRNs), including lambda, V, PPI V, delta, and bifan motifs. GRNs can be tested for motif enrichment by comparing motif frequencies to a null distribution generated from degree-preserving simulated GRNs. Motif frequencies can be analyzed in the context of gene duplications to explore the impact of small-scale and whole-genome duplications on gene regulatory networks. Finally, users can calculate interaction similarity for gene pairs based on the Sorensen-Dice similarity index. biocViews: Software, MotifDiscovery, NetworkEnrichment, SystemsBiology, GraphAndNetwork Author: Fabrício Almeida-Silva [aut, cre] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/magrene VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/magrene git_url: https://git.bioconductor.org/packages/magrene git_branch: RELEASE_3_16 git_last_commit: a080a0d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/magrene_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/magrene_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/magrene_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/magrene_1.0.0.tgz vignettes: vignettes/magrene/inst/doc/magrene.html vignetteTitles: Introduction to magrene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/magrene/inst/doc/magrene.R dependencyCount: 13 Package: MAI Version: 1.4.0 Depends: R (>= 3.5.0) Imports: caret, parallel, doParallel, foreach, e1071, future.apply, future, missForest, pcaMethods, tidyverse, stats, utils, methods, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: afff79c1e82563cfafefa83b365508a3 NeedsCompilation: no Title: Mechanism-Aware Imputation Description: A two-step approach to imputing missing data in metabolomics. Step 1 uses a random forest classifier to classify missing values as either Missing Completely at Random/Missing At Random (MCAR/MAR) or Missing Not At Random (MNAR). MCAR/MAR are combined because it is often difficult to distinguish these two missing types in metabolomics data. Step 2 imputes the missing values based on the classified missing mechanisms, using the appropriate imputation algorithms. Imputation algorithms tested and available for MCAR/MAR include Bayesian Principal Component Analysis (BPCA), Multiple Imputation No-Skip K-Nearest Neighbors (Multi_nsKNN), and Random Forest. Imputation algorithms tested and available for MNAR include nsKNN and a single imputation approach for imputation of metabolites where left-censoring is present. biocViews: Software, Metabolomics, StatisticalMethod, Classification Author: Jonathan Dekermanjian [aut, cre], Elin Shaddox [aut], Debmalya Nandy [aut], Debashis Ghosh [aut], Katerina Kechris [aut] Maintainer: Jonathan Dekermanjian URL: https://github.com/KechrisLab/MAI VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MAI/issues git_url: https://git.bioconductor.org/packages/MAI git_branch: RELEASE_3_16 git_last_commit: 8e3eba9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MAI_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAI_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAI_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MAI_1.4.0.tgz vignettes: vignettes/MAI/inst/doc/UsingMAI.html vignetteTitles: Utilizing Mechanism-Aware Imputation (MAI) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MAI/inst/doc/UsingMAI.R dependencyCount: 173 Package: maigesPack Version: 1.62.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) MD5sum: 97aedbad25ae29db86a1c2f6cc9a07c5 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ git_url: https://git.bioconductor.org/packages/maigesPack git_branch: RELEASE_3_16 git_last_commit: d97a6b1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maigesPack_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maigesPack_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maigesPack_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maigesPack_1.62.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R dependencyCount: 12 Package: MAIT Version: 1.32.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 Archs: x64 MD5sum: b9bcf055609035513aacdca252e98bb9 NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos git_url: https://git.bioconductor.org/packages/MAIT git_branch: RELEASE_3_16 git_last_commit: ccb19be git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MAIT_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAIT_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MAIT_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MAIT_1.32.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R dependencyCount: 211 Package: makecdfenv Version: 1.74.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) MD5sum: ec90875d67856eb2042bec906851da9c NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: RELEASE_3_16 git_last_commit: 412affc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/makecdfenv_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/makecdfenv_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/makecdfenv_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/makecdfenv_1.74.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 12 Package: MANOR Version: 1.70.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 Archs: x64 MD5sum: 86328e070a9541927ab5d8072eb79f6f NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation, Normalization Author: Pierre Neuvial , Philippe Hupé Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html VignetteBuilder: knitr BugReports: https://github.com/pneuvial/MANOR/issues git_url: https://git.bioconductor.org/packages/MANOR git_branch: RELEASE_3_16 git_last_commit: ef93a82 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MANOR_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MANOR_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MANOR_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MANOR_1.70.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.html vignetteTitles: Overview of the MANOR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: MantelCorr Version: 1.68.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) Archs: x64 MD5sum: 6967eb1ffa9dd7ab516ad3bdac2d0403 NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: RELEASE_3_16 git_last_commit: 4d299ef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MantelCorr_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MantelCorr_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MantelCorr_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MantelCorr_1.68.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: mAPKL Version: 1.28.0 Depends: R (>= 3.6.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 8572304372e73ea0910184b2342910d0 NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mAPKL git_branch: RELEASE_3_16 git_last_commit: 2266b1f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mAPKL_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mAPKL_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mAPKL_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mAPKL_1.28.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R dependencyCount: 70 Package: maPredictDSC Version: 1.36.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: bb396b8e675728dcee062276413210fc NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: RELEASE_3_16 git_last_commit: 3c557a2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maPredictDSC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maPredictDSC_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maPredictDSC_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maPredictDSC_1.36.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 136 Package: mapscape Version: 1.22.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 846c1f7cc1df376b746250585144331c NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: RELEASE_3_16 git_last_commit: 1a4f08e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mapscape_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mapscape_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mapscape_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mapscape_1.22.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 37 Package: marr Version: 1.8.0 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) MD5sum: 82706a852d843ae2106d68b822aa844a NeedsCompilation: yes Title: Maximum rank reproducibility Description: marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs. biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq, ChIPSeq Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: RELEASE_3_16 git_last_commit: 98daaf3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/marr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/marr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/marr_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/marr_1.8.0.tgz vignettes: vignettes/marr/inst/doc/MarrVignette.html vignetteTitles: The marr user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/marr/inst/doc/MarrVignette.R dependencyCount: 56 Package: marray Version: 1.76.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL Archs: x64 MD5sum: 409204c0f7a5df93537c63e9d7018d4d NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: RELEASE_3_16 git_last_commit: 88cb0fd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/marray_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/marray_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/marray_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/marray_1.76.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, methylPipe, MSstats, MSstatsShiny, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin dependencyCount: 6 Package: martini Version: 1.18.1 Depends: R (>= 4.0) Imports: igraph (>= 1.0.1), Matrix, memoise (>= 2.0.0), methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), stats, utils, LinkingTo: Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), circlize (>= 0.4.11), STRINGdb (>= 2.2.0), httr (>= 1.2.1), IRanges (>= 2.8.2), S4Vectors (>= 0.12.2), knitr, testthat, readr, rmarkdown License: GPL-3 MD5sum: 3f1d00115ff5804bdbbbbe47f0dbc872 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre] (), Chloe-Agathe Azencott [aut] () Maintainer: Hector Climente-Gonzalez URL: https://github.com/hclimente/martini VignetteBuilder: knitr BugReports: https://github.com/hclimente/martini/issues git_url: https://git.bioconductor.org/packages/martini git_branch: RELEASE_3_16 git_last_commit: cc9025d git_last_commit_date: 2023-01-11 Date/Publication: 2023-01-11 source.ver: src/contrib/martini_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/martini_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/martini_1.18.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/martini_1.18.1.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 23 Package: maser Version: 1.16.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE Archs: x64 MD5sum: 091ac65e236509a216e877ff347c6055 NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: RELEASE_3_16 git_last_commit: d9a3e41 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maser_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maser_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maser_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maser_1.16.0.tgz vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 159 Package: maSigPro Version: 1.70.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: 805963ced65e6a497b27e0b0fbfb2bf3 NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa and Maria Jose Nueda Maintainer: Maria Jose Nueda git_url: https://git.bioconductor.org/packages/maSigPro git_branch: RELEASE_3_16 git_last_commit: 9dbfdfe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maSigPro_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maSigPro_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maSigPro_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maSigPro_1.70.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: maskBAD Version: 1.42.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: a07de67df0ce205e7a276fc12709d0b6 NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: RELEASE_3_16 git_last_commit: adbcb8a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/maskBAD_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/maskBAD_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/maskBAD_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/maskBAD_1.42.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 25 Package: MassArray Version: 1.50.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: ca918721022f140429588476a9262781 NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: RELEASE_3_16 git_last_commit: 19928cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MassArray_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MassArray_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MassArray_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MassArray_1.50.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.34.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: c109b811e70aba521dd4e28825ea0575 NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: RELEASE_3_16 git_last_commit: 579e529 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/massiR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/massiR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/massiR_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/massiR_1.34.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 14 Package: MassSpecWavelet Version: 1.64.1 Suggests: signal, waveslim, BiocStyle, knitr, rmarkdown, RUnit, bench License: LGPL (>= 2) MD5sum: 9d63ad876e78f55716e99c9844c35607 NeedsCompilation: yes Title: Peak Detection for Mass Spectrometry data using wavelet-based algorithms Description: Peak Detection in Mass Spectrometry data is one of the important preprocessing steps. The performance of peak detection affects subsequent processes, including protein identification, profile alignment and biomarker identification. Using Continuous Wavelet Transform (CWT), this package provides a reliable algorithm for peak detection that does not require any type of smoothing or previous baseline correction method, providing more consistent results for different spectra. See ) Maintainer: Sergio Oller Moreno URL: https://github.com/zeehio/MassSpecWavelet VignetteBuilder: knitr BugReports: http://github.com/zeehio/MassSpecWavelet/issues git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_16 git_last_commit: 3d555a8 git_last_commit_date: 2023-01-30 Date/Publication: 2023-01-30 source.ver: src/contrib/MassSpecWavelet_1.64.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MassSpecWavelet_1.64.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MassSpecWavelet_1.64.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MassSpecWavelet_1.64.1.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.html, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.html vignetteTitles: Finding local maxima, Using the MassSpecWavelet package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/FindingLocalMaxima.R, vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, Rnmr1D, speaq suggestsMe: downlit dependencyCount: 0 Package: MAST Version: 1.24.1 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, Matrix, HDF5Array, zinbwave, dplyr License: GPL(>= 2) Archs: x64 MD5sum: 10506e9bc335f3fd25802ca5aabce102 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: RELEASE_3_16 git_last_commit: 4f17987 git_last_commit_date: 2023-01-23 Date/Publication: 2023-01-23 source.ver: src/contrib/MAST_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MAST_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MAST_1.24.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MAST_1.24.1.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R dependsOnMe: POWSC importsMe: benchdamic, celaref, singleCellTK, CSCDRNA, DWLS, PALMO suggestsMe: clusterExperiment, EWCE, MARVEL, Seurat dependencyCount: 65 Package: matchBox Version: 1.40.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 417da5756e7ae1ef6c7ec7d91bd6ce97 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: RELEASE_3_16 git_last_commit: 24835c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/matchBox_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/matchBox_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/matchBox_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/matchBox_1.40.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixGenerics Version: 1.10.0 Depends: matrixStats (>= 0.60.1) Imports: methods Suggests: sparseMatrixStats, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0), Matrix License: Artistic-2.0 MD5sum: d699dd28635bdee023a78d352c651bf2 NeedsCompilation: no Title: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects Description: S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. biocViews: Infrastructure, Software Author: Constantin Ahlmann-Eltze [aut] (), Peter Hickey [aut, cre] (), Hervé Pagès [aut] Maintainer: Peter Hickey URL: https://bioconductor.org/packages/MatrixGenerics BugReports: https://github.com/Bioconductor/MatrixGenerics/issues git_url: https://git.bioconductor.org/packages/MatrixGenerics git_branch: RELEASE_3_16 git_last_commit: 6d9d907 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MatrixGenerics_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MatrixGenerics_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MatrixGenerics_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MatrixGenerics_1.10.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: CoreGx, crisprDesign, escape, glmGamPoi, imcRtools, lineagespot, miaSim, MinimumDistance, PDATK, RaggedExperiment, scone, scPCA, TENxIO, tLOH, transformGamPoi, VanillaICE suggestsMe: MungeSumstats dependencyCount: 2 Package: MatrixQCvis Version: 1.6.1 Depends: SummarizedExperiment (>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) Imports: ComplexHeatmap (>= 2.7.9), dplyr (>= 1.0.5), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>= 1.5.3), impute (>= 1.65.0), imputeLCMD (>= 2.0), limma (>= 3.47.12), MASS (>= 7.3-58.1), methods (>= 4.1.0), pcaMethods (>= 1.83.0), proDA (>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vsn (>= 3.59.1) Suggests: BiocGenerics (>= 0.37.4), BiocStyle (>= 2.19.2), hexbin (>= 1.28.2), knitr (>= 1.33), testthat (>= 3.0.2) License: GPL (>= 3) MD5sum: 04ee2f8908e11bd87ebe033bb5c6d1ff NeedsCompilation: no Title: Shiny-based interactive data-quality exploration for omics data Description: Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object. biocViews: Visualization, ShinyApps, GUI, QualityControl, DimensionReduction, Metabolomics, Proteomics, Transcriptomics Author: Thomas Naake [aut, cre] (), Wolfgang Huber [aut] () Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MatrixQCvis git_branch: RELEASE_3_16 git_last_commit: e6d299a git_last_commit_date: 2022-11-08 Date/Publication: 2022-11-08 source.ver: src/contrib/MatrixQCvis_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MatrixQCvis_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MatrixQCvis_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MatrixQCvis_1.6.1.tgz vignettes: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.html vignetteTitles: Shiny-based interactive data quality exploration of omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixQCvis/inst/doc/MatrixQCvis.R dependencyCount: 157 Package: MatrixRider Version: 1.30.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 MD5sum: 2f7739a0013d538ede275004afc6b7ff NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: RELEASE_3_16 git_last_commit: 51fd941 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MatrixRider_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MatrixRider_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MatrixRider_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MatrixRider_1.30.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 121 Package: matter Version: 2.0.1 Depends: R (>= 3.5), BiocParallel, Matrix, methods, stats Imports: BiocGenerics, ProtGenerics, digest, irlba, biglm, utils Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 Archs: x64 MD5sum: b72133dd10854e4847437c74723aa77f NeedsCompilation: yes Title: A framework for rapid prototyping with file-based data structures Description: Memory-efficient reading, writing, and manipulation of structured binary data as file-based vectors, matrices, arrays, lists, and data frames. biocViews: Infrastructure, DataRepresentation Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/matter git_branch: RELEASE_3_16 git_last_commit: 1ff1962 git_last_commit_date: 2022-11-15 Date/Publication: 2022-11-16 source.ver: src/contrib/matter_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/matter_2.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/matter_2.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/matter_2.0.1.tgz vignettes: vignettes/matter/inst/doc/matter-2-guide.html vignetteTitles: 1. Matter 2: User guide for flexible out-of-memory data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-2-guide.R importsMe: Cardinal dependencyCount: 24 Package: MBAmethyl Version: 1.32.0 Depends: R (>= 2.15) License: Artistic-2.0 Archs: x64 MD5sum: 626fab9ef64ab97d9bb3a54a303b1c59 NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: RELEASE_3_16 git_last_commit: d7a6216 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MBAmethyl_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBAmethyl_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBAmethyl_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MBAmethyl_1.32.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.32.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 62c450d62d07e35671f50afaff3e481b NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: RELEASE_3_16 git_last_commit: 3047cc7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MBASED_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBASED_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBASED_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MBASED_1.32.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 36 Package: MBCB Version: 1.52.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: de89ddcb10aec57de534a5b9c077c1b3 NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Jeff Allen URL: http://www.utsouthwestern.edu git_url: https://git.bioconductor.org/packages/MBCB git_branch: RELEASE_3_16 git_last_commit: 3045b95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MBCB_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBCB_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBCB_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MBCB_1.52.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: MBECS Version: 1.2.0 Depends: R (>= 4.1) Imports: methods, magrittr, phyloseq, limma, lme4, lmerTest, pheatmap, rmarkdown, cluster, dplyr, ggplot2, gridExtra, ruv, sva, tibble, tidyr, vegan, stats, utils, Matrix Suggests: knitr, markdown, BiocStyle, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: b82851d33548f8a63ece200e01a81b9c NeedsCompilation: no Title: Evaluation and correction of batch effects in microbiome data-sets Description: The Microbiome Batch Effect Correction Suite (MBECS) provides a set of functions to evaluate and mitigate unwated noise due to processing in batches. To that end it incorporates a host of batch correcting algorithms (BECA) from various packages. In addition it offers a correction and reporting pipeline that provides a preliminary look at the characteristics of a data-set before and after correcting for batch effects. biocViews: BatchEffect, Microbiome, ReportWriting, Visualization, Normalization, QualityControl Author: Michael Olbrich [aut, cre] () Maintainer: Michael Olbrich URL: https://github.com/rmolbrich/MBECS VignetteBuilder: knitr BugReports: https://github.com/rmolbrich/MBECS/issues/new git_url: https://git.bioconductor.org/packages/MBECS git_branch: RELEASE_3_16 git_last_commit: 58656c6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MBECS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBECS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBECS_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MBECS_1.2.0.tgz vignettes: vignettes/MBECS/inst/doc/mbecs_vignette.html vignetteTitles: MBECS introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBECS/inst/doc/mbecs_vignette.R dependencyCount: 155 Package: mbkmeans Version: 1.14.0 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, bluster, knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: f349be5a250f06fc5b8a5b1aabd354b8 NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: RELEASE_3_16 git_last_commit: c048655 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mbkmeans_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mbkmeans_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mbkmeans_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mbkmeans_1.14.0.tgz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R dependsOnMe: OSCA.basic importsMe: clusterExperiment suggestsMe: bluster, scDblFinder dependencyCount: 87 Package: mbOmic Version: 1.2.0 Depends: R (>= 4.1.0) Imports: parallel, doParallel, psych, WGCNA, data.table, igraph, visNetwork, cluster, clusterSim, methods, graphics, stats Suggests: testthat (>= 3.0.0), knitr, rmarkdown, devtools, impute License: Artistic-2.0 Archs: x64 MD5sum: 4e4e2dff7c987f29b65a42415193621e NeedsCompilation: no Title: Integrative analysis of the microbiome and metabolome Description: The mbOmic package contains a set of analysis functions for microbiomics and metabolomics data, designed to analyze the inter-omic correlation between microbiology and metabolites. Integrative analysis of the microbiome and metabolome is the aim of mbOmic. Additionally, the identification of enterotype using the gut microbiota abundance is preliminaryimplemented. biocViews: Metabolomics, Microbiome, Network Author: Congcong Gong [aut, cre] () Maintainer: Congcong Gong URL: https://github.com/gongcongcong/mbOmic VignetteBuilder: knitr BugReports: https://github.com/gongcongcong/mbOmic/issues git_url: https://git.bioconductor.org/packages/mbOmic git_branch: RELEASE_3_16 git_last_commit: be27122 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mbOmic_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mbOmic_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mbOmic_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mbOmic_1.2.0.tgz vignettes: vignettes/mbOmic/inst/doc/enterotyping.html, vignettes/mbOmic/inst/doc/Integrative_analysis_of_metabolome_and_microbiome.html vignetteTitles: enterotyping, Integrative analysis of metabolome and microbiome hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mbOmic/inst/doc/enterotyping.R, vignettes/mbOmic/inst/doc/Integrative_analysis_of_metabolome_and_microbiome.R dependencyCount: 129 Package: mBPCR Version: 1.52.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: 1257ad22641e0de0f2f3d7b54b7b30fa NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes. biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: RELEASE_3_16 git_last_commit: 2b29f2a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mBPCR_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mBPCR_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mBPCR_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mBPCR_1.52.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 89 Package: MBQN Version: 2.10.0 Depends: R (>= 3.6) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, xml2, RCurl, ggplot2, PairedData, rmarkdown Suggests: knitr License: GPL-3 + file LICENSE MD5sum: 35da55cc00f00359c3c6620bcde393d3 NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Eva Brombacher [aut, cre] (), Clemens Kreutz [aut, ctb] (), Ariane Schad [aut, ctb] () Maintainer: Eva Brombacher URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: RELEASE_3_16 git_last_commit: e7c0e34 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MBQN_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBQN_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBQN_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MBQN_2.10.0.tgz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 106 Package: MBttest Version: 1.26.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 7088560785d6a6276f86cead49f90ef5 NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: RELEASE_3_16 git_last_commit: 353504b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MBttest_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MBttest_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MBttest_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MBttest_1.26.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 11 Package: MCbiclust Version: 1.22.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 MD5sum: 835a9f2ed663c29089258cb00e056f6a NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: RELEASE_3_16 git_last_commit: 90b6226 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MCbiclust_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MCbiclust_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MCbiclust_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MCbiclust_1.22.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 136 Package: mCSEA Version: 1.18.0 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, methods, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 MD5sum: 37b5bf1c53d9fd57ca5d0aefd6da0262 NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: RELEASE_3_16 git_last_commit: 68000c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mCSEA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mCSEA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mCSEA_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mCSEA_1.18.0.tgz vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf vignetteTitles: Predefined DMRs identification with mCSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R suggestsMe: shinyepico dependencyCount: 166 Package: mdp Version: 1.18.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: 7387ef3ab0e62a1006d02e00e3d9d3b6 NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: RELEASE_3_16 git_last_commit: 8d8910a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mdp_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mdp_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mdp_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mdp_1.18.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 36 Package: mdqc Version: 1.60.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 8d82c7aba584e2523fc09bda2ba4f409 NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: RELEASE_3_16 git_last_commit: 5cfb017 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mdqc_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mdqc_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mdqc_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mdqc_1.60.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDTS Version: 1.18.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: a552de65d5b110c862321a2bedaaea08 NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: RELEASE_3_16 git_last_commit: c9870c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MDTS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MDTS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MDTS_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MDTS_1.18.0.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 49 Package: MEAL Version: 1.28.0 Depends: R (>= 3.6.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: 8d17ccdc765b89dccdfc4e71ff7d88ee NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: RELEASE_3_16 git_last_commit: 9faea2e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/MEAL_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEAL_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEAL_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MEAL_1.28.0.tgz vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R dependencyCount: 220 Package: MeasurementError.cor Version: 1.70.0 License: LGPL MD5sum: df7c8bf4546ea1fcf5abc1ef2900ebbe NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: RELEASE_3_16 git_last_commit: 5799041 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MeasurementError.cor_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MeasurementError.cor_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MeasurementError.cor_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MeasurementError.cor_1.70.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEAT Version: 1.10.0 Depends: R (>= 4.0) Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0), grDevices, graphics, stats, utils, stringr, tibble, RPMM (>= 1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon Suggests: knitr, markdown, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 697c230d4cff7cccca0acbc5712f2d52 NeedsCompilation: no Title: Muscle Epigenetic Age Test Description: This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC). biocViews: Epigenetics, DNAMethylation, Microarray, Normalization, BiomedicalInformatics, MethylationArray, Preprocessing Author: Sarah Voisin [aut, cre] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin URL: https://github.com/sarah-voisin/MEAT VignetteBuilder: knitr BugReports: https://github.com/sarah-voisin/MEAT/issues git_url: https://git.bioconductor.org/packages/MEAT git_branch: RELEASE_3_16 git_last_commit: 6a7e235 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MEAT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEAT_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEAT_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MEAT_1.10.0.tgz vignettes: vignettes/MEAT/inst/doc/MEAT.html vignetteTitles: MEAT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAT/inst/doc/MEAT.R dependencyCount: 175 Package: MEB Version: 1.12.0 Depends: R (>= 3.6.0) Imports: e1071, SummarizedExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: 377f8bcf1d05dca277ca4faa644cef3f NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq data Description: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work. The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. We transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a normalization-invariant minimum enclosing ball (NIMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed NIMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the NIMEB method could be easily extended to different species without normalization. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification, Sequencing Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: RELEASE_3_16 git_last_commit: 5573f45 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MEB_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEB_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEB_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MEB_1.12.0.tgz vignettes: vignettes/MEB/inst/doc/NIMEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/NIMEB.R dependencyCount: 29 Package: MEDIPS Version: 1.50.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: 74a83e23eaceb74b612abcc98a88af9f NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: RELEASE_3_16 git_last_commit: 04e00ee git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MEDIPS_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEDIPS_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEDIPS_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MEDIPS_1.50.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R dependencyCount: 103 Package: MEDME Version: 1.58.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) MD5sum: cb31997e1c89fe04653b1554848ed04a NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: RELEASE_3_16 git_last_commit: 170d6e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MEDME_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEDME_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEDME_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MEDME_1.58.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 98 Package: megadepth Version: 1.8.0 Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun, dplyr, magrittr Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer, derfinder, GenomeInfoDb, tools, RefManageR, testthat License: Artistic-2.0 Archs: x64 MD5sum: 7117f19e94ae0ade0fd844097f15f2a5 NeedsCompilation: no Title: megadepth: BigWig and BAM related utilities Description: This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3. biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq, Preprocessing Author: Leonardo Collado-Torres [aut] (), David Zhang [aut, cre] () Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: RELEASE_3_16 git_last_commit: 6e567a2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/megadepth_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/megadepth_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/megadepth_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/megadepth_1.8.0.tgz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R importsMe: dasper, ODER dependencyCount: 83 Package: MEIGOR Version: 1.31.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR, knitr License: GPL-3 MD5sum: 50a47ae91d44e99d84fc830223aed148 NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose A. Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: master git_last_commit: 44e6a78 git_last_commit_date: 2022-04-26 Date/Publication: 2022-05-11 source.ver: src/contrib/MEIGOR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MEIGOR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MEIGOR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MEIGOR_1.32.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R importsMe: bioOED dependencyCount: 80 Package: Melissa Version: 1.14.0 Depends: R (>= 3.5.0), BPRMeth, GenomicRanges Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2, doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm, truncnorm, assertthat, BiocStyle, stats, utils Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: a02b9d9345b8937018fb1be05403ec4b NeedsCompilation: no Title: Bayesian clustering and imputationa of single cell methylomes Description: Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: C. A. Kapourani [aut, cre] Maintainer: C. A. Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: RELEASE_3_16 git_last_commit: 033e42e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Melissa_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Melissa_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Melissa_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Melissa_1.14.0.tgz vignettes: vignettes/Melissa/inst/doc/process_files.html, vignettes/Melissa/inst/doc/run_melissa.html vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and impute scBS-seq data using Melissa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Melissa/inst/doc/process_files.R, vignettes/Melissa/inst/doc/run_melissa.R dependencyCount: 111 Package: memes Version: 1.6.0 Depends: R (>= 4.1) Imports: Biostrings, dplyr, cmdfun (>= 1.0.2), GenomicRanges, ggplot2, ggseqlogo, magrittr, matrixStats, methods, patchwork, processx, purrr, rlang, readr, stats, tools, tibble, tidyr, utils, usethis, universalmotif (>= 1.9.3), xml2 Suggests: cowplot, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm6, forcats, testthat (>= 2.1.0), knitr, MotifDb, pheatmap, PMCMRplus, plyranges (>= 1.9.1), rmarkdown, covr License: MIT + file LICENSE MD5sum: cd87df8450ab4b31ed3be36ac4bd7ce1 NeedsCompilation: no Title: motif matching, comparison, and de novo discovery using the MEME Suite Description: A seamless interface to the MEME Suite family of tools for motif analysis. 'memes' provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. 'memes' functions and data structures are amenable to both base R and tidyverse workflows. biocViews: DataImport, FunctionalGenomics, GeneRegulation, MotifAnnotation, MotifDiscovery, SequenceMatching, Software Author: Spencer Nystrom [aut, cre, cph] () Maintainer: Spencer Nystrom URL: https://snystrom.github.io/memes/, https://github.com/snystrom/memes SystemRequirements: Meme Suite (v5.3.3 or above) VignetteBuilder: knitr BugReports: https://github.com/snystrom/memes/issues git_url: https://git.bioconductor.org/packages/memes git_branch: RELEASE_3_16 git_last_commit: bad5ebf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/memes_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/memes_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/memes_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/memes_1.6.0.tgz vignettes: vignettes/memes/inst/doc/core_ame.html, vignettes/memes/inst/doc/core_dreme.html, vignettes/memes/inst/doc/core_fimo.html, vignettes/memes/inst/doc/core_tomtom.html, vignettes/memes/inst/doc/install_guide.html, vignettes/memes/inst/doc/integrative_analysis.html, vignettes/memes/inst/doc/tidy_motifs.html vignetteTitles: Motif Enrichment Testing using AME, Denovo Motif Discovery Using DREME, Motif Scanning using FIMO, Motif Comparison using TomTom, Install MEME, ChIP-seq Analysis, Tidying Motif Metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/memes/inst/doc/core_ame.R, vignettes/memes/inst/doc/core_dreme.R, vignettes/memes/inst/doc/core_fimo.R, vignettes/memes/inst/doc/core_tomtom.R, vignettes/memes/inst/doc/install_guide.R, vignettes/memes/inst/doc/integrative_analysis.R, vignettes/memes/inst/doc/tidy_motifs.R importsMe: ggmotif dependencyCount: 110 Package: Mergeomics Version: 1.26.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: d3abc85630fc4589d351dd3e51621fdb NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: RELEASE_3_16 git_last_commit: e0c1cee git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Mergeomics_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Mergeomics_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Mergeomics_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Mergeomics_1.26.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.34.0 Depends: R (>= 3.0.1) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: testthat License: Artistic-2.0 MD5sum: f352f912e56306ac1d3eafa4c9034abf NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: RELEASE_3_16 git_last_commit: 39b73f7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MeSHDbi_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MeSHDbi_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MeSHDbi_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MeSHDbi_1.34.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: meshes, meshr, scTensor dependencyCount: 46 Package: meshes Version: 1.24.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim, methods, utils, AnnotationHub, MeSHDbi, yulab.utils Suggests: knitr, rmarkdown, prettydoc License: Artistic-2.0 MD5sum: 9b5827ad8c0fb6536efd04eaa78c098d NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre], Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: RELEASE_3_16 git_last_commit: fdaaf5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/meshes_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/meshes_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/meshes_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/meshes_1.24.0.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 160 Package: meshr Version: 2.4.0 Depends: R (>= 4.1.0) Imports: markdown, rmarkdown, BiocStyle, knitr, methods, stats, utils, fdrtool, MeSHDbi, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 75e145adeff5dfbef95848ca41431bf4 NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr BugReports: https://github.com/rikenbit/meshr/issues git_url: https://git.bioconductor.org/packages/meshr git_branch: RELEASE_3_16 git_last_commit: b95327d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/meshr_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/meshr_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/meshr_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/meshr_2.4.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.html vignetteTitles: AnnotationHub-style MeSH ORA Framework from BioC 3.14 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 87 Package: MesKit Version: 1.8.0 Depends: R (>= 4.0.0) Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape (>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges, circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3), ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), org.Hs.eg.db, clusterProfiler, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: d43a3e4893ddf2236ca7cb581c403eb3 NeedsCompilation: no Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study. Author: Mengni Liu [aut, cre] (), Jianyu Chen [aut, ctb] (), Xin Wang [aut, ctb] () Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: RELEASE_3_16 git_last_commit: f2c7a2f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MesKit_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MesKit_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MesKit_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MesKit_1.8.0.tgz vignettes: vignettes/MesKit/inst/doc/MesKit.html vignetteTitles: Analyze and Visualize Multi-region Whole-exome Sequencing Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MesKit/inst/doc/MesKit.R dependencyCount: 103 Package: messina Version: 1.34.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) MD5sum: e12125306b13ef61e4b98dd3d9c1851b NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: RELEASE_3_16 git_last_commit: 1ffb87a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/messina_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/messina_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/messina_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/messina_1.34.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 41 Package: Metab Version: 1.32.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) Archs: x64 MD5sum: d2590780fb9463863f1b3c0a6c37c2f7 NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio git_url: https://git.bioconductor.org/packages/Metab git_branch: RELEASE_3_16 git_last_commit: 7502174 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Metab_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Metab_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Metab_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Metab_1.32.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R dependencyCount: 97 Package: metabCombiner Version: 1.8.0 Depends: R (>= 4.0), dplyr (>= 1.0) Imports: methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats, tidyr Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: eaaebdcaf1ea4f58ee9edf60bd9b9e6b NeedsCompilation: yes Title: Method for Combining LC-MS Metabolomics Feature Measurements Description: This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Peak-picked and simply aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods. biocViews: Software, MassSpectrometry, Metabolomics Author: Hani Habra [aut, cre], Alla Karnovsky [ths] Maintainer: Hani Habra VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: RELEASE_3_16 git_last_commit: 6a66b0a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metabCombiner_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabCombiner_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabCombiner_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metabCombiner_1.8.0.tgz vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R dependencyCount: 89 Package: metabinR Version: 1.0.0 Depends: R (>= 4.2) Imports: methods, rJava Suggests: BiocStyle, cvms, data.table, dplyr, ggplot2, gridExtra, knitr, rmarkdown, sabre, spelling, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 2f246dbf15e1c86236ad5a07e686e383 NeedsCompilation: no Title: Abundance and Compositional Based Binning of Metagenomes Description: Provide functions for performing abundance and compositional based binning on metagenomic samples, directly from FASTA or FASTQ files. Functions are implemented in Java and called via rJava. Parallel implementation that operates directly on input FASTA/FASTQ files for fast execution. biocViews: Classification, Clustering, Microbiome, Sequencing, Software Author: Anestis Gkanogiannis [aut, cre] () Maintainer: Anestis Gkanogiannis URL: https://github.com/gkanogiannis/metabinR SystemRequirements: Java (>= 8) VignetteBuilder: knitr BugReports: https://github.com/gkanogiannis/metabinR/issues git_url: https://git.bioconductor.org/packages/metabinR git_branch: RELEASE_3_16 git_last_commit: 922bf59 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metabinR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabinR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabinR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metabinR_1.0.0.tgz vignettes: vignettes/metabinR/inst/doc/metabinR_vignette.html vignetteTitles: metabinR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabinR/inst/doc/metabinR_vignette.R dependencyCount: 2 Package: MetaboAnnotation Version: 1.2.0 Depends: R (>= 4.0.0) Imports: BiocGenerics, MsCoreUtils, MetaboCoreUtils, ProtGenerics, methods, S4Vectors, Spectra (>= 1.7.2), BiocParallel, SummarizedExperiment, QFeatures, graphics, CompoundDb Suggests: testthat, knitr, msdata, BiocStyle, rmarkdown, plotly, shiny, shinyjs, DT, AnnotationHub Enhances: RMariaDB, RSQLite License: Artistic-2.0 MD5sum: 765d584f864fd68d19f39e7aded811ab NeedsCompilation: no Title: Utilities for Annotation of Metabolomics Data Description: High level functions to assist in annotation of (metabolomics) data sets. These include functions to perform simple tentative annotations based on mass matching but also functions to consider m/z and retention times for annotation of LC-MS features given that respective reference values are available. In addition, the function provides high-level functions to simplify matching of LC-MS/MS spectra against spectral libraries and objects and functionality to represent and manage such matched data. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Michael Witting [aut] (), Johannes Rainer [aut, cre] (), Andrea Vicini [aut] (), Carolin Huber [aut] (), Nir Shachaf [ctb] Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboAnnotation VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboAnnotation/issues git_url: https://git.bioconductor.org/packages/MetaboAnnotation git_branch: RELEASE_3_16 git_last_commit: f8abfdd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaboAnnotation_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaboAnnotation_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaboAnnotation_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaboAnnotation_1.2.0.tgz vignettes: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.html vignetteTitles: Annotation of MS-based Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboAnnotation/inst/doc/MetaboAnnotation.R dependencyCount: 130 Package: MetaboCoreUtils Version: 1.6.0 Depends: R (>= 4.0) Imports: utils, MsCoreUtils Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7f8894003e45813e3b3b636a8e6c690c NeedsCompilation: no Title: Core Utils for Metabolomics Data Description: MetaboCoreUtils defines metabolomics-related core functionality provided as low-level functions to allow a data structure-independent usage across various R packages. This includes functions to calculate between ion (adduct) and compound mass-to-charge ratios and masses or functions to work with chemical formulas. The package provides also a set of adduct definitions and information on some commercially available internal standard mixes commonly used in MS experiments. biocViews: Infrastructure, Metabolomics, MassSpectrometry Author: Johannes Rainer [aut, cre] (), Michael Witting [aut] (), Andrea Vicini [aut], Liesa Salzer [ctb] (), Sebastian Gibb [ctb] (), Michael Stravs [ctb] (), Roger Gine [aut] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MetaboCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MetaboCoreUtils/issues git_url: https://git.bioconductor.org/packages/MetaboCoreUtils git_branch: RELEASE_3_16 git_last_commit: 5db2de6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaboCoreUtils_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaboCoreUtils_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaboCoreUtils_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaboCoreUtils_1.6.0.tgz vignettes: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.html vignetteTitles: Core Utils for Metabolomics Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboCoreUtils/inst/doc/MetaboCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, MobilityTransformR dependencyCount: 13 Package: metabolomicsWorkbenchR Version: 1.8.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, HDF5Array, rmarkdown, structToolbox, testthat, pmp, grid, png License: GPL-3 MD5sum: 98779e05a6ecaf5e96427b9783d2709f NeedsCompilation: no Title: Metabolomics Workbench in R Description: This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included. biocViews: Software, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: RELEASE_3_16 git_last_commit: a01bdb0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metabolomicsWorkbenchR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabolomicsWorkbenchR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabolomicsWorkbenchR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metabolomicsWorkbenchR_1.8.0.tgz vignettes: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.html, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html vignetteTitles: Example using structToolbox, Introduction_to_metabolomicsWorkbenchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabolomicsWorkbenchR/inst/doc/example_using_structToolbox.R, vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R suggestsMe: fobitools dependencyCount: 69 Package: metabomxtr Version: 1.32.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 5c21ea442b80f415e609147d24c54420 NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: RELEASE_3_16 git_last_commit: 272a296 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metabomxtr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metabomxtr_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metabomxtr_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metabomxtr_1.32.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 55 Package: MetaboSignal Version: 1.28.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 7753098e88b0645ce181a04b75e746d1 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: RELEASE_3_16 git_last_commit: efa796b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaboSignal_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaboSignal_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaboSignal_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaboSignal_1.28.0.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R dependencyCount: 197 Package: metaCCA Version: 1.26.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 2dd11c0a868cce00b1017b8b9307960b NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: RELEASE_3_16 git_last_commit: 0c559ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metaCCA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaCCA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaCCA_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metaCCA_1.26.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.20.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr, rmarkdown License: GPL (>= 2) MD5sum: fd528475a5ee7f8d84e6db523f61eeb3 NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_16 git_last_commit: a9e46bd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaCyto_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaCyto_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaCyto_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaCyto_1.20.0.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 118 Package: metagene Version: 2.30.0 Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, Rsamtools, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 44d4a58f2bc9eeafdf638408df47ad99 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Cedric Lippens , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues git_url: https://git.bioconductor.org/packages/metagene git_branch: RELEASE_3_16 git_last_commit: 3dc7c03 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metagene_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metagene_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metagene_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metagene_2.30.0.tgz vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html, vignettes/metagene/inst/doc/metagene.html vignetteTitles: RNA-seq exp ext, Introduction to metagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R, vignettes/metagene/inst/doc/metagene.R dependencyCount: 120 Package: metagene2 Version: 1.14.0 Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, purrr, data.table, methods, dplyr, magrittr, reshape2 Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: b72b6a0fdcaba7a341f0498423558284 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: RELEASE_3_16 git_last_commit: 1b39327 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metagene2_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metagene2_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metagene2_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metagene2_1.14.0.tgz vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 83 Package: metagenomeSeq Version: 1.40.0 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay, IHW License: Artistic-2.0 MD5sum: 781b0ca9bd689236e6130ec81ce66998 NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: RELEASE_3_16 git_last_commit: be25ae5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metagenomeSeq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metagenomeSeq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metagenomeSeq_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metagenomeSeq_1.40.0.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: metavizr, microbiomeExplorer, etec16s importsMe: benchdamic, Maaslin2, microbiomeDASim, microbiomeMarker, ggpicrust2, MetaLonDA suggestsMe: interactiveDisplay, phyloseq, scTreeViz, Wrench dependencyCount: 30 Package: metahdep Version: 1.56.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 MD5sum: 3254dcdb515fa65cee397b399a41e8a1 NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: RELEASE_3_16 git_last_commit: 97ce24c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metahdep_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metahdep_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metahdep_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metahdep_1.56.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: metaMS Version: 1.34.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) Archs: x64 MD5sum: 8b3004d0b568589b810c5629a0c3f449 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: RELEASE_3_16 git_last_commit: aa2e678 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metaMS_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaMS_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaMS_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metaMS_1.34.0.tgz vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R suggestsMe: CluMSID dependencyCount: 133 Package: MetaNeighbor Version: 1.18.0 Depends: R(>= 3.5) Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4), Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2), gplots (>= 3.0.1), RColorBrewer (>= 1.1.2), SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph, dplyr, tidyr, tibble, ggplot2 Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2), UpSetR License: MIT + file LICENSE MD5sum: 047097b746bdb379a6ca23938eeb41ac NeedsCompilation: no Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Stephan Fischer [ctb], Jesse Gillis [aut] Maintainer: Stephan Fischer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: RELEASE_3_16 git_last_commit: 0d0d406 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaNeighbor_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaNeighbor_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaNeighbor_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaNeighbor_1.18.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R dependencyCount: 67 Package: MetaPhOR Version: 1.0.0 Depends: R (>= 4.2.0) Imports: utils, ggplot2, ggrepel, stringr, pheatmap, grDevices, stats, clusterProfiler, RecordLinkage, RCy3 Suggests: BiocStyle, knitr, rmarkdown, kableExtra License: Artistic-2.0 MD5sum: 62c0c64907ffe17e7ff61dfe5eb49a24 NeedsCompilation: no Title: Metabolic Pathway Analysis of RNA Description: MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models. biocViews: Metabolomics, RNASeq, Pathways, GeneExpression, DifferentialExpression, KEGG, Sequencing, Microarray Author: Emily Isenhart [aut, cre], Spencer Rosario [aut] Maintainer: Emily Isenhart SystemRequirements: Cytoscape (>= 3.9.0) for the cytoPath() examples VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaPhOR git_branch: RELEASE_3_16 git_last_commit: 033530e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaPhOR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaPhOR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaPhOR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaPhOR_1.0.0.tgz vignettes: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.html vignetteTitles: MetaPhOR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaPhOR/inst/doc/MetaPhOR-vignette.R dependencyCount: 175 Package: metapod Version: 1.6.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 544ab23b2d3a4c6ec951080c4f485ef4 NeedsCompilation: yes Title: Meta-Analyses on P-Values of Differential Analyses Description: Implements a variety of methods for combining p-values in differential analyses of genome-scale datasets. Functions can combine p-values across different tests in the same analysis (e.g., genomic windows in ChIP-seq, exons in RNA-seq) or for corresponding tests across separate analyses (e.g., replicated comparisons, effect of different treatment conditions). Support is provided for handling log-transformed input p-values, missing values and weighting where appropriate. biocViews: MultipleComparison, DifferentialPeakCalling Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapod git_branch: RELEASE_3_16 git_last_commit: cfeaa95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metapod_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metapod_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metapod_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metapod_1.6.0.tgz vignettes: vignettes/metapod/inst/doc/overview.html vignetteTitles: Meta-analysis strategies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapod/inst/doc/overview.R importsMe: csaw, mumosa, scran suggestsMe: TSCAN dependencyCount: 3 Package: metapone Version: 1.4.0 Depends: R (>= 4.1.0), BiocParallel, fields, markdown, fdrtool, fgsea, ggplot2, ggrepel Imports: methods Suggests: rmarkdown, knitr License: Artistic-2.0 Archs: x64 MD5sum: cb7f29f1c0343c6b332a9f3b7fbec80a NeedsCompilation: no Title: Conducts pathway test of metabolomics data using a weighted permutation test Description: The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests. biocViews: Technology, MassSpectrometry, Metabolomics, Pathways Author: Leqi Tian [aut], Tianwei Yu [aut], Tianwei Yu [cre] Maintainer: Tianwei Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metapone git_branch: RELEASE_3_16 git_last_commit: 1532506 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metapone_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metapone_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metapone_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metapone_1.4.0.tgz vignettes: vignettes/metapone/inst/doc/metapone.html vignetteTitles: metapone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metapone/inst/doc/metapone.R dependencyCount: 62 Package: metaSeq Version: 1.38.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 926c2f82c7410bce7ebfa51e93895157 NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: RELEASE_3_16 git_last_commit: eebff40 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metaSeq_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaSeq_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaSeq_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metaSeq_1.38.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 14 Package: metaseqR2 Version: 1.10.0 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, baySeq, Biobase, BiocGenerics, BiocParallel, biomaRt, Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, gplots, graphics, grDevices, harmonicmeanp, heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r, magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel, qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer, S4Vectors, stats, stringr, SummarizedExperiment, survcomp, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) MD5sum: 12e54b517ff10bbb7f765ff325904823 NeedsCompilation: yes Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, CellBiology, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, ImmunoOncology, AlternativeSplicing, DifferentialSplicing, MultipleComparison, TimeCourse, DataImport, ATACSeq, Epigenetics, Regression, ProprietaryPlatforms, GeneSetEnrichment, BatchEffect, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr BugReports: https://github.com/pmoulos/metaseqR2/issues git_url: https://git.bioconductor.org/packages/metaseqR2 git_branch: RELEASE_3_16 git_last_commit: 73e2738 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/metaseqR2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/metaseqR2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/metaseqR2_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/metaseqR2_1.10.0.tgz vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq data analysis with metaseqR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R dependencyCount: 232 Package: metavizr Version: 1.21.0 Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table, Biobase, digest Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan, GenomeInfoDb, phyloseq, httr Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s, testthat, gss, ExperimentHub, tidyr, rmarkdown License: MIT + file LICENSE MD5sum: fd5ace5edab7af4671b13311b1b0aab4 NeedsCompilation: no Title: R Interface to the metaviz web app for interactive metagenomics data analysis and visualization Description: This package provides Websocket communication to the metaviz web app (http://metaviz.cbcb.umd.edu) for interactive visualization of metagenomics data. Objects in R/bioc interactive sessions can be displayed in plots and data can be explored using a facetzoom visualization. Fundamental Bioconductor data structures are supported (e.g., MRexperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI, Metagenomics, ImmunoOncology Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metavizr git_branch: master git_last_commit: eb1b65b git_last_commit_date: 2022-04-26 Date/Publication: 2022-05-01 source.ver: src/contrib/metavizr_1.21.0.tar.gz vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html vignetteTitles: Introduction to metavizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R dependencyCount: 162 Package: MetaVolcanoR Version: 1.12.0 Depends: R (>= 4.1.1) Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot, parallel, metafor, metap, rlang, topconfects, grDevices, graphics, stats, htmlwidgets Suggests: knitr, markdown, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: ac500cc922d3bc71ba24c1b76ab53285 NeedsCompilation: no Title: Gene Expression Meta-analysis Visualization Tool Description: MetaVolcanoR combines differential gene expression results. It implements three strategies to summarize differential gene expression from different studies. i) Random Effects Model (REM) approach, ii) a p-value combining-approach, and iii) a vote-counting approach. In all cases, MetaVolcano exploits the Volcano plot reasoning to visualize the gene expression meta-analysis results. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, mRNAMicroarray, RNASeq Author: Cesar Prada [aut, cre], Diogenes Lima [aut], Helder Nakaya [aut, ths] Maintainer: Cesar Prada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaVolcanoR git_branch: RELEASE_3_16 git_last_commit: f1a0ded git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetaVolcanoR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetaVolcanoR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetaVolcanoR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetaVolcanoR_1.12.0.tgz vignettes: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.html, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.html vignetteTitles: MetaVolcanoR: Differential expression meta-analysis tool, MetaVolcanoR inputs: differential expression examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.R, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.R dependencyCount: 113 Package: MetCirc Version: 1.28.0 Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>= 0.3.0), shiny (>= 1.0.0), Spectra (>= 1.4.3) Imports: ggplot2 (>= 3.2.1), MsCoreUtils (>= 1.9.2), S4Vectors (>= 0.22.0) Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr (>= 1.11), testthat (>= 2.2.1) License: GPL (>= 3) MD5sum: 66d09727991fe6ab93188f52f1a7f48e NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectra object infrastructure defined in the package Spectra that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ShinyApps, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: RELEASE_3_16 git_last_commit: f661a59 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetCirc_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetCirc_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetCirc_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetCirc_1.28.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.html vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 83 Package: MethCP Version: 1.11.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, S4Vectors, bsseq, DSS, methylKit, DNAcopy, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel Suggests: testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: bb85633025374d75480bc6ebc378e1a2 NeedsCompilation: no Title: Differential methylation anlsysis for bisulfite sequencing data Description: MethCP is a differentially methylated region (DMR) detecting method for whole-genome bisulfite sequencing (WGBS) data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. biocViews: DifferentialMethylation, Sequencing, WholeGenome, TimeCourse Author: Boying Gong [aut, cre] Maintainer: Boying Gong VignetteBuilder: knitr BugReports: https://github.com/boyinggong/methcp/issues git_url: https://git.bioconductor.org/packages/MethCP git_branch: master git_last_commit: e38198b git_last_commit_date: 2022-04-26 Date/Publication: 2022-08-24 source.ver: src/contrib/MethCP_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethCP_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethCP_1.11.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethCP_1.12.0.tgz vignettes: vignettes/MethCP/inst/doc/methcp.html vignetteTitles: methcp: User’s Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethCP/inst/doc/methcp.R dependencyCount: 108 Package: methimpute Version: 1.20.0 Depends: R (>= 3.5.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 2d5fa5fe9c7fe201f53af5561519b0ac NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: RELEASE_3_16 git_last_commit: 4be41e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methimpute_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methimpute_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methimpute_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methimpute_1.20.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 56 Package: methInheritSim Version: 1.20.0 Depends: R (>= 3.5.0) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 Archs: x64 MD5sum: c1f975201f2fa7cfee6890e12fc0ef1b NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: RELEASE_3_16 git_last_commit: 1c88aaa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methInheritSim_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methInheritSim_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methInheritSim_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methInheritSim_1.20.0.tgz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 98 Package: MethPed Version: 1.26.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: af4c539faa889628f3a62a72292fc82d NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: RELEASE_3_16 git_last_commit: f639e37 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MethPed_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethPed_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethPed_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethPed_1.26.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 8 Package: MethReg Version: 1.8.0 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, sesame, AnnotationHub, ExperimentHub, stringr, readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges, sfsmisc, progress, utils Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, downloader, R.utils, doParallel, reshape2, JASPAR2022, TFBSTools, motifmatchr, matrixStats, biomaRt, dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr, jpeg, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: 93dfe428335da54d3cc959b5623140fb NeedsCompilation: no Title: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription Description: Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis. biocViews: MethylationArray, Regression, GeneExpression, Epigenetics, GeneTarget, Transcription Author: Tiago Silva [aut, cre] (), Lily Wang [aut] Maintainer: Tiago Silva VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/MethReg/issues/ git_url: https://git.bioconductor.org/packages/MethReg git_branch: RELEASE_3_16 git_last_commit: 6235257 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MethReg_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethReg_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethReg_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethReg_1.8.0.tgz vignettes: vignettes/MethReg/inst/doc/MethReg.html vignetteTitles: MethReg: estimating regulatory potential of DNA methylation in gene transcription hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethReg/inst/doc/MethReg.R dependencyCount: 183 Package: methrix Version: 1.12.0 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: fecc1dae6e67b793613f4e1eb62c2307 NeedsCompilation: no Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre] (), Reka Toth [aut] (), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda URL: https://github.com/CompEpigen/methrix VignetteBuilder: knitr BugReports: https://github.com/CompEpigen/methrix/issues git_url: https://git.bioconductor.org/packages/methrix git_branch: RELEASE_3_16 git_last_commit: c93ba71 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methrix_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methrix_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methrix_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methrix_1.12.0.tgz vignettes: vignettes/methrix/inst/doc/methrix.html vignetteTitles: Methrix tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methrix/inst/doc/methrix.R dependencyCount: 82 Package: MethTargetedNGS Version: 1.30.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: e1a0275c0f8ae3004821d5d37635e171 NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: RELEASE_3_16 git_last_commit: 55148d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MethTargetedNGS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethTargetedNGS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethTargetedNGS_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethTargetedNGS_1.30.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 41 Package: MethylAid Version: 1.32.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: 0328e8b4faf6fb81958299f2e622cf5a NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: RELEASE_3_16 git_last_commit: 49b0870 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MethylAid_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethylAid_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethylAid_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethylAid_1.32.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R dependsOnMe: MethylAidData dependencyCount: 166 Package: methylCC Version: 1.12.0 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: rmarkdown, knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: CC BY 4.0 MD5sum: 370c98b0b66db5ef70157a92c16e2549 NeedsCompilation: no Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ git_url: https://git.bioconductor.org/packages/methylCC git_branch: RELEASE_3_16 git_last_commit: 4a79f51 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylCC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylCC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylCC_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylCC_1.12.0.tgz vignettes: vignettes/methylCC/inst/doc/methylCC.html vignetteTitles: The methylCC user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylCC/inst/doc/methylCC.R dependencyCount: 155 Package: methylclock Version: 1.4.0 Depends: R (>= 4.1.0), methylclockData, devtools, quadprog Imports: Rcpp (>= 1.0.6), ExperimentHub, dplyr, impute, PerformanceAnalytics, Biobase, ggpmisc, tidyverse, ggplot2, ggpubr, minfi, tibble, RPMM, stats, graphics, tidyr, gridExtra, preprocessCore, dynamicTreeCut, planet LinkingTo: Rcpp Suggests: BiocStyle, knitr, GEOquery, rmarkdown License: MIT + file LICENSE MD5sum: c33b21d106163a730dafd515dc4716cf NeedsCompilation: yes Title: Methylclock - DNA methylation-based clocks Description: This package allows to estimate chronological and gestational DNA methylation (DNAm) age as well as biological age using different methylation clocks. Chronological DNAm age (in years) : Horvath's clock, Hannum's clock, BNN, Horvath's skin+blood clock, PedBE clock and Wu's clock. Gestational DNAm age : Knight's clock, Bohlin's clock, Mayne's clock and Lee's clocks. Biological DNAm clocks : Levine's clock and Telomere Length's clock. biocViews: DNAMethylation, BiologicalQuestion, Preprocessing, StatisticalMethod, Normalization Author: Dolors Pelegri-Siso [aut, cre] (), Juan R. Gonzalez [aut] () Maintainer: Dolors Pelegri-Siso URL: https://github.com/isglobal-brge/methylclock VignetteBuilder: knitr BugReports: https://github.com/isglobal-brge/methylclock/issues git_url: https://git.bioconductor.org/packages/methylclock git_branch: RELEASE_3_16 git_last_commit: af3c8d8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylclock_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylclock_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylclock_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylclock_1.4.0.tgz vignettes: vignettes/methylclock/inst/doc/methylclock.html vignetteTitles: DNAm age using diffrent methylation clocks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methylclock/inst/doc/methylclock.R dependencyCount: 286 Package: methylGSA Version: 1.16.0 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 Archs: x64 MD5sum: bf2720c54865670d8495e478148d2d78 NeedsCompilation: no Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues git_url: https://git.bioconductor.org/packages/methylGSA git_branch: RELEASE_3_16 git_last_commit: 21ccfeb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylGSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylGSA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylGSA_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylGSA_1.16.0.tgz vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R dependencyCount: 218 Package: methylInheritance Version: 1.22.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim License: Artistic-2.0 MD5sum: b39c2367bd3f224318ad8e9315ea7723 NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes [cre, aut] (), Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: RELEASE_3_16 git_last_commit: e34c4cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylInheritance_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylInheritance_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylInheritance_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylInheritance_1.22.0.tgz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 101 Package: methylKit Version: 1.24.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 MD5sum: b8bf98f842f90ab6e4759bf0711282e0 NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Gosdschan [aut] Maintainer: Altuna Akalin , Alexander Gosdschan URL: http://code.google.com/p/methylkit/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_16 git_last_commit: 9870e27 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylKit_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylKit_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylKit_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylKit_1.24.0.tgz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: deconvR, MethCP, methInheritSim, methylInheritance dependencyCount: 94 Package: MethylMix Version: 2.28.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 676e6ad64f624fe6bced99b03ca4b1d8 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: RELEASE_3_16 git_last_commit: 0707d87 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MethylMix_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethylMix_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethylMix_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethylMix_2.28.0.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 51 Package: methylMnM Version: 1.36.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 MD5sum: 9770923b1feb24d89c16cdab8bf12dbb NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: RELEASE_3_16 git_last_commit: 930c886 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylMnM_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylMnM_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylMnM_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylMnM_1.36.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 12 Package: methylPipe Version: 1.32.0 Depends: R (>= 3.5.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) Archs: x64 MD5sum: 1eb838d98212f5df6771e7e0a9f10cb8 NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: RELEASE_3_16 git_last_commit: 954da5d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylPipe_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylPipe_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylPipe_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylPipe_1.32.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R dependsOnMe: ListerEtAlBSseq importsMe: compEpiTools dependencyCount: 159 Package: methylscaper Version: 1.6.0 Depends: R (>= 4.1.0) Imports: shiny, shinyjs, seriation, BiocParallel, seqinr, Biostrings, Rfast, grDevices, graphics, stats, utils, tools, methods, shinyFiles, data.table, SummarizedExperiment Suggests: knitr, rmarkdown, devtools License: GPL-2 Archs: x64 MD5sum: cc23579307bd57e74cd97b526a1618ea NeedsCompilation: no Title: Visualization of Methylation Data Description: methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning. biocViews: DNAMethylation, Epigenetics, PrincipalComponent, Visualization, SingleCell, NucleosomePositioning Author: Bacher Rhonda [aut, cre], Parker Knight [aut] Maintainer: Bacher Rhonda VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylscaper git_branch: RELEASE_3_16 git_last_commit: cc53f5b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylscaper_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylscaper_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylscaper_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylscaper_1.6.0.tgz vignettes: vignettes/methylscaper/inst/doc/methylScaper.html vignetteTitles: Using methylscaper to visualize joint methylation and nucleosome occupancy data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylscaper/inst/doc/methylScaper.R dependencyCount: 97 Package: MethylSeekR Version: 1.38.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: 2dcf909a7e64435d3c0f5a4c7f5fd70f NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: RELEASE_3_16 git_last_commit: 376a349 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MethylSeekR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MethylSeekR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MethylSeekR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MethylSeekR_1.38.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 79 Package: methylSig Version: 1.10.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, GenomeInfoDb, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 MD5sum: 92bc68749ca4fdfa145c3ef7c59323ee NeedsCompilation: no Title: MethylSig: Differential Methylation Testing for WGBS and RRBS Data Description: MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Regression, MethylSeq Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues git_url: https://git.bioconductor.org/packages/methylSig git_branch: RELEASE_3_16 git_last_commit: 688a7f9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylSig_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylSig_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylSig_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylSig_1.10.0.tgz vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html, vignettes/methylSig/inst/doc/using-methylSig.html vignetteTitles: Updating methylSig code, Using methylSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R, vignettes/methylSig/inst/doc/using-methylSig.R dependencyCount: 78 Package: methylumi Version: 2.44.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio, GenomicFeatures Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: 7f80c5b030cf4bb315423e21f3398913 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: RELEASE_3_16 git_last_commit: 8f1f1f9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/methylumi_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/methylumi_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/methylumi_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/methylumi_2.44.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, missMethyl dependencyCount: 154 Package: MetID Version: 1.16.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: 58a99e831a5559c9c157f2f3db33421b NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: RELEASE_3_16 git_last_commit: ff12791 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetID_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetID_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetID_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetID_1.16.0.tgz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 133 Package: MetNet Version: 1.16.0 Depends: R (>= 4.0), S4Vectors (>= 0.28.1), SummarizedExperiment (>= 1.20.0) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), corpcor (>= 1.6.10), dplyr (>= 1.0.3), ggplot2 (>= 3.3.3), GeneNet (>= 1.2.15), GENIE3 (>= 1.7.0), methods (>= 3.5), parmigene (>= 1.0.2), psych (>= 2.1.6), rlang (>= 0.4.10), stabs (>= 0.6), stats (>= 3.6), tibble (>= 3.0.5), tidyr (>= 1.1.2) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 4.1-1), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1), Spectra (>= 1.4.1), MsCoreUtils (>= 1.6.0) License: GPL (>= 3) MD5sum: f83206950d7f0fa7a2e3b62122035181 NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre], Liesa Salzer [ctb] Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: RELEASE_3_16 git_last_commit: ce36581 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MetNet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MetNet_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MetNet_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MetNet_1.16.0.tgz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 82 Package: mfa Version: 1.20.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: 05a223975c67fb8300c8ed545748a85c NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: RELEASE_3_16 git_last_commit: 5d56a76 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mfa_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mfa_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mfa_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mfa_1.20.0.tgz vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R suggestsMe: splatter dependencyCount: 70 Package: Mfuzz Version: 2.58.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 Archs: x64 MD5sum: 49767b0d251d93740d749aec0b30f2eb NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: RELEASE_3_16 git_last_commit: 5d926ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Mfuzz_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Mfuzz_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Mfuzz_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Mfuzz_2.58.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle, TimiRGeN importsMe: Patterns suggestsMe: DAPAR, pwOmics dependencyCount: 16 Package: MGFM Version: 1.32.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 336f0be43a2c223680559bf3c2f771dd NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: RELEASE_3_16 git_last_commit: 3f0eab3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MGFM_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MGFM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MGFM_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MGFM_1.32.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier dependencyCount: 49 Package: MGFR Version: 1.24.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 MD5sum: 602d734b6bd69c687c8d9d1600f95c9f NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFR git_branch: RELEASE_3_16 git_last_commit: 2493145 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MGFR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MGFR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MGFR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MGFR_1.24.0.tgz vignettes: vignettes/MGFR/inst/doc/MGFR.pdf vignetteTitles: Using MGFR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFR/inst/doc/MGFR.R dependsOnMe: sampleClassifier dependencyCount: 72 Package: mgsa Version: 1.46.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 MD5sum: 8ced9f2d140d4c14f1fff69dfb679f49 NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: RELEASE_3_16 git_last_commit: d106230 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mgsa_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mgsa_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mgsa_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mgsa_1.46.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R suggestsMe: pareg dependencyCount: 9 Package: mia Version: 1.6.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, TreeSummarizedExperiment (>= 1.99.3), MultiAssayExperiment Imports: methods, stats, utils, MASS, ape, decontam, vegan, BiocGenerics, S4Vectors, IRanges, Biostrings, DECIPHER, BiocParallel, DelayedArray, DelayedMatrixStats, scuttle, scater, DirichletMultinomial, rlang, dplyr, tibble, tidyr Suggests: testthat, knitr, patchwork, BiocStyle, yaml, phyloseq, dada2, stringr, biomformat, reldist, ade4, microbiomeDataSets, rmarkdown License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: addf1abe62527c86a77e7dce098c8f4d NeedsCompilation: no Title: Microbiome analysis Description: mia implements tools for microbiome analysis based on the SummarizedExperiment, SingleCellExperiment and TreeSummarizedExperiment infrastructure. Data wrangling and analysis in the context of taxonomic data is the main scope. Additional functions for common task are implemented such as community indices calculation and summarization. biocViews: Microbiome, Software, DataImport Author: Felix G.M. Ernst [aut] (), Sudarshan A. Shetty [aut] (), Tuomas Borman [aut, cre] (), Leo Lahti [aut] (), Yang Cao [ctb], Nathan D. Olson [ctb], Levi Waldron [ctb], Marcel Ramos [ctb], Héctor Corrada Bravo [ctb], Jayaram Kancherla [ctb], Domenick Braccia [ctb] Maintainer: Tuomas Borman URL: https://github.com/microbiome/mia VignetteBuilder: knitr BugReports: https://github.com/microbiome/mia/issues git_url: https://git.bioconductor.org/packages/mia git_branch: RELEASE_3_16 git_last_commit: 41e31ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mia_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mia_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mia_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mia_1.6.0.tgz vignettes: vignettes/mia/inst/doc/mia.html vignetteTitles: mia hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mia/inst/doc/mia.R dependsOnMe: miaViz importsMe: ANCOMBC, curatedMetagenomicData suggestsMe: CBEA, philr, MicrobiomeBenchmarkData dependencyCount: 133 Package: miaSim Version: 1.4.0 Depends: SummarizedExperiment, TreeSummarizedExperiment Imports: deSolve, stats, poweRlaw, gtools, S4Vectors, MatrixGenerics Suggests: rmarkdown, knitr, BiocStyle, testthat License: Artistic-2.0 | file LICENSE MD5sum: 2799c0ad22de60cfa4463f37604eebcb NeedsCompilation: no Title: Microbiome Data Simulation Description: Microbiome time series simulation with generalized Lotka-Volterra model, Self-Organized Instability (SOI), and other models. Hubbell's Neutral model is used to determine the abundance matrix. The resulting abundance matrix is applied to SummarizedExperiment or TreeSummarizedExperiment objects. biocViews: Microbiome, Software, Sequencing, DNASeq, ATACSeq, Coverage, Network Author: Karoline Faust [aut], Yu Gao [aut], Emma Gheysen [aut], Daniel Rios Garza [aut], Yagmur Simsek [cre, aut], Leo Lahti [aut] () Maintainer: Yagmur Simsek URL: https://github.com/microbiome/miaSim VignetteBuilder: knitr BugReports: https://github.com/microbiome/miaSim/issues git_url: https://git.bioconductor.org/packages/miaSim git_branch: RELEASE_3_16 git_last_commit: 8feadab git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miaSim_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miaSim_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miaSim_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miaSim_1.4.0.tgz vignettes: vignettes/miaSim/inst/doc/miaSim.html vignetteTitles: miaSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaSim/inst/doc/miaSim.R dependencyCount: 72 Package: miaViz Version: 1.6.0 Depends: R (>= 4.0), SummarizedExperiment, TreeSummarizedExperiment, mia (>= 0.99), ggplot2, ggraph (>= 2.0) Imports: methods, stats, S4Vectors, BiocGenerics, BiocParallel, DelayedArray, scater, ggtree, ggnewscale, viridis, tibble, tidytree, tidygraph, rlang, purrr, tidyr, dplyr, ape, DirichletMultinomial Suggests: knitr, rmarkdown, BiocStyle, testthat, patchwork, microbiomeDataSets License: Artistic-2.0 | file LICENSE MD5sum: 8c72bf7cd69b58db6e4e9b2138f1e4e4 NeedsCompilation: no Title: Microbiome Analysis Plotting and Visualization Description: The miaViz package implements plotting function to work with TreeSummarizedExperiment and related objects in a context of microbiome analysis. Among others this includes plotting tree, graph and microbiome series data. The package is part of the broader miaverse framework. biocViews: Microbiome, Software, Visualization Author: Felix G.M. Ernst [aut] (), Tuomas Borman [aut, cre] (), Leo Lahti [aut] () Maintainer: Tuomas Borman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miaViz git_branch: RELEASE_3_16 git_last_commit: b72c5dd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miaViz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miaViz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miaViz_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miaViz_1.6.0.tgz vignettes: vignettes/miaViz/inst/doc/miaViz.html vignetteTitles: miaViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miaViz/inst/doc/miaViz.R dependencyCount: 149 Package: MiChip Version: 1.52.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: b53a01c3859c397181c6ab483c19da4c NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: RELEASE_3_16 git_last_commit: 2e1bf89 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MiChip_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MiChip_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MiChip_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MiChip_1.52.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 6 Package: microbiome Version: 1.20.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: Biostrings, compositions, dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: c43968ac013d507e53ad62db6e172973 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre] (), Sudarshan Shetty [aut] () Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: RELEASE_3_16 git_last_commit: 819977f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/microbiome_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiome_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiome_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/microbiome_1.20.0.tgz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R suggestsMe: ANCOMBC dependencyCount: 89 Package: microbiomeDASim Version: 1.12.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats, phyloseq, metagenomeSeq, Biobase Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE MD5sum: 35cff3663fc38b7de8dc1a3a3ae6a5fd NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: RELEASE_3_16 git_last_commit: 0f72d43 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/microbiomeDASim_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiomeDASim_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiomeDASim_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/microbiomeDASim_1.12.0.tgz vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf vignetteTitles: microbiomeDASim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R dependencyCount: 96 Package: microbiomeExplorer Version: 1.8.0 Depends: shiny, magrittr, metagenomeSeq, Biobase Imports: shinyjs (>= 2.0.0), shinydashboard, shinycssloaders, shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer, dplyr, tidyr, purrr, rlang, knitr, readr, DT (>= 0.12.0), biomformat, tools, stringr, vegan, matrixStats, heatmaply, car, broom, limma, reshape2, tibble, forcats, lubridate, methods, plotly (>= 4.9.1) Suggests: V8, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 773c0a0ac1e0dc54b530fcf0c4258028 NeedsCompilation: no Title: Microbiome Exploration App Description: The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software, ImmunoOncology Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd] Maintainer: Janina Reeder VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: RELEASE_3_16 git_last_commit: a1ed081 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/microbiomeExplorer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiomeExplorer_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiomeExplorer_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/microbiomeExplorer_1.8.0.tgz vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html vignetteTitles: microbiomeExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R dependencyCount: 205 Package: microbiomeMarker Version: 1.4.0 Depends: R (>= 4.1.0) Imports: dplyr, phyloseq, magrittr, purrr, MASS, utils, ggplot2, tibble, rlang, stats, coin, ggtree, tidytree, methods, IRanges, tidyr, patchwork, ggsignif, metagenomeSeq, DESeq2, edgeR, BiocGenerics, Biostrings, yaml, biomformat, S4Vectors, Biobase, ComplexHeatmap, ANCOMBC, caret, limma, ALDEx2, multtest, plotROC, vegan, pROC, BiocParallel Suggests: testthat, covr, glmnet, Matrix, kernlab, e1071, ranger, knitr, rmarkdown, BiocStyle, withr License: GPL-3 MD5sum: 6e1ee6214cd3d90cc0224958866ad4b8 NeedsCompilation: no Title: microbiome biomarker analysis toolkit Description: To date, a number of methods have been developed for microbiome marker discovery based on metagenomic profiles, e.g. LEfSe. However, all of these methods have its own advantages and disadvantages, and none of them is considered standard or universal. Moreover, different programs or softwares may be development using different programming languages, even in different operating systems. Here, we have developed an all-in-one R package microbiomeMarker that integrates commonly used differential analysis methods as well as three machine learning-based approaches, including Logistic regression, Random forest, and Support vector machine, to facilitate the identification of microbiome markers. biocViews: Metagenomics, Microbiome, DifferentialExpression Author: Yang Cao [aut, cre] Maintainer: Yang Cao URL: https://github.com/yiluheihei/microbiomeMarker VignetteBuilder: knitr BugReports: https://github.com/yiluheihei/microbiomeMarker/issues git_url: https://git.bioconductor.org/packages/microbiomeMarker git_branch: RELEASE_3_16 git_last_commit: ab8f294 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/microbiomeMarker_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microbiomeMarker_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microbiomeMarker_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/microbiomeMarker_1.4.0.tgz vignettes: vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.html vignetteTitles: Tools for microbiome marker identification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/microbiomeMarker/inst/doc/microbiomeMarker-vignette.R dependencyCount: 307 Package: MicrobiomeProfiler Version: 1.4.0 Depends: R (>= 4.1.0) Imports: clusterProfiler (>= 4.0.2), config, DT, enrichplot, golem, magrittr, shiny (>= 1.6.0), shinyWidgets, shinycustomloader, htmltools, ggplot2, graphics, utils Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-2 MD5sum: 3b2f44f617bb86f6733fe436a09de06a NeedsCompilation: no Title: An R/shiny package for microbiome functional enrichment analysis Description: This is an R/shiny package to perform functional enrichment analysis for microbiome data. This package was based on clusterProfiler. Moreover, MicrobiomeProfiler support KEGG enrichment analysis, COG enrichment analysis, Microbe-Disease association enrichment analysis, Metabo-Pathway analysis. biocViews: Microbiome, Software, Visualization,KEGG Author: Meijun Chen [aut, cre] (), Guangchuang Yu [aut, ths] () Maintainer: Meijun Chen URL: https://github.com/YuLab-SMU/MicrobiomeProfiler/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiomeProfiler/issues git_url: https://git.bioconductor.org/packages/MicrobiomeProfiler git_branch: RELEASE_3_16 git_last_commit: d038f26 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MicrobiomeProfiler_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MicrobiomeProfiler_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MicrobiomeProfiler_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MicrobiomeProfiler_1.4.0.tgz vignettes: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.html vignetteTitles: MicrobiomeProfiler hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiomeProfiler/inst/doc/MicrobiomeProfiler.R dependencyCount: 163 Package: MicrobiotaProcess Version: 1.10.3 Depends: R (>= 4.0.0) Imports: ape, tidyr, ggplot2, magrittr, dplyr, Biostrings, ggrepel, vegan, zoo, ggtree, tidytree (>= 0.4.2), MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, patchwork, ggstar, tidyselect, SummarizedExperiment, foreach, treeio (>= 1.17.2), pillar, cli, plyr, dtplyr, ggtreeExtra, data.table Suggests: rmarkdown, prettydoc, testthat, knitr, nlme, phangorn, DECIPHER, randomForest, jsonlite, biomformat, scales, yaml, withr, S4Vectors, purrr, seqmagick, glue, corrr, ggupset, ggVennDiagram, gghalves, ggalluvial (>= 0.11.1), forcats, phyloseq, aplot, ggnewscale, ggside, ggh4x, hopach, parallel, shadowtext, DirichletMultinomial License: GPL (>= 3.0) MD5sum: 80e56ad826837b0f1768e0e30d30ee11 NeedsCompilation: no Title: A comprehensive R package for managing and analyzing microbiome and other ecological data within the tidy framework Description: MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It introduces MPSE class, this make it more interoperable with the existing computing ecosystem. Moreover, it introduces a tidy microbiome data structure paradigm and analysis grammar. It provides a wide variety of microbiome data analysis procedures under the unified and common framework (tidy-like framework). biocViews: Visualization, Microbiome, Software, MultipleComparison, FeatureExtraction Author: Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/MicrobiotaProcess/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues git_url: https://git.bioconductor.org/packages/MicrobiotaProcess git_branch: RELEASE_3_16 git_last_commit: 048ca61 git_last_commit_date: 2023-02-08 Date/Publication: 2023-02-08 source.ver: src/contrib/MicrobiotaProcess_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/MicrobiotaProcess_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.2/MicrobiotaProcess_1.10.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MicrobiotaProcess_1.10.3.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.html vignetteTitles: Introduction to MicrobiotaProcess hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess.R dependencyCount: 101 Package: microRNA Version: 1.56.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 MD5sum: 0db9d6be4a9f7499e050a5737a05848b NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" git_url: https://git.bioconductor.org/packages/microRNA git_branch: RELEASE_3_16 git_last_commit: 61c702f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/microRNA_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/microRNA_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/microRNA_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/microRNA_1.56.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 18 Package: midasHLA Version: 1.6.0 Depends: R (>= 4.1), MultiAssayExperiment (>= 1.8.3) Imports: assertthat (>= 0.2.0), broom (>= 0.5.1), dplyr (>= 0.8.0.1), formattable (>= 0.2.0.1), HardyWeinberg (>= 1.6.3), kableExtra (>= 1.1.0), knitr (>= 1.21), magrittr (>= 1.5), methods, stringi (>= 1.2.4), rlang (>= 0.3.1), S4Vectors (>= 0.20.1), stats, SummarizedExperiment (>= 1.12.0), tibble (>= 2.0.1), utils, qdapTools (>= 1.3.3) Suggests: broom.mixed (>= 0.2.4), cowplot (>= 1.0.0), devtools (>= 2.0.1), ggplot2 (>= 3.1.0), ggpubr (>= 0.2.5), rmarkdown, seqinr (>= 3.4-5), survival (>= 2.43-3), testthat (>= 2.0.1), tidyr (>= 1.1.2) License: MIT + file LICENCE Archs: x64 MD5sum: a077050126dc237d2d1522ccfe471835 NeedsCompilation: no Title: R package for immunogenomics data handling and association analysis Description: MiDAS is a R package for immunogenetics data transformation and statistical analysis. MiDAS accepts input data in the form of HLA alleles and KIR types, and can transform it into biologically meaningful variables, enabling HLA amino acid fine mapping, analyses of HLA evolutionary divergence, KIR gene presence, as well as validated HLA-KIR interactions. Further, it allows comprehensive statistical association analysis workflows with phenotypes of diverse measurement scales. MiDAS closes a gap between the inference of immunogenetic variation and its efficient utilization to make relevant discoveries related to T cell, Natural Killer cell, and disease biology. biocViews: CellBiology, Genetics, StatisticalMethod Author: Christian Hammer [aut], Maciej Migdał [aut, cre] Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/midasHLA git_branch: RELEASE_3_16 git_last_commit: 3dab17a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/midasHLA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/midasHLA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/midasHLA_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/midasHLA_1.6.0.tgz vignettes: vignettes/midasHLA/inst/doc/MiDAS_tutorial.html, vignettes/midasHLA/inst/doc/MiDAS_vignette.html vignetteTitles: MiDAS tutorial, MiDAS quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/midasHLA/inst/doc/MiDAS_tutorial.R, vignettes/midasHLA/inst/doc/MiDAS_vignette.R dependencyCount: 109 Package: MIGSA Version: 1.21.0 Depends: R (>= 3.4), methods, BiocGenerics Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table, edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph, graphics, grDevices, grid, GSEABase, ismev, jsonlite, limma, matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, stats, utils, vegan Suggests: BiocStyle, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, knitr, mGSZ, MIGSAdata, RUnit License: GPL (>= 2) MD5sum: f2db9241a0426842261c38f9399da681 NeedsCompilation: no Title: Massive and Integrative Gene Set Analysis Description: Massive and Integrative Gene Set Analysis. The MIGSA package allows to perform a massive and integrative gene set analysis over several expression and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of the best available methods, i.e. dEnricher and mGSZ respectively. The greatest strengths of this big omics data tool are the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. MIGSA package also provides several functions that allow to easily load the most updated gene sets from several repositories. biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression, Microarray, RNASeq, KEGG Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez Maintainer: Juan C. Rodriguez URL: https://github.com/jcrodriguez1989/MIGSA/ BugReports: https://github.com/jcrodriguez1989/MIGSA/issues git_url: https://git.bioconductor.org/packages/MIGSA git_branch: master git_last_commit: 6ecff2d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/MIGSA_1.21.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MIGSA_1.21.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MIGSA_1.21.0.tgz vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf, vignettes/MIGSA/inst/doc/gettingTcgaData.pdf, vignettes/MIGSA/inst/doc/MIGSA.pdf vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive and Integrative Gene Set Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R, vignettes/MIGSA/inst/doc/gettingTcgaData.R, vignettes/MIGSA/inst/doc/MIGSA.R dependencyCount: 108 Package: miloR Version: 1.6.0 Depends: R (>= 4.0.0), edgeR Imports: BiocNeighbors, BiocGenerics, SingleCellExperiment, Matrix (>= 1.3-0), S4Vectors, stats, stringr, methods, igraph, irlba, cowplot, BiocParallel, BiocSingular, limma, ggplot2, tibble, matrixStats, ggraph, gtools, SummarizedExperiment, patchwork, tidyr, dplyr, ggrepel, ggbeeswarm, RColorBrewer, grDevices Suggests: testthat, MASS, mvtnorm, scater, scran, covr, knitr, rmarkdown, uwot, scuttle, BiocStyle, MouseGastrulationData, MouseThymusAgeing, magick, RCurl, curl, graphics License: GPL-3 + file LICENSE MD5sum: 4a6b1d138e8b16026e1d8328a92f81f1 NeedsCompilation: no Title: Differential neighbourhood abundance testing on a graph Description: Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using a negative bionomial generalized linear model. biocViews: SingleCell, MultipleComparison, FunctionalGenomics, Software Author: Mike Morgan [aut, cre], Emma Dann [aut, ctb] Maintainer: Mike Morgan URL: https://marionilab.github.io/miloR VignetteBuilder: knitr BugReports: https://github.com/MarioniLab/miloR/issues git_url: https://git.bioconductor.org/packages/miloR git_branch: RELEASE_3_16 git_last_commit: 9f2b6cb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miloR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miloR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miloR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miloR_1.6.0.tgz vignettes: vignettes/miloR/inst/doc/milo_contrasts.html, vignettes/miloR/inst/doc/milo_demo.html, vignettes/miloR/inst/doc/milo_gastrulation.html vignetteTitles: Using contrasts for differential abundance testing, Differential abundance testing with Milo, Differential abundance testing with Milo - Mouse gastrulation example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miloR/inst/doc/milo_contrasts.R, vignettes/miloR/inst/doc/milo_demo.R, vignettes/miloR/inst/doc/milo_gastrulation.R dependencyCount: 101 Package: mimager Version: 1.22.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: cecaf443f618e6d9b68cc808df98807f NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: RELEASE_3_16 git_last_commit: e18736e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mimager_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mimager_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mimager_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mimager_1.22.0.tgz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 66 Package: MIMOSA Version: 1.36.0 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, dplyr, tidyr, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: MIT + file LICENSE MD5sum: b674eccd06b07947d8ba2620a3398c48 NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MIMOSA git_branch: RELEASE_3_16 git_last_commit: aedd0eb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MIMOSA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MIMOSA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MIMOSA_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MIMOSA_1.36.0.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R dependencyCount: 91 Package: mina Version: 1.6.0 Depends: R (>= 4.0.0) Imports: methods, stats, Rcpp, MCL, RSpectra, apcluster, bigmemory, foreach, ggplot2, parallel, parallelDist, reshape2, plyr, biganalytics, stringr, Hmisc, utils LinkingTo: Rcpp, RcppParallel, RcppArmadillo Suggests: knitr, rmarkdown Enhances: doMC License: GPL Archs: x64 MD5sum: cbd92e6ffc537bc52b3a9137245cff0e NeedsCompilation: yes Title: Microbial community dIversity and Network Analysis Description: An increasing number of microbiome datasets have been generated and analyzed with the help of rapidly developing sequencing technologies. At present, analysis of taxonomic profiling data is mainly conducted using composition-based methods, which ignores interactions between community members. Besides this, a lack of efficient ways to compare microbial interaction networks limited the study of community dynamics. To better understand how community diversity is affected by complex interactions between its members, we developed a framework (Microbial community dIversity and Network Analysis, mina), a comprehensive framework for microbial community diversity analysis and network comparison. By defining and integrating network-derived community features, we greatly reduce noise-to-signal ratio for diversity analyses. A bootstrap and permutation-based method was implemented to assess community network dissimilarities and extract discriminative features in a statistically principled way. biocViews: Software, WorkflowStep Author: Rui Guan [aut, cre], Ruben Garrido-Oter [ctb] Maintainer: Rui Guan VignetteBuilder: knitr BugReports: https://github.com/Guan06/mina git_url: https://git.bioconductor.org/packages/mina git_branch: RELEASE_3_16 git_last_commit: 10ef6e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mina_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mina_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mina_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mina_1.6.0.tgz vignettes: vignettes/mina/inst/doc/mina.html vignetteTitles: Microbial dIversity and Network Analysis with MINA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mina/inst/doc/mina.R dependencyCount: 96 Package: MineICA Version: 1.38.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX, future, future.apply Enhances: doMC License: GPL-2 Archs: x64 MD5sum: 186df2862fd0a00c5177bcc091980aef NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton git_url: https://git.bioconductor.org/packages/MineICA git_branch: RELEASE_3_16 git_last_commit: 4d1fbc6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MineICA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MineICA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MineICA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MineICA_1.38.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R dependencyCount: 212 Package: minet Version: 3.56.0 Imports: infotheo License: Artistic-2.0 MD5sum: 03df10c3ad62a7fd0f27d9ebc9965370 NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: RELEASE_3_16 git_last_commit: 53e597c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/minet_3.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/minet_3.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/minet_3.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/minet_3.56.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: BioNERO, coexnet, epiNEM, netOmics, RTN, PRANA, TGS suggestsMe: CNORfeeder, TCGAbiolinks, dnapath, MACP, WGCNA dependencyCount: 1 Package: minfi Version: 1.44.0 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.15.16), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 Archs: x64 MD5sum: 890d1f3cbb088914bc0db16d0e178b27 NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: RELEASE_3_16 git_last_commit: 7c89fef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/minfi_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/minfi_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/minfi_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/minfi_1.44.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, conumee, methylumi, REMP, shinyMethyl, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICanno.ilm10b3.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, BeadSorted.Saliva.EPIC, FlowSorted.Blood.450k, FlowSorted.Blood.EPIC, FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k, FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k, minfiData, minfiDataEPIC, methylationArrayAnalysis importsMe: deconvR, DMRcate, epimutacions, funtooNorm, MEAL, MEAT, MethylAid, methylCC, methylclock, methylumi, missMethyl, quantro, recountmethylation, shinyepico, skewr, EMAS suggestsMe: epivizr, epivizrChart, Harman, mCSEA, MultiDataSet, planet, RnBeads, brgedata, epimutacionsData, GSE159526, MLML2R dependencyCount: 139 Package: MinimumDistance Version: 1.42.0 Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: 65b928725f3e52f7530b8227e2d32266 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: RELEASE_3_16 git_last_commit: 9206e15 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MinimumDistance_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MinimumDistance_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MinimumDistance_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MinimumDistance_1.42.0.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 87 Package: MiPP Version: 1.70.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 0122b8eb3ab46046b573e6758a4b17d9 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: RELEASE_3_16 git_last_commit: 10dcd13 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MiPP_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MiPP_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MiPP_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MiPP_1.70.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 11 Package: miQC Version: 1.6.0 Depends: R (>= 3.5.0) Imports: SingleCellExperiment, flexmix, ggplot2, splines Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 02f1277f05c5eb3c8ec7b5d34e92fc9b NeedsCompilation: no Title: Flexible, probabilistic metrics for quality control of scRNA-seq data Description: Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing, Sequencing Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut] Maintainer: Ariel Hippen URL: https://github.com/greenelab/miQC VignetteBuilder: knitr BugReports: https://github.com/greenelab/miQC/issues git_url: https://git.bioconductor.org/packages/miQC git_branch: RELEASE_3_16 git_last_commit: de1de6e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miQC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miQC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miQC_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miQC_1.6.0.tgz vignettes: vignettes/miQC/inst/doc/miQC.html vignetteTitles: miQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miQC/inst/doc/miQC.R dependencyCount: 56 Package: MIRA Version: 1.20.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 1ffa63e33dedb1aed2e839e278a8a867 NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA git_url: https://git.bioconductor.org/packages/MIRA git_branch: RELEASE_3_16 git_last_commit: 167d3f7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MIRA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MIRA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MIRA_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MIRA_1.20.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R importsMe: COCOA dependencyCount: 91 Package: MiRaGE Version: 1.40.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: 7c5dc2365d050d540270cbf2b96ada3d NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: RELEASE_3_16 git_last_commit: b2cb6be git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MiRaGE_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MiRaGE_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MiRaGE_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MiRaGE_1.40.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 47 Package: miRBaseConverter Version: 1.22.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils, rmarkdown License: GPL (>= 2) MD5sum: c022a54c4fbfe63559302943a5449416 NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: RELEASE_3_16 git_last_commit: 87f7042 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRBaseConverter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRBaseConverter_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRBaseConverter_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRBaseConverter_1.22.0.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R importsMe: SPONGE, ExpHunterSuite dependencyCount: 1 Package: miRcomp Version: 1.28.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: f96d242d0f8f015b23a865562733fe89 NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: RELEASE_3_16 git_last_commit: 80c09e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRcomp_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRcomp_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRcomp_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRcomp_1.28.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 8 Package: mirIntegrator Version: 1.28.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: fe3209f40cf22714653bf412c94a62b7 NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: RELEASE_3_16 git_last_commit: 1b280aa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mirIntegrator_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mirIntegrator_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mirIntegrator_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mirIntegrator_1.28.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 76 Package: miRLAB Version: 1.28.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit,rmarkdown License: GPL (>=2) MD5sum: 618d6e5f7e86ae9ad92a66f48fefb075 NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_16 git_last_commit: 5df160d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRLAB_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRLAB_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRLAB_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRLAB_1.28.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R dependencyCount: 192 Package: miRmine Version: 1.20.0 Depends: R (>= 3.5.0), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: 799db6e6ba77d99fbbefb76fa25e2f38 NeedsCompilation: no Title: Data package with miRNA-seq datasets from miRmine database as RangedSummarizedExperiment Description: miRmine database is a collection of expression profiles from different publicly available miRNA-seq datasets, Panwar et al (2017) miRmine: A Database of Human miRNA Expression, prepared with this data package as RangedSummarizedExperiment. biocViews: Homo_sapiens_Data, RNASeqData, SequencingData, ExpressionData Author: Dusan Randjelovic [aut, cre] Maintainer: Dusan Randjelovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRmine git_branch: RELEASE_3_16 git_last_commit: 92c5d6a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRmine_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRmine_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRmine_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRmine_1.20.0.tgz vignettes: vignettes/miRmine/inst/doc/miRmine.html vignetteTitles: miRmine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRmine/inst/doc/miRmine.R dependencyCount: 25 Package: miRNAmeConverter Version: 1.26.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: d5d941c00a45e71aa7950bed9cc11275 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: RELEASE_3_16 git_last_commit: 0dc67ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRNAmeConverter_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRNAmeConverter_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRNAmeConverter_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRNAmeConverter_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 54 Package: miRNApath Version: 1.58.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: aef82306383b07fc57ff69eb3aa57fa8 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: RELEASE_3_16 git_last_commit: a22c734 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRNApath_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRNApath_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRNApath_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRNApath_1.58.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.32.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: 0adca963228232d3eed8230401ebfad0 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: RELEASE_3_16 git_last_commit: f0aaaa6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRNAtap_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRNAtap_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRNAtap_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRNAtap_1.32.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R dependsOnMe: miRNAtap.db importsMe: SpidermiR, miRNAtap.db dependencyCount: 55 Package: miRSM Version: 1.16.0 Depends: R (>= 3.5.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL, NMF, biclust, iBBiG, fabia, BicARE, isa2, s4vd, BiBitR, rqubic, Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero, ppclust, miRspongeR, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: d889bed7ea716fb1c1d93e736b197fa4 NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge modules in heterogeneous data. It provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: RELEASE_3_16 git_last_commit: b8f0942 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRSM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRSM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRSM_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRSM_1.16.0.tgz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 365 Package: miRspongeR Version: 2.2.0 Depends: R (>= 3.5.0) Imports: corpcor, SPONGE, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, linkcomm, utils, Rcpp, org.Hs.eg.db, foreach, doParallel Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 02afd81cc904ec994a3d34b7dee0247c NeedsCompilation: yes Title: Identification and analysis of miRNA sponge regulation Description: This package provides several functions to explore miRNA sponge (also called ceRNA or miRNA decoy) regulation from putative miRNA-target interactions or/and transcriptomics data (including bulk, single-cell and spatial gene expression data). It provides eight popular methods for identifying miRNA sponge interactions, and an integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of miRNA sponge modules, and conduct survival analysis of miRNA sponge modules. By using a sample control variable strategy, it provides a function to infer sample-specific miRNA sponge interactions. In terms of sample-specific miRNA sponge interactions, it implements three similarity methods to construct sample-sample correlation network. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software, SingleCell, Spatial, RNASeq Author: Junpeng Zhang Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: RELEASE_3_16 git_last_commit: 4988a6b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/miRspongeR_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/miRspongeR_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/miRspongeR_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/miRspongeR_2.2.0.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: Identification and analysis of miRNA sponge regulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R importsMe: miRSM dependencyCount: 285 Package: mirTarRnaSeq Version: 1.6.0 Depends: R (>= 4.1.0), ggplot2 Imports: purrr, MASS, pscl, assertthat, caTools, dplyr, pheatmap, reshape2, corrplot, grDevices, graphics, stats, utils, data.table, R.utils Suggests: BiocStyle, knitr, rmarkdown, R.cache, SPONGE License: MIT + file LICENSE Archs: x64 MD5sum: 816b561f506262e174f4077f7288893a NeedsCompilation: no Title: mirTarRnaSeq Description: mirTarRnaSeq R package can be used for interactive mRNA miRNA sequencing statistical analysis. This package utilizes expression or differential expression mRNA and miRNA sequencing results and performs interactive correlation and various GLMs (Regular GLM, Multivariate GLM, and Interaction GLMs ) analysis between mRNA and miRNA expriments. These experiments can be time point experiments, and or condition expriments. biocViews: miRNA, Regression, Software, Sequencing, SmallRNA, TimeCourse, DifferentialExpression Author: Mercedeh Movassagh [aut, cre] (), Sarah Morton [aut], Rafael Irizarry [aut], Jeffrey Bailey [aut], Joseph N Paulson [aut] Maintainer: Mercedeh Movassagh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mirTarRnaSeq git_branch: RELEASE_3_16 git_last_commit: 12bc350 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mirTarRnaSeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mirTarRnaSeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mirTarRnaSeq_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mirTarRnaSeq_1.6.0.tgz vignettes: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.pdf vignetteTitles: mirTarRnaSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mirTarRnaSeq/inst/doc/mirTarRnaSeq.R dependencyCount: 56 Package: missMethyl Version: 1.32.1 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub License: GPL-2 MD5sum: 0e312da3c00ec9d012e93bd39b55f47c NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: RELEASE_3_16 git_last_commit: a3328e8 git_last_commit_date: 2023-03-16 Date/Publication: 2023-03-17 source.ver: src/contrib/missMethyl_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/missMethyl_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/missMethyl_1.32.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/missMethyl_1.32.1.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R dependsOnMe: methylationArrayAnalysis importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 164 Package: missRows Version: 1.18.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 9e8586a06377cbc50361ec3d17546891 NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: RELEASE_3_16 git_last_commit: d2ab643 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/missRows_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/missRows_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/missRows_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/missRows_1.18.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 65 Package: mistyR Version: 1.6.1 Depends: R (>= 4.0) Imports: assertthat, caret, deldir, digest, distances, dplyr, filelock, furrr (>= 0.2.0), ggplot2, methods, purrr, ranger, readr (>= 2.0.0), ridge, rlang, rlist, R.utils, stats, stringr, tibble, tidyr, tidyselect (>= 1.2.0), utils, withr Suggests: BiocStyle, covr, earth, future, igraph (>= 1.2.7), iml, kernlab, knitr, MASS, rmarkdown, RSNNS, testthat (>= 3.0.0), xgboost License: GPL-3 MD5sum: dcbffd5985755f9fa83c80ef923808d9 NeedsCompilation: no Title: Multiview Intercellular SpaTial modeling framework Description: mistyR is an implementation of the Multiview Intercellular SpaTialmodeling framework (MISTy). MISTy is an explainable machine learning framework for knowledge extraction and analysis of single-cell, highly multiplexed, spatially resolved data. MISTy facilitates an in-depth understanding of marker interactions by profiling the intra- and intercellular relationships. MISTy is a flexible framework able to process a custom number of views. Each of these views can describe a different spatial context, i.e., define a relationship among the observed expressions of the markers, such as intracellular regulation or paracrine regulation, but also, the views can also capture cell-type specific relationships, capture relations between functional footprints or focus on relations between different anatomical regions. Each MISTy view is considered as a potential source of variability in the measured marker expressions. Each MISTy view is then analyzed for its contribution to the total expression of each marker and is explained in terms of the interactions with other measurements that led to the observed contribution. biocViews: Software, BiomedicalInformatics, CellBiology, SystemsBiology, Regression, DecisionTree, SingleCell, Spatial Author: Jovan Tanevski [cre, aut] (), Ricardo Omar Ramirez Flores [ctb] (), Philipp Schäfer [ctb] Maintainer: Jovan Tanevski URL: https://saezlab.github.io/mistyR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/mistyR/issues git_url: https://git.bioconductor.org/packages/mistyR git_branch: RELEASE_3_16 git_last_commit: 23b3fdf git_last_commit_date: 2023-02-20 Date/Publication: 2023-02-20 source.ver: src/contrib/mistyR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/mistyR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/mistyR_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mistyR_1.6.1.tgz vignettes: vignettes/mistyR/inst/doc/mistyR.html vignetteTitles: Getting started hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mistyR/inst/doc/mistyR.R dependencyCount: 110 Package: mitch Version: 1.10.0 Depends: R (>= 4.0) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r Suggests: stringi, testthat (>= 2.1.0) License: CC BY-SA 4.0 + file LICENSE MD5sum: 61b7f17ae9e8a64a0dd745d9aa1c6608 NeedsCompilation: no Title: Multi-Contrast Gene Set Enrichment Analysis Description: mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments. biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome Author: Mark Ziemann [aut, cre, cph], Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitch git_branch: RELEASE_3_16 git_last_commit: 5873999 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mitch_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mitch_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mitch_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mitch_1.10.0.tgz vignettes: vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 98 Package: mitoClone2 Version: 1.4.0 Depends: R (>= 4.1.0) Imports: reshape2, GenomicRanges, pheatmap, deepSNV, grDevices, graphics, stats, utils, S4Vectors, Rhtslib, parallel, methods, ggplot2 LinkingTo: Rhtslib (>= 1.13.1) Suggests: knitr, rmarkdown, Biostrings, testthat License: GPL-3 MD5sum: fdfd4eb3ec7d37bca9f071aaaf98be54 NeedsCompilation: yes Title: Clonal Population Identification in Single-Cell RNA-Seq Data using Mitochondrial and Somatic Mutations Description: This package primarily identifies variants in mitochondrial genomes from BAM alignment files. It filters these variants to remove RNA editing events then estimates their evolutionary relationship (i.e. their phylogenetic tree) and groups single cells into clones. It also visualizes the mutations and providing additional genomic context. biocViews: Annotation, DataImport, Genetics, SNP, Software, SingleCell, Alignment Author: Benjamin Story [aut, cre], Lars Velten [aut], Gregor Mönke [aut] Maintainer: Benjamin Story URL: https://github.com/benstory/mitoClone2 SystemRequirements: GNU make, PhISCS (optional) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitoClone2 git_branch: RELEASE_3_16 git_last_commit: 77f24af git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mitoClone2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mitoClone2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mitoClone2_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mitoClone2_1.4.0.tgz vignettes: vignettes/mitoClone2/inst/doc/clustering.html, vignettes/mitoClone2/inst/doc/overview.html vignetteTitles: Computation of phylogenetic trees and clustering of mutations, Variant Calling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoClone2/inst/doc/clustering.R, vignettes/mitoClone2/inst/doc/overview.R dependencyCount: 118 Package: mixOmics Version: 6.22.0 Depends: R (>= 3.5.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rgl License: GPL (>= 2) Archs: x64 MD5sum: aa1c36cbe7e8e0a7a37083ab74944f79 NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al J Abadi [ctb, cre], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb] Maintainer: Al J Abadi URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: RELEASE_3_16 git_last_commit: e151cec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mixOmics_6.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mixOmics_6.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mixOmics_6.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mixOmics_6.22.0.tgz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: timeOmics, mixKernel, sgPLS importsMe: AlpsNMR, DepecheR, multiSight, POMA, MetabolomicsBasics, MSclassifR, plsmod, plsRcox, RVAideMemoire, SISIR suggestsMe: autonomics, netOmics, SelectBoost, sharp dependencyCount: 65 Package: MLInterfaces Version: 1.78.0 Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats, magrittr, SummarizedExperiment Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, klaR, testthat Enhances: parallel License: LGPL MD5sum: 536741263f88a52f9a393d5859e5442d NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_16 git_last_commit: 0988b95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MLInterfaces_1.78.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MLInterfaces_1.78.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MLInterfaces_1.78.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MLInterfaces_1.78.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, SigCheck, dGAselID, nlcv dependencyCount: 125 Package: MLP Version: 1.46.0 Imports: AnnotationDbi, gplots, graphics, stats, utils Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, org.Mmu.eg.db, KEGGREST, annotate, Rgraphviz, GOstats, graph, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 844ae3d974c32f6defd6a7dd29bff58f NeedsCompilation: no Title: Mean Log P Analysis Description: Pathway analysis based on p-values associated to genes from a genes expression analysis of interest. Utility functions enable to extract pathways from the Gene Ontology Biological Process (GOBP), Molecular Function (GOMF) and Cellular Component (GOCC), Kyoto Encyclopedia of Genes of Genomes (KEGG) and Reactome databases. Methodology, and helper functions to display the results as a table, barplot of pathway significance, Gene Ontology graph and pathway significance are available. biocViews: Genetics, GeneExpression, Pathways, Reactome, KEGG, GO Author: Nandini Raghavan [aut], Tobias Verbeke [aut], An De Bondt [aut], Javier Cabrera [ctb], Dhammika Amaratunga [ctb], Tine Casneuf [ctb], Willem Ligtenberg [ctb], Laure Cougnaud [cre], Katarzyna Gorczak [ctb] Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_16 git_last_commit: fad144e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MLP_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MLP_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MLP_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MLP_1.46.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 50 Package: MLSeq Version: 2.16.0 Depends: caret, ggplot2 Imports: testthat, VennDiagram, pamr, methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, e1071, kernlab License: GPL(>=2) MD5sum: 8ebfbcfbb6fa1c9a7e853b284b106af6 NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: RELEASE_3_16 git_last_commit: 42c5e33 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MLSeq_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MLSeq_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MLSeq_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MLSeq_2.16.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 157 Package: MMAPPR2 Version: 1.12.0 Depends: R (>= 3.6.0) Imports: ensemblVEP (>= 1.20.0), gmapR, Rsamtools, VariantAnnotation, BiocParallel, Biobase, BiocGenerics, dplyr, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, tidyr, VariantTools, magrittr, methods, grDevices, graphics, stats, utils, stringr, data.table Suggests: testthat, mockery, roxygen2, knitr, rmarkdown, BiocStyle, MMAPPR2data License: GPL-3 OS_type: unix MD5sum: d35dd8072d88eebf402eff1483bcd724 NeedsCompilation: no Title: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq Description: MMAPPR2 maps mutations resulting from pooled RNA-seq data from the F2 cross of forward genetic screens. Its predecessor is described in a paper published in Genome Research (Hill et al. 2013). MMAPPR2 accepts aligned BAM files as well as a reference genome as input, identifies loci of high sequence disparity between the control and mutant RNA sequences, predicts variant effects using Ensembl's Variant Effect Predictor, and outputs a ranked list of candidate mutations. biocViews: RNASeq, PooledScreens, DNASeq, VariantDetection Author: Kyle Johnsen [aut], Nathaniel Jenkins [aut], Jonathon Hill [cre] Maintainer: Jonathon Hill URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613585/, https://github.com/kjohnsen/MMAPPR2 SystemRequirements: Ensembl VEP, Samtools VignetteBuilder: knitr BugReports: https://github.com/kjohnsen/MMAPPR2/issues git_url: https://git.bioconductor.org/packages/MMAPPR2 git_branch: RELEASE_3_16 git_last_commit: d984d12 git_last_commit_date: 2022-11-01 Date/Publication: 2022-12-09 source.ver: src/contrib/MMAPPR2_1.12.0.tar.gz vignettes: vignettes/MMAPPR2/inst/doc/MMAPPR2.html vignetteTitles: An Introduction to MMAPPR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMAPPR2/inst/doc/MMAPPR2.R dependencyCount: 102 Package: MMDiff2 Version: 1.26.0 Depends: R (>= 3.5.0), Rsamtools, Biobase Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: 36025b6b0186326f0c2cd3820d04b800 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: RELEASE_3_16 git_last_commit: 662ecc9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MMDiff2_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MMDiff2_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MMDiff2_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MMDiff2_1.26.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R suggestsMe: MMDiffBamSubset dependencyCount: 98 Package: MMUPHin Version: 1.12.1 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, stringr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: e6dd325875529debae9076b245ee0a97 NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA SystemRequirements: glpk (>= 4.57) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: RELEASE_3_16 git_last_commit: 6046c20 git_last_commit_date: 2023-02-16 Date/Publication: 2023-02-17 source.ver: src/contrib/MMUPHin_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MMUPHin_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MMUPHin_1.12.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MMUPHin_1.12.1.tgz vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html vignetteTitles: MMUPHin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R dependencyCount: 149 Package: mnem Version: 1.14.0 Depends: R (>= 4.1) Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices, e1071, ggplot2, wesanderson LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM License: GPL-3 MD5sum: d834503c0fd1ecfdb2b49da56b2d980d NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/mnem/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/mnem/issues git_url: https://git.bioconductor.org/packages/mnem git_branch: RELEASE_3_16 git_last_commit: 4f77e45 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mnem_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mnem_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mnem_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mnem_1.14.0.tgz vignettes: vignettes/mnem/inst/doc/mnem.html vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R dependsOnMe: nempi importsMe: bnem, dce, epiNEM dependencyCount: 82 Package: moanin Version: 1.6.0 Depends: R (>= 4.0), SummarizedExperiment, topGO, stats Imports: S4Vectors, MASS (>= 1.0.0), limma, viridis, edgeR, graphics, methods, grDevices, reshape2, NMI, zoo, ClusterR, splines, matrixStats Suggests: testthat (>= 1.0.0), timecoursedata, knitr, rmarkdown, markdown, covr, BiocStyle License: BSD 3-clause License + file LICENSE Archs: x64 MD5sum: 9c1b7197cfc9b9aa4f3441a6db647ba9 NeedsCompilation: no Title: An R Package for Time Course RNASeq Data Analysis Description: Simple and efficient workflow for time-course gene expression data, built on publictly available open-source projects hosted on CRAN and bioconductor. moanin provides helper functions for all the steps required for analysing time-course data using functional data analysis: (1) functional modeling of the timecourse data; (2) differential expression analysis; (3) clustering; (4) downstream analysis. biocViews: TimeCourse, GeneExpression, RNASeq, Microarray, DifferentialExpression, Clustering Author: Elizabeth Purdom [aut] (), Nelle Varoquaux [aut, cre] () Maintainer: Nelle Varoquaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/moanin git_branch: RELEASE_3_16 git_last_commit: 02230d6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/moanin_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/moanin_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/moanin_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/moanin_1.6.0.tgz vignettes: vignettes/moanin/inst/doc/documentation.html vignetteTitles: The Moanin Package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/moanin/inst/doc/documentation.R dependencyCount: 93 Package: MobilityTransformR Version: 1.2.0 Depends: MSnbase, R (>= 4.2) Imports: xcms, MetaboCoreUtils, Spectra Suggests: testthat, msdata (>= 0.35.3), knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: 81d5f77198e6db54c7301db6a87354fe NeedsCompilation: no Title: Effective mobility scale transformation of CE-MS(/MS) data Description: MobilityTransformR collects a tool set for effective mobility scale transformation of CE-MS/MS data in order to increase reproducibility. It provides functionality to determine the migration times from mobility markers that have been added to the analysis and performs the transformation based on these markers. MobilityTransformR supports the conversion of numeric vectors, Spectra-objects, and MSnOnDiskExp. biocViews: Infrastructure, Metabolomics, MassSpectrometry, Proteomics, Preprocessing Author: Liesa Salzer [cre, aut] () Maintainer: Liesa Salzer URL: https://github.com/LiesaSalzer/MobilityTransformR VignetteBuilder: knitr BugReports: https://github.com/LiesaSalzer/MobilityTransformR/issues git_url: https://git.bioconductor.org/packages/MobilityTransformR git_branch: RELEASE_3_16 git_last_commit: 4ce61cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MobilityTransformR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MobilityTransformR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MobilityTransformR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MobilityTransformR_1.2.0.tgz vignettes: vignettes/MobilityTransformR/inst/doc/MobilityTransformR.html vignetteTitles: Description and usage of MobilityTransformR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MobilityTransformR/inst/doc/MobilityTransformR.R dependencyCount: 96 Package: MODA Version: 1.24.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 863743b3386a4fdece7e786d5f1f7fc9 NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: RELEASE_3_16 git_last_commit: 56decf3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MODA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MODA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MODA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MODA_1.24.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 118 Package: ModCon Version: 1.6.0 Depends: data.table, parallel, utils, stats, R (>= 4.1) Suggests: testthat, knitr, rmarkdown, dplyr, shinycssloaders, shiny, shinyFiles, shinydashboard, shinyjs License: GPL-3 + file LICENSE MD5sum: c90f43a74da5a77b665f2c4a2cca97fa NeedsCompilation: no Title: Modifying splice site usage by changing the mRNP code, while maintaining the genetic code Description: Collection of functions to calculate a nucleotide sequence surrounding for splice donors sites to either activate or repress donor usage. The proposed alternative nucleotide sequence encodes the same amino acid and could be applied e.g. in reporter systems to silence or activate cryptic splice donor sites. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] () Maintainer: Johannes Ptok URL: https://github.com/caggtaagtat/ModCon SystemRequirements: Perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ModCon git_branch: RELEASE_3_16 git_last_commit: b211455 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ModCon_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ModCon_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ModCon_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ModCon_1.6.0.tgz vignettes: vignettes/ModCon/inst/doc/ModCon.html vignetteTitles: Designing SD context with ModCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ModCon/inst/doc/ModCon.R dependencyCount: 5 Package: Modstrings Version: 1.14.0 Depends: R (>= 3.6), Biostrings (>= 2.51.5) Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr, crayon, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: 2659416d5be20fde27bcab5b7ee37aed NeedsCompilation: no Title: Working with modified nucleotide sequences Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: RELEASE_3_16 git_last_commit: 7d268fc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Modstrings_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Modstrings_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Modstrings_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Modstrings_1.14.0.tgz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport importsMe: tRNA suggestsMe: EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3 dependencyCount: 27 Package: MOFA2 Version: 1.8.0 Depends: R (>= 4.0) Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi Suggests: knitr, testthat, Seurat, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown License: file LICENSE MD5sum: 07111554b80efb5a6f13afab3908dcab NeedsCompilation: yes Title: Multi-Omics Factor Analysis v2 Description: The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet [aut, cre] (), Damien Arnol [aut] (), Danila Bredikhin [aut] (), Britta Velten [aut] () Maintainer: Ricard Argelaguet URL: https://biofam.github.io/MOFA2/index.html SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 VignetteBuilder: knitr BugReports: https://github.com/bioFAM/MOFA2 git_url: https://git.bioconductor.org/packages/MOFA2 git_branch: RELEASE_3_16 git_last_commit: a1ef44d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MOFA2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOFA2_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOFA2_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MOFA2_1.8.0.tgz vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html, vignettes/MOFA2/inst/doc/getting_started_R.html, vignettes/MOFA2/inst/doc/MEFISTO_temporal.html vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a model in R, MEFISTO on simulated data (temporal) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R, vignettes/MOFA2/inst/doc/getting_started_R.R, vignettes/MOFA2/inst/doc/MEFISTO_temporal.R dependencyCount: 84 Package: MOGAMUN Version: 1.8.0 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: knitr, markdown License: GPL-3 + file LICENSE MD5sum: 2650c1d882e64ff1fed5a46afc552f87 NeedsCompilation: no Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks Description: MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks. biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression, BiomedicalInformatics, Transcriptomics, Clustering, Network Author: Elva-María Novoa-del-Toro [aut, cre] () Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: RELEASE_3_16 git_last_commit: dfe49f8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MOGAMUN_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOGAMUN_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOGAMUN_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MOGAMUN_1.8.0.tgz vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html vignetteTitles: Finding active modules with MOGAMUN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R dependencyCount: 70 Package: mogsa Version: 1.32.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 MD5sum: f5a3ac58634e270b8b16a5a114f74d3d NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: RELEASE_3_16 git_last_commit: adf4719 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mogsa_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mogsa_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mogsa_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mogsa_1.32.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 69 Package: MOMA Version: 1.10.0 Depends: R (>= 4.0) Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics, grid, grDevices, magrittr, methods, MKmisc, MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, viper License: GPL-3 MD5sum: 12880abd652cc5fd253beb8846ebdc16 NeedsCompilation: no Title: Multi Omic Master Regulator Analysis Description: This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions. biocViews: Software, NetworkEnrichment, NetworkInference, Network, FeatureExtraction, Clustering, FunctionalGenomics, Transcriptomics, SystemsBiology Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut] Maintainer: Sunny Jones VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: RELEASE_3_16 git_last_commit: bea1295 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MOMA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOMA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOMA_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MOMA_1.10.0.tgz vignettes: vignettes/MOMA/inst/doc/moma.html vignetteTitles: MOMA - Multi Omic Master Regulator Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOMA/inst/doc/moma.R dependencyCount: 95 Package: monaLisa Version: 1.4.0 Depends: R (>= 4.1) Imports: methods, stats, utils, grDevices, graphics, BiocGenerics, GenomicRanges, TFBSTools, Biostrings, IRanges, stabs, BSgenome, glmnet, S4Vectors, SummarizedExperiment, BiocParallel, grid, circlize, ComplexHeatmap (>= 2.11.1), XVector, GenomeInfoDb, tools, vioplot Suggests: JASPAR2020, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, knitr, rmarkdown, testthat, BiocStyle, gridExtra License: GPL (>= 3) MD5sum: 9a731daa69f1272f7c2182d52398c7bf NeedsCompilation: no Title: Binned Motif Enrichment Analysis and Visualization Description: Useful functions to work with sequence motifs in the analysis of genomics data. These include methods to annotate genomic regions or sequences with predicted motif hits and to identify motifs that drive observed changes in accessibility or expression. Functions to produce informative visualizations of the obtained results are also provided. biocViews: MotifAnnotation, Visualization, FeatureExtraction, Epigenetics Author: Dania Machlab [aut] (), Lukas Burger [aut], Charlotte Soneson [aut] (), Michael Stadler [aut, cre] () Maintainer: Michael Stadler URL: https://github.com/fmicompbio/monaLisa, https://bioconductor.org/packages/monaLisa/, https://fmicompbio.github.io/monaLisa/ VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/monaLisa/issues git_url: https://git.bioconductor.org/packages/monaLisa git_branch: RELEASE_3_16 git_last_commit: 337acef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/monaLisa_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/monaLisa_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/monaLisa_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/monaLisa_1.4.0.tgz vignettes: vignettes/monaLisa/inst/doc/monaLisa.html, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.html vignetteTitles: monaLisa - MOtif aNAlysis with Lisa, selecting_motifs_with_randLassoStabSel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monaLisa/inst/doc/monaLisa.R, vignettes/monaLisa/inst/doc/selecting_motifs_with_randLassoStabSel.R dependencyCount: 139 Package: monocle Version: 2.26.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, Rtsne, MASS, reshape2, leidenbase (>= 0.1.9), limma, tibble, dplyr, qlcMatrix, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 MD5sum: af246fb6ee2b887d62a568245dc47c7e NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: RELEASE_3_16 git_last_commit: ec2dcba git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/monocle_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/monocle_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/monocle_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/monocle_2.26.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero, phemd importsMe: uSORT suggestsMe: M3Drop, scran, sincell, grandR, Seurat dependencyCount: 80 Package: MoonlightR Version: 1.24.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png License: GPL (>= 3) Archs: x64 MD5sum: 57ce0d4904bbe180444e74d2e35e6b13 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico [aut], Catharina Olsen [aut], Matthew H. Bailey [aut], Gabriel J. Odom [aut], Thilde Terkelsen [aut], Mona Nourbakhsh [aut], Astrid Saksager [aut], Tiago C. Silva [aut], André V. Olsen [aut], Laura Cantini [aut], Andrei Zinovyev [aut], Emmanuel Barillot [aut], Houtan Noushmehr [aut], Gloria Bertoli [aut], Isabella Castiglioni [aut], Claudia Cava [aut], Gianluca Bontempi [aut], Xi Steven Chen [aut], Elena Papaleo [aut], Matteo Tiberti [cre, aut] Maintainer: Matteo Tiberti URL: https://github.com/ELELAB/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ELELAB/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_16 git_last_commit: 4135c48 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MoonlightR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MoonlightR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MoonlightR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MoonlightR_1.24.0.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 190 Package: mosaics Version: 2.36.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) MD5sum: 51393e2b668b86b00ae828177a9e8b35 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/mosaics git_branch: RELEASE_3_16 git_last_commit: 3166c3a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mosaics_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mosaics_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mosaics_2.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mosaics_2.36.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependencyCount: 43 Package: mosbi Version: 1.4.0 Depends: R (>= 4.1) Imports: Rcpp, BH, xml2, methods, igraph, fabia, RcppParallel, biclust, isa2, QUBIC, akmbiclust, RColorBrewer LinkingTo: Rcpp, BH, RcppParallel Suggests: knitr, rmarkdown, BiocGenerics, runibic, BiocStyle, testthat (>= 3.0.0) License: AGPL-3 + file LICENSE Archs: x64 MD5sum: 92da211f59f0b9f354ec33245ca38244 NeedsCompilation: yes Title: Molecular Signature identification using Biclustering Description: This package is a implementation of biclustering ensemble method MoSBi (Molecular signature Identification from Biclustering). MoSBi provides standardized interfaces for biclustering results and can combine their results with a multi-algorithm ensemble approach to compute robust ensemble biclusters on molecular omics data. This is done by computing similarity networks of biclusters and filtering for overlaps using a custom error model. After that, the louvain modularity it used to extract bicluster communities from the similarity network, which can then be converted to ensemble biclusters. Additionally, MoSBi includes several network visualization methods to give an intuitive and scalable overview of the results. MoSBi comes with several biclustering algorithms, but can be easily extended to new biclustering algorithms. biocViews: Software, StatisticalMethod, Clustering, Network Author: Tim Daniel Rose [cre, aut], Josch Konstantin Pauling [aut], Nikolai Koehler [aut] Maintainer: Tim Daniel Rose SystemRequirements: C++17, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mosbi git_branch: RELEASE_3_16 git_last_commit: 5f88068 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mosbi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mosbi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mosbi_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mosbi_1.4.0.tgz vignettes: vignettes/mosbi/inst/doc/example-workflow.html, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.html vignetteTitles: example-workflow, similarity-metrics-evaluation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mosbi/inst/doc/example-workflow.R, vignettes/mosbi/inst/doc/similarity-metrics-evaluation.R dependencyCount: 63 Package: MOSim Version: 1.12.0 Depends: R (>= 3.6) Imports: HiddenMarkov, zoo, methods, matrixStats, dplyr, stringi, lazyeval, rlang, stats, utils, purrr, scales, stringr, tibble, tidyr, ggplot2, Biobase, IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: 885623f05f8aaa564849e4988f49c49b NeedsCompilation: no Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carlos Martínez [cre, aut], Sonia Tarazona [aut] Maintainer: Carlos Martínez URL: https://github.com/Neurergus/MOSim VignetteBuilder: knitr BugReports: https://github.com/Neurergus/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: RELEASE_3_16 git_last_commit: 4a064b1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MOSim_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MOSim_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MOSim_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MOSim_1.12.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.pdf vignetteTitles: MOSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R dependencyCount: 53 Package: Motif2Site Version: 1.2.0 Depends: R (>= 4.1) Imports: S4Vectors, stats, utils, methods, grDevices, graphics, BiocGenerics, BSgenome, GenomeInfoDb, MASS, IRanges, GenomicRanges, Biostrings, GenomicAlignments, edgeR, mixtools Suggests: BiocStyle, rmarkdown, knitr, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Ecoli.NCBI.20080805 License: GPL-2 MD5sum: f0e170e90136ca550329605c691f71d9 NeedsCompilation: no Title: Detect binding sites from motifs and ChIP-seq experiments, and compare binding sites across conditions Description: Detect binding sites using motifs IUPAC sequence or bed coordinates and ChIP-seq experiments in bed or bam format. Combine/compare binding sites across experiments, tissues, or conditions. All normalization and differential steps are done using TMM-GLM method. Signal decomposition is done by setting motifs as the centers of the mixture of normal distribution curves. biocViews: Software, Sequencing, ChIPSeq, DifferentialPeakCalling, Epigenetics, SequenceMatching Author: Peyman Zarrineh [cre, aut] () Maintainer: Peyman Zarrineh VignetteBuilder: knitr BugReports: https://github.com/ManchesterBioinference/Motif2Site/issues git_url: https://git.bioconductor.org/packages/Motif2Site git_branch: RELEASE_3_16 git_last_commit: 6015b74 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Motif2Site_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Motif2Site_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Motif2Site_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Motif2Site_1.2.0.tgz vignettes: vignettes/Motif2Site/inst/doc/Motif2Site.html vignetteTitles: Motif2Site hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Motif2Site/inst/doc/Motif2Site.R dependencyCount: 122 Package: motifbreakR Version: 2.12.3 Depends: R (>= 4.1.0), grid, MotifDb Imports: methods, grDevices, stringr, parallel, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP155.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: f4bae4ef784e838affe4aef7db9ee743 NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 32 species, a total of 109 versions). biocViews: ChIPSeq, Visualization, MotifAnnotation, Transcription Author: Simon Gert Coetzee [aut, cre], Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: RELEASE_3_16 git_last_commit: d04fa91 git_last_commit_date: 2023-03-08 Date/Publication: 2023-03-09 source.ver: src/contrib/motifbreakR_2.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifbreakR_2.12.3.zip mac.binary.ver: bin/macosx/contrib/4.2/motifbreakR_2.12.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/motifbreakR_2.12.3.tgz vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R dependencyCount: 181 Package: motifcounter Version: 1.22.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 MD5sum: 20feebf9276954794856bff8f95a52a6 NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: RELEASE_3_16 git_last_commit: 0144072 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/motifcounter_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifcounter_1.21.0.zip mac.binary.ver: bin/macosx/contrib/4.2/motifcounter_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/motifcounter_1.22.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R dependencyCount: 18 Package: MotifDb Version: 1.40.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown, formatR, markdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes Archs: x64 MD5sum: 3927588cd790546340de008fd2481d98 NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon VignetteBuilder: knitr, rmarkdown, formatR, markdown git_url: https://git.bioconductor.org/packages/MotifDb git_branch: RELEASE_3_16 git_last_commit: 213f1a8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MotifDb_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MotifDb_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MotifDb_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MotifDb_1.40.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.html vignetteTitles: "A collection of PWMs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, trena, generegulation importsMe: rTRMui suggestsMe: ATACseqQC, DiffLogo, enhancerHomologSearch, igvR, memes, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 48 Package: motifmatchr Version: 1.20.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, GenomeInfoDb LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE MD5sum: e934281fb8744d36f255bf4f3d71cd38 NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: RELEASE_3_16 git_last_commit: 3a72a09 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/motifmatchr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifmatchr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/motifmatchr_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/motifmatchr_1.20.0.tgz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: ATACCoGAPS, ATACseqTFEA, enhancerHomologSearch, enrichTF, esATAC, pageRank, spatzie suggestsMe: chromVAR, GRaNIE, MethReg, CAGEWorkflow, MOCHA, Signac dependencyCount: 122 Package: motifStack Version: 1.42.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML, TFBSTools Suggests: grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr, RUnit, rmarkdown, JASPAR2020 License: GPL (>= 2) Archs: x64 MD5sum: 446df0cffd8663471bef7e94735b3b4b NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: RELEASE_3_16 git_last_commit: f7ddd7f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/motifStack_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/motifStack_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/motifStack_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/motifStack_1.42.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: generegulation importsMe: ATACseqQC, atSNP, dagLogo, LowMACA, motifbreakR, ribosomeProfilingQC suggestsMe: ChIPpeakAnno, TFutils, trackViewer, tripr, universalmotif dependencyCount: 142 Package: MouseFM Version: 1.8.0 Depends: R (>= 4.0.0) Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales, gtools, tidyr, data.table, jsonlite, rlist, GenomeInfoDb, methods, biomaRt, stats, IRanges Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 2fc5307376b839f1ba11d13d9bb83806 NeedsCompilation: no Title: In-silico methods for genetic finemapping in inbred mice Description: This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%). biocViews: Genetics, SNP, GeneTarget, VariantAnnotation, GenomicVariation, MultipleComparison, SystemsBiology, MathematicalBiology, PatternLogic, GenePrediction, BiomedicalInformatics, FunctionalGenomics Author: Matthias Munz [aut, cre] (), Inken Wohlers [aut] (), Hauke Busch [aut] () Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: RELEASE_3_16 git_last_commit: 662f69d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MouseFM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MouseFM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MouseFM_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MouseFM_1.8.0.tgz vignettes: vignettes/MouseFM/inst/doc/fetch.html, vignettes/MouseFM/inst/doc/finemap.html, vignettes/MouseFM/inst/doc/prio.html vignetteTitles: Fetch, Finemapping, Prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MouseFM/inst/doc/fetch.R, vignettes/MouseFM/inst/doc/finemap.R, vignettes/MouseFM/inst/doc/prio.R dependencyCount: 95 Package: MPFE Version: 1.34.0 License: GPL (>= 3) MD5sum: e59a18a2268928f9957729c78ebf188a NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: RELEASE_3_16 git_last_commit: 3224538 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MPFE_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MPFE_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MPFE_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MPFE_1.34.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.20.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 9fde05e24c2ebcfb5f64451a50833191 NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: RELEASE_3_16 git_last_commit: 9dad5bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mpra_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mpra_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mpra_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mpra_1.20.0.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 39 Package: MPRAnalyze Version: 1.16.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: 05a78c393a53d5adc5342c8045e6adec NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression, ExperimentalDesign, Classification Author: Tal Ashuach [aut, cre], David S Fischer [aut], Anat Kriemer [ctb], Fabian J Theis [ctb], Nir Yosef [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: RELEASE_3_16 git_last_commit: 3bdb160 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MPRAnalyze_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MPRAnalyze_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MPRAnalyze_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MPRAnalyze_1.16.0.tgz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 46 Package: MQmetrics Version: 1.6.0 Imports: ggplot2, readr, magrittr, dplyr, purrr, reshape2, gridExtra, utils, stringr, ggpubr, stats, cowplot, RColorBrewer, tidyr, scales, grid, rlang, ggforce, grDevices, gtable, plyr, knitr, rmarkdown, ggrepel, gghalves, tools Suggests: testthat (>= 3.0.0), BiocStyle License: GPL-3 MD5sum: 879b06ee1b711956dd33ce3acb8068ff NeedsCompilation: no Title: Quality Control of Protemics Data Description: The package MQmetrics (MaxQuant metrics) provides a workflow to analyze the quality and reproducibility of your proteomics mass spectrometry analysis from MaxQuant.Input data are extracted from several MaxQuant output tables and produces a pdf report. It includes several visualization tools to check numerous parameters regarding the quality of the runs. It also includes two functions to visualize the iRT peptides from Biognosys in case they were spiked in the samples. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Alvaro Sanchez-Villalba [aut, cre], Thomas Stehrer [aut], Marek Vrbacky [aut] Maintainer: Alvaro Sanchez-Villalba VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MQmetrics git_branch: RELEASE_3_16 git_last_commit: 1bc9b41 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MQmetrics_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MQmetrics_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MQmetrics_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MQmetrics_1.6.0.tgz vignettes: vignettes/MQmetrics/inst/doc/MQmetrics.html vignetteTitles: MQmetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MQmetrics/inst/doc/MQmetrics.R dependencyCount: 123 Package: msa Version: 1.30.1 Depends: R (>= 3.3.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape (>= 5.1), phangorn License: GPL (>= 2) MD5sum: 05f60762e9db486708261a5db83eefdb NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: RELEASE_3_16 git_last_commit: 155f119 git_last_commit_date: 2022-12-05 Date/Publication: 2022-12-05 source.ver: src/contrib/msa_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/msa_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.2/msa_1.30.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msa_1.30.1.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: LymphoSeq, odseq, surfaltr suggestsMe: idpr, bio3d dependencyCount: 19 Package: MSA2dist Version: 1.2.0 Depends: R (>= 4.2.0) Imports: Rcpp, Biostrings, GenomicRanges, IRanges, ape, doParallel, dplyr, foreach, methods, parallel, rlang, seqinr, stringr, tibble, tidyr, stats, stringi LinkingTo: Rcpp, RcppThread Suggests: rmarkdown, knitr, devtools, testthat, ggplot2, BiocStyle License: GPL-3 + file LICENSE Archs: x64 MD5sum: 9f8beef3590593304e45a90a6a22bdaa NeedsCompilation: yes Title: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis Description: MSA2dist calculates pairwise distances between all sequences of a DNAStringSet or a AAStringSet using a custom score matrix and conducts codon based analysis. It uses scoring matrices to be used in these pairwise distance calcualtions which can be adapted to any scoring for DNA or AA characters. E.g. by using literal distances MSA2dist calcualtes pairwise IUPAC distances. biocViews: Alignment, Sequencing, Genetics, GO Author: Kristian K Ullrich [aut, cre] () Maintainer: Kristian K Ullrich URL: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist, https://mpievolbio-it.pages.gwdg.de/MSA2dist/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/mpievolbio-it/MSA2dist/issues git_url: https://git.bioconductor.org/packages/MSA2dist git_branch: RELEASE_3_16 git_last_commit: 62bf9da git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSA2dist_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSA2dist_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSA2dist_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSA2dist_1.2.0.tgz vignettes: vignettes/MSA2dist/inst/doc/MSA2dist.html vignetteTitles: MSA2dist Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MSA2dist/inst/doc/MSA2dist.R dependencyCount: 59 Package: MsBackendMassbank Version: 1.6.1 Depends: R (>= 4.0), Spectra (>= 1.5.17) Imports: BiocParallel, S4Vectors, IRanges, methods, ProtGenerics, MsCoreUtils, DBI, utils Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), RSQLite, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 0b8bfe58c0843ef9aaeedae22863df1b NeedsCompilation: no Title: Mass Spectrometry Data Backend for MassBank record Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS library spectra from MassBank record files. Different backends are available that allow handling of data in plain MassBank text file format or allow also to interact directly with MassBank SQL databases. Objects from this package are supposed to be used with the Spectra Bioconductor package. This package thus adds MassBank support to the Spectra package. biocViews: Infrastructure, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Michael Witting [aut] (), Johannes Rainer [aut] (), Michael Stravs [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMassbank VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMassbank/issues git_url: https://git.bioconductor.org/packages/MsBackendMassbank git_branch: RELEASE_3_16 git_last_commit: 8a02dd6 git_last_commit_date: 2023-01-18 Date/Publication: 2023-01-18 source.ver: src/contrib/MsBackendMassbank_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendMassbank_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendMassbank_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MsBackendMassbank_1.6.1.tgz vignettes: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.html vignetteTitles: Description and usage of MsBackendMassbank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMassbank/inst/doc/MsBackendMassbank.R dependencyCount: 29 Package: MsBackendMgf Version: 1.6.0 Depends: R (>= 4.0), Spectra (>= 1.5.14) Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: bf1148385b65602b3480c9db6bf4e8e4 NeedsCompilation: no Title: Mass Spectrometry Data Backend for Mascot Generic Format (mgf) Files Description: Mass spectrometry (MS) data backend supporting import and export of MS/MS spectra data from Mascot Generic Format (mgf) files. Objects defined in this package are supposed to be used with the Spectra Bioconductor package. This package thus adds mgf file support to the Spectra package. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] () Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsBackendMgf VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMgf/issues git_url: https://git.bioconductor.org/packages/MsBackendMgf git_branch: RELEASE_3_16 git_last_commit: 042d204 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MsBackendMgf_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendMgf_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendMgf_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MsBackendMgf_1.6.0.tgz vignettes: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.html vignetteTitles: Description and usage of MsBackendMgf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMgf/inst/doc/MsBackendMgf.R suggestsMe: CompoundDb, MsBackendRawFileReader, xcms dependencyCount: 28 Package: MsBackendMsp Version: 1.2.0 Depends: R (>= 4.1.0), Spectra (>= 1.5.14) Imports: BiocParallel, S4Vectors, IRanges, MsCoreUtils, methods, stats Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown License: Artistic-2.0 MD5sum: 96f533dfd6955624cb6ea70c6419dacb NeedsCompilation: no Title: Mass Spectrometry Data Backend for NIST msp Files Description: Mass spectrometry (MS) data backend supporting import and handling of MS/MS spectra from NIST MSP Format (msp) files. Import of data from files with different MSP *flavours* is supported. Objects from this package add support for MSP files to Bioconductor's Spectra package. This package is thus not supposed to be used without the Spectra package that provides a complete infrastructure for MS data handling. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, DataImport Author: Neumann Steffen [aut] (), Johannes Rainer [aut, cre] (), Michael Witting [ctb] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsBackendMsp VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsBackendMsp/issues git_url: https://git.bioconductor.org/packages/MsBackendMsp git_branch: RELEASE_3_16 git_last_commit: 3cdc114 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MsBackendMsp_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendMsp_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendMsp_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MsBackendMsp_1.2.0.tgz vignettes: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.html vignetteTitles: MsBackendMsp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsBackendMsp/inst/doc/MsBackendMsp.R dependencyCount: 28 Package: MsBackendRawFileReader Version: 1.4.0 Depends: R (>= 4.1), methods, Spectra (>= 1.5.8) Imports: MsCoreUtils, S4Vectors, IRanges, rawrr (>= 1.3.6), utils, BiocParallel Suggests: BiocStyle (>= 2.5), ExperimentHub, MsBackendMgf, knitr, lattice, mzR, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: 368010c70d67f3febaecc347068f4c2b NeedsCompilation: yes Title: Mass Spectrometry Backend for Reading Thermo Fisher Scientific raw Files Description: implements a MsBackend for the Spectra package using Thermo Fisher Scientific's NewRawFileReader .Net libraries. The package is generalizing the functionality introduced by the rawrr package (Kockmann T. et al. (2020) ) Methods defined in this package are supposed to extend the Spectra Bioconductor package. biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (), Tobias Kockmann [aut] () Maintainer: Christian Panse URL: https://github.com/fgcz/MsBackendRawFileReader SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/MsBackendRawFileReader/issues git_url: https://git.bioconductor.org/packages/MsBackendRawFileReader git_branch: RELEASE_3_16 git_last_commit: 3cffb45 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MsBackendRawFileReader_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsBackendRawFileReader_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsBackendRawFileReader_1.4.0.tgz vignettes: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.html vignetteTitles: On Using and Extending the `MsBackendRawFileReader` Backend. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/MsBackendRawFileReader/inst/doc/MsBackendRawFileReader.R dependencyCount: 29 Package: MsCoreUtils Version: 1.10.0 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats, clue LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, Matrix, preprocessCore, missForest Enhances: HDF5Array License: Artistic-2.0 MD5sum: de53cc4129c32af69cd6b1d7301a5e70 NeedsCompilation: yes Title: Core Utils for Mass Spectrometry Data Description: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...) as well as misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb], Josep Maria Badia Aparicio [ctb] (), Michael Witting [ctb] (), Samuel Wieczorek [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues git_url: https://git.bioconductor.org/packages/MsCoreUtils git_branch: RELEASE_3_16 git_last_commit: 742c0c7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MsCoreUtils_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsCoreUtils_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsCoreUtils_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MsCoreUtils_1.10.0.tgz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: CompoundDb, MetaboAnnotation, MetaboCoreUtils, MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader, MsFeatures, MSnbase, PSMatch, QFeatures, qmtools, scp, Spectra, xcms suggestsMe: MetNet, msqrob2 dependencyCount: 12 Package: MsExperiment Version: 1.0.0 Depends: R (>= 4.2), ProtGenerics (>= 1.9.1), Imports: methods, S4Vectors, IRanges, Spectra, SummarizedExperiment, QFeatures Suggests: testthat, knitr (>= 1.1.0), roxygen2, BiocStyle (>= 2.5.19), rmarkdown, rpx, mzR, msdata License: Artistic-2.0 Archs: x64 MD5sum: 9ad2dfd41904028b925a6cd53bbddef8 NeedsCompilation: no Title: Infrastructure for Mass Spectrometry Experiments Description: Infrastructure to store and manage all aspects related to a complete proteomics or metabolomics mass spectrometry (MS) experiment. The MsExperiment package provides light-weight and flexible containers for MS experiments building on the new MS infrastructure provided by the Spectra, QFeatures and related packages. Along with raw data representations, links to original data files and sample annotations, additional metadata or annotations can also be stored within the MsExperiment container. To guarantee maximum flexibility only minimal constraints are put on the type and content of the data within the containers. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics, ExperimentalDesign, DataImport Author: Laurent Gatto [aut, cre] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] () Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/MsExperiment VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsExperiment/issues git_url: https://git.bioconductor.org/packages/MsExperiment git_branch: RELEASE_3_16 git_last_commit: 605b25f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MsExperiment_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsExperiment_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsExperiment_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MsExperiment_1.0.0.tgz vignettes: vignettes/MsExperiment/inst/doc/MsExperiment.html vignetteTitles: Managing Mass Spectrometry Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsExperiment/inst/doc/MsExperiment.R dependencyCount: 114 Package: MsFeatures Version: 1.6.0 Depends: R (>= 4.1) Imports: methods, ProtGenerics (>= 1.23.5), MsCoreUtils, SummarizedExperiment, stats Suggests: testthat, roxygen2, BiocStyle, pheatmap, knitr, rmarkdown License: Artistic-2.0 MD5sum: c98bd9d75a340c91e7f5407268fc4665 NeedsCompilation: no Title: Functionality for Mass Spectrometry Features Description: The MsFeature package defines functionality for Mass Spectrometry features. This includes functions to group (LC-MS) features based on some of their properties, such as retention time (coeluting features), or correlation of signals across samples. This packge hence allows to group features, and its results can be used as an input for the `QFeatures` package which allows to aggregate abundance levels of features within each group. This package defines concepts and functions for base and common data types, implementations for more specific data types are expected to be implemented in the respective packages (such as e.g. `xcms`). All functionality of this package is implemented in a modular way which allows combination of different grouping approaches and enables its re-use in other R packages. biocViews: Infrastructure, MassSpectrometry, Metabolomics Author: Johannes Rainer [aut, cre] () Maintainer: Johannes Rainer URL: https://github.com/RforMassSpectrometry/MsFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsFeatures/issues git_url: https://git.bioconductor.org/packages/MsFeatures git_branch: RELEASE_3_16 git_last_commit: ab0b2e5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MsFeatures_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MsFeatures_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MsFeatures_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MsFeatures_1.6.0.tgz vignettes: vignettes/MsFeatures/inst/doc/MsFeatures.html vignetteTitles: Grouping Mass Spectrometry Features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsFeatures/inst/doc/MsFeatures.R importsMe: xcms suggestsMe: qmtools dependencyCount: 31 Package: msgbsR Version: 1.22.0 Depends: R (>= 3.5.0), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 MD5sum: 361adbeeca0b89ff5781311122349928 NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: RELEASE_3_16 git_last_commit: a480eaf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/msgbsR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msgbsR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msgbsR_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msgbsR_1.22.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 174 Package: msImpute Version: 1.8.0 Depends: R (>= 3.5.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, limma, mvtnorm, tidyr, dplyr Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) MD5sum: 5a01888e2cec0c899c50e020be008920 NeedsCompilation: no Title: Imputation of label-free mass spectrometry peptides Description: MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. The missing values are imputed by low-rank approximation of the underlying data matrix if they are MAR (method = "v2"), by Barycenter approach if missingness is MNAR ("v2-mnar"), or by Peptide Identity Propagation (PIP). biocViews: MassSpectrometry, Proteomics, Software Author: Soroor Hediyeh-zadeh [aut, cre] () Maintainer: Soroor Hediyeh-zadeh SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/msImpute/issues git_url: https://git.bioconductor.org/packages/msImpute git_branch: RELEASE_3_16 git_last_commit: 96b3929 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/msImpute_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msImpute_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msImpute_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msImpute_1.8.0.tgz vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html vignetteTitles: msImpute: proteomics missing values imputation and diagnosis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R dependencyCount: 90 Package: mslp Version: 1.0.2 Depends: R (>= 4.2.0), data.table (>= 1.13.0) Imports: doRNG, fmsb, foreach, magrittr, org.Hs.eg.db, pROC, randomForest, RankProd, stats, utils Suggests: BiocStyle, doFuture, future, knitr, rmarkdown, roxygen2, tinytest License: GPL-3 MD5sum: e853879599d4200feed7f27eb5ec7ada NeedsCompilation: no Title: Predict synthetic lethal partners of tumour mutations Description: An integrated pipeline to predict the potential synthetic lethality partners (SLPs) of tumour mutations, based on gene expression, mutation profiling and cell line genetic screens data. It has builtd-in support for data from cBioPortal. The primary SLPs correlating with muations in WT and compensating for the loss of function of mutations are predicted by random forest based methods (GENIE3) and Rank Products, respectively. Genetic screens are employed to identfy consensus SLPs leads to reduced cell viability when perturbed. biocViews: Pharmacogenetics, Pharmacogenomics Author: Chunxuan Shao [aut, cre] Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mslp git_branch: RELEASE_3_16 git_last_commit: b5dac95 git_last_commit_date: 2023-03-20 Date/Publication: 2023-03-22 source.ver: src/contrib/mslp_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/mslp_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/mslp_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mslp_1.0.2.tgz vignettes: vignettes/mslp/inst/doc/mslp.html vignetteTitles: mslp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mslp/inst/doc/mslp.R dependencyCount: 64 Package: msmsEDA Version: 1.36.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: 1db59be3d7ea20bdc4b3293d74cb44cb NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: RELEASE_3_16 git_last_commit: d580f36 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/msmsEDA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msmsEDA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msmsEDA_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msmsEDA_1.36.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman, RforProteomics dependencyCount: 81 Package: msmsTests Version: 1.36.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: d6fcb6524c0eaee866ec79bc270b2c24 NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font git_url: https://git.bioconductor.org/packages/msmsTests git_branch: RELEASE_3_16 git_last_commit: 9273375 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/msmsTests_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msmsTests_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msmsTests_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msmsTests_1.36.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R importsMe: MSnID suggestsMe: RforProteomics dependencyCount: 89 Package: MSnbase Version: 2.24.2 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.29.3), S4Vectors, ProtGenerics (>= 1.29.1) Imports: MsCoreUtils, BiocParallel, IRanges (>= 2.13.28), plyr, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, XML, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 MD5sum: be73d48936ab826ac6b03489c1ada7ba NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker and Lieven Clement. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: RELEASE_3_16 git_last_commit: b934748 git_last_commit_date: 2022-12-21 Date/Publication: 2022-12-21 source.ver: src/contrib/MSnbase_2.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSnbase_2.24.2.zip mac.binary.ver: bin/macosx/contrib/4.2/MSnbase_2.24.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSnbase_2.24.2.tgz vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html, vignettes/MSnbase/inst/doc/v02-MSnbase-io.html, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/v04-benchmarking.html, vignettes/MSnbase/inst/doc/v05-MSnbase-development.html vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase IO capabilities, MSnbase: centroiding of profile-mode MS data, MSnbase benchmarking, A short introduction to `MSnbase` development hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R, vignettes/MSnbase/inst/doc/v02-MSnbase-io.R, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/v04-benchmarking.R, vignettes/MSnbase/inst/doc/v05-MSnbase-development.R dependsOnMe: bandle, MobilityTransformR, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, synapter, xcms, DAPARdata, pRolocdata, RforProteomics importsMe: cliqueMS, CluMSID, DAPAR, DEP, MSnID, MSstatsQC, peakPantheR, PrInCE, ptairMS, topdownr, qPLEXdata, LCMSQA suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, msPurity, msqrob2, proDA, qcmetrics, wpm, msdata, enviGCMS, pmd, RAMClustR dependencyCount: 75 Package: MSnID Version: 1.32.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang, stringr, tibble, AnnotationHub, AnnotationDbi, xtable License: Artistic-2.0 Archs: x64 MD5sum: a3fbc76e3678abed4b9837d7908a1523 NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk git_url: https://git.bioconductor.org/packages/MSnID git_branch: RELEASE_3_16 git_last_commit: d641e00 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSnID_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSnID_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSnID_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSnID_1.32.0.tgz vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf, vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: Handling Modifications with MSnID, MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/handling_mods.R, vignettes/MSnID/inst/doc/msnid_vignette.R suggestsMe: RforProteomics dependencyCount: 161 Package: MSPrep Version: 1.8.0 Depends: R (>= 4.1.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), crmn, preprocessCore, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, missForest, sva, VIM, Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 MD5sum: ef7a1345f7a5e6cfaa0798d54072a579 NeedsCompilation: no Title: Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data Description: Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data. biocViews: Metabolomics, MassSpectrometry, Preprocessing Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes [aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut], Katerina Kechris [aut, cph, ths] Maintainer: Max McGrath URL: https://github.com/KechrisLab/MSPrep VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MSPrep/issues git_url: https://git.bioconductor.org/packages/MSPrep git_branch: RELEASE_3_16 git_last_commit: aa814e4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSPrep_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSPrep_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSPrep_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSPrep_1.8.0.tgz vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html vignetteTitles: Using MSPrep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R dependencyCount: 152 Package: msPurity Version: 1.24.0 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite Suggests: MSnbase, testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE MD5sum: 87acc64e53efae7328e2121a3e16a85d NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: RELEASE_3_16 git_last_commit: f0aecfe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/msPurity_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/msPurity_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/msPurity_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msPurity_1.24.0.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R dependencyCount: 70 Package: msqrob2 Version: 1.6.1 Depends: R (>= 4.1), QFeatures (>= 1.1.2) Imports: stats, methods, lme4, purrr, BiocParallel, Matrix, MASS, limma, SummarizedExperiment, MultiAssayExperiment, codetools Suggests: multcomp, gridExtra, knitr, BiocStyle, RefManageR, sessioninfo, rmarkdown, testthat, tidyverse, plotly, msdata, MSnbase, matrixStats, MsCoreUtils, covr License: Artistic-2.0 MD5sum: cca88bb22aa157b11b5444245512523e NeedsCompilation: no Title: Robust statistical inference for quantitative LC-MS proteomics Description: msqrob2 provides a robust linear mixed model framework for assessing differential abundance in MS-based Quantitative proteomics experiments. Our workflows can start from raw peptide intensities or summarised protein expression values. The model parameter estimates can be stabilized by ridge regression, empirical Bayes variance estimation and robust M-estimation. msqrob2's hurde workflow can handle missing data without having to rely on hard-to-verify imputation assumptions, and, outcompetes state-of-the-art methods with and without imputation for both high and low missingness. It builds on QFeature infrastructure for quantitative mass spectrometry data to store the model results together with the raw data and preprocessed data. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, MultipleComparison, Regression, ExperimentalDesign, Software, ImmunoOncology, Normalization, TimeCourse, Preprocessing Author: Lieven Clement [aut, cre] (), Laurent Gatto [aut] (), Oliver M. Crook [aut] (), Adriaan Sticker [ctb], Ludger Goeminne [ctb], Milan Malfait [ctb] (), Stijn Vandenbulcke [aut] Maintainer: Lieven Clement URL: https://github.com/statOmics/msqrob2 VignetteBuilder: knitr BugReports: https://github.com/statOmics/msqrob2/issues git_url: https://git.bioconductor.org/packages/msqrob2 git_branch: RELEASE_3_16 git_last_commit: f85e8f3 git_last_commit_date: 2023-02-02 Date/Publication: 2023-02-02 source.ver: src/contrib/msqrob2_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/msqrob2_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/msqrob2_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/msqrob2_1.6.1.tgz vignettes: vignettes/msqrob2/inst/doc/cptac.html vignetteTitles: A. label-free workflow with two group design hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msqrob2/inst/doc/cptac.R dependencyCount: 132 Package: MSstats Version: 4.6.5 Depends: R (>= 4.0) Imports: MSstatsConvert, data.table, checkmate, MASS, limma, lme4, preprocessCore, survival, utils, Rcpp, ggplot2, ggrepel, gplots, marray, stats, grDevices, graphics, methods, statmod LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, tinytest, covr, markdown License: Artistic-2.0 MD5sum: a7971a884a9581a7a1b0beb14cc40451 NeedsCompilation: yes Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Mateusz Staniak [aut], Tsung-Heng Tsai [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: RELEASE_3_16 git_last_commit: 6ade3b9 git_last_commit_date: 2023-03-27 Date/Publication: 2023-03-27 source.ver: src/contrib/MSstats_4.6.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstats_4.6.5.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstats_4.6.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstats_4.6.5.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html vignetteTitles: MSstats: Protein/Peptide significance analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R importsMe: artMS, MSstatsLiP, MSstatsPTM, MSstatsSampleSize, MSstatsShiny, MSstatsTMT dependencyCount: 79 Package: MSstatsConvert Version: 1.8.3 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils, stringi Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 3d5774617ff3a9c7e9c222ee78e67d3d NeedsCompilation: no Title: Import Data from Various Mass Spectrometry Signal Processing Tools to MSstats Format Description: MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. biocViews: MassSpectrometry, Proteomics, Software, DataImport, QualityControl Author: Mateusz Staniak [aut, cre], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: RELEASE_3_16 git_last_commit: 6000ac3 git_last_commit_date: 2023-03-27 Date/Publication: 2023-03-27 source.ver: src/contrib/MSstatsConvert_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsConvert_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsConvert_1.8.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsConvert_1.8.3.tgz vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html vignetteTitles: Working with MSstatsConvert hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R importsMe: MSstats, MSstatsLiP, MSstatsPTM, MSstatsShiny, MSstatsTMT dependencyCount: 9 Package: MSstatsLiP Version: 1.4.1 Depends: R (>= 4.1) Imports: dplyr, gridExtra, stringr, ggplot2, grDevices, MSstats, MSstatsConvert, data.table, Biostrings, MSstatsPTM, Rcpp, checkmate, factoextra, ggpubr, purrr, tibble, tidyr, tidyverse, scales, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, gghighlight License: Artistic-2.0 MD5sum: 241a15a910d7aef6a480056c58bdc651 NeedsCompilation: yes Title: LiP Significance Analysis in shotgun mass spectrometry-based proteomic experiments Description: Tools for LiP peptide and protein significance analysis. Provides functions for summarization, estimation of LiP peptide abundance, and detection of changes across conditions. Utilizes functionality across the MSstats family of packages. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Valentina Cappelletti [aut], Liliana Malinovska [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLiP/issues git_url: https://git.bioconductor.org/packages/MSstatsLiP git_branch: RELEASE_3_16 git_last_commit: 48bf904 git_last_commit_date: 2022-12-06 Date/Publication: 2022-12-06 source.ver: src/contrib/MSstatsLiP_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsLiP_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsLiP_1.4.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsLiP_1.4.1.tgz vignettes: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.html, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.html vignetteTitles: MSstatsLiP Workflow: An example workflow and analysis of the MSstatsLiP package, MSstatsLiP Proteolytic Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLiP/inst/doc/MSstatsLiP_Workflow.R, vignettes/MSstatsLiP/inst/doc/Proteolytic_resistance_notebook.R dependencyCount: 198 Package: MSstatsLOBD Version: 1.6.0 Depends: R (>= 4.0) Imports: minpack.lm, ggplot2, utils, stats, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, covr, tinytest, dplyr License: Artistic-2.0 Archs: x64 MD5sum: fa80836576862fe29deeebca93a3d0e7 NeedsCompilation: no Title: Assay characterization: estimation of limit of blanc(LoB) and limit of detection(LOD) Description: The MSstatsLOBD package allows calculation and visualization of limit of blac (LOB) and limit of detection (LOD). We define the LOB as the highest apparent concentration of a peptide expected when replicates of a blank sample containing no peptides are measured. The LOD is defined as the measured concentration value for which the probability of falsely claiming the absence of a peptide in the sample is 0.05, given a probability 0.05 of falsely claiming its presence. These functionalities were previously a part of the MSstats package. The methodology is described in Galitzine (2018) . biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Mateusz Staniak [aut], Cyril Galitzine [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsLODQ/issues git_url: https://git.bioconductor.org/packages/MSstatsLOBD git_branch: RELEASE_3_16 git_last_commit: 6abf388 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSstatsLOBD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsLOBD_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsLOBD_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsLOBD_1.6.0.tgz vignettes: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.html vignetteTitles: LOB/LOD Estimation Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsLOBD/inst/doc/MSstatsLOBD_workflow.R dependencyCount: 37 Package: MSstatsPTM Version: 2.0.3 Depends: R (>= 4.2) Imports: dplyr, gridExtra, stringr, stats, ggplot2, grDevices, MSstatsTMT, MSstatsConvert, MSstats, data.table, Rcpp, Biostrings, checkmate, ggrepel LinkingTo: Rcpp Suggests: knitr, rmarkdown, tinytest, covr License: Artistic-2.0 MD5sum: 64d596c4355a1619d69463a86366d625 NeedsCompilation: yes Title: Statistical Characterization of Post-translational Modifications Description: MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Supports DDA, DIA, SRM, and tandem mass tag (TMT) labeling. Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Tsung-Heng Tsai [aut], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: RELEASE_3_16 git_last_commit: 12af477 git_last_commit_date: 2023-02-28 Date/Publication: 2023-02-28 source.ver: src/contrib/MSstatsPTM_2.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsPTM_2.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsPTM_2.0.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsPTM_2.0.3.tgz vignettes: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.html, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.html vignetteTitles: MSstatsPTM LabelFree Workflow, MSstatsPTM TMT Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM_LabelFree_Workflow.R, vignettes/MSstatsPTM/inst/doc/MSstatsPTM_TMT_Workflow.R importsMe: MSstatsLiP, MSstatsShiny dependencyCount: 98 Package: MSstatsQC Version: 2.16.0 Depends: R (>= 3.5.0) Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 Archs: x64 MD5sum: 124330192d72e5ae986d5c4e2b80a6d0 NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQC git_branch: RELEASE_3_16 git_last_commit: 7c9dd86 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSstatsQC_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsQC_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsQC_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsQC_2.16.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui dependencyCount: 129 Package: MSstatsQCgui Version: 1.18.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 MD5sum: 0fde1a1850e1fe2bd0e6b2ccafb3e928 NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQCgui git_branch: RELEASE_3_16 git_last_commit: c8e19fd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSstatsQCgui_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsQCgui_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsQCgui_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsQCgui_1.18.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R dependencyCount: 131 Package: MSstatsSampleSize Version: 1.12.0 Depends: R (>= 3.6) Imports: ggplot2, BiocParallel, caret, gridExtra, reshape2, stats, utils, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: dbfb1df46a9e01baab5a384e6ff952e8 NeedsCompilation: no Title: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment Description: The packages estimates the variance in the input protein abundance data and simulates data with predefined number of biological replicates based on the variance estimation. It reports the mean predictive accuracy of the classifier and mean protein importance over multiple iterations of the simulation. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression, Classification, PrincipalComponent, ExperimentalDesign, Visualization Author: Ting Huang [aut, cre], Meena Choi [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsSampleSize git_branch: RELEASE_3_16 git_last_commit: 71aa80a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MSstatsSampleSize_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsSampleSize_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsSampleSize_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsSampleSize_1.12.0.tgz vignettes: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.html vignetteTitles: MSstatsSampleSize User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.R dependencyCount: 130 Package: MSstatsShiny Version: 1.0.10 Depends: R (>= 4.2) Imports: shiny, shinyBS, shinyjs, shinybusy, dplyr, ggplot2, data.table, Hmisc, MSstats, MSstatsTMT, MSstatsPTM, MSstatsConvert, gplots, marray, DT, ggrepel, uuid, utils, stats, htmltools, methods, tidyr, grDevices, graphics Suggests: rmarkdown, tinytest, sessioninfo, knitr License: Artistic-2.0 MD5sum: 4e0564229ccace2b111e8e2be4ccbd57 NeedsCompilation: no Title: MSstats GUI for Statistical Anaylsis of Proteomics Experiments Description: MSstatsShiny is an R-Shiny graphical user interface (GUI) integrated with the R packages MSstats, MSstatsTMT, and MSstatsPTM. It provides a point and click end-to-end analysis pipeline applicable to a wide variety of experimental designs. These include data-dependedent acquisitions (DDA) which are label-free or tandem mass tag (TMT)-based, as well as DIA, SRM, and PRM acquisitions and those targeting post-translational modifications (PTMs). The application automatically saves users selections and builds an R script that recreates their analysis, supporting reproducible data analysis. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, ShinyApps, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl, GUI Author: Devon Kohler [aut, cre], Deril Raju [aut], Maanasa Kaza [aut], Cristina Pasi [aut], Ting Huang [aut], Mateusz Staniak [aut], Dhaval Mohandas [aut], Eduard Sabido [aut], Meena Choi [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsShiny/issues git_url: https://git.bioconductor.org/packages/MSstatsShiny git_branch: RELEASE_3_16 git_last_commit: 3cb9cb3 git_last_commit_date: 2023-03-02 Date/Publication: 2023-03-03 source.ver: src/contrib/MSstatsShiny_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsShiny_1.0.10.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsShiny_1.0.10.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsShiny_1.0.10.tgz vignettes: vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.pdf vignetteTitles: MSstatsPTM LabelFree Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsShiny/inst/doc/MSstatsShiny_Launch_Instructions.R dependencyCount: 143 Package: MSstatsTMT Version: 2.6.1 Depends: R (>= 4.2) Imports: limma, lme4, lmerTest, methods, data.table, stats, utils, ggplot2, grDevices, graphics, MSstats, MSstatsConvert, checkmate Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 3edaceb29f623d71576eca677e0e9da4 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. It provides multiple functionalities, including aata visualization, protein quantification and normalization, and statistical modeling and inference. Furthermore, it is inter-operable with other data processing tools, such as Proteome Discoverer, MaxQuant, OpenMS and SpectroMine. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Ting Huang [aut, cre], Meena Choi [aut], Mateusz Staniak [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: RELEASE_3_16 git_last_commit: a8aa9bc git_last_commit_date: 2023-02-26 Date/Publication: 2023-02-27 source.ver: src/contrib/MSstatsTMT_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MSstatsTMT_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MSstatsTMT_2.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MSstatsTMT_2.6.1.tgz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R importsMe: MSstatsPTM, MSstatsShiny dependencyCount: 82 Package: MuData Version: 1.2.0 Depends: Matrix, S4Vectors, rhdf5 Imports: methods, stats, MultiAssayExperiment, SingleCellExperiment, SummarizedExperiment, DelayedArray Suggests: HDF5Array, rmarkdown, knitr, fs, testthat, BiocStyle, covr, SingleCellMultiModal, CiteFuse, scater License: GPL-3 MD5sum: 5d86826895ec779de269c50aaf347b29 NeedsCompilation: no Title: Serialization for MultiAssayExperiment Objects Description: Save MultiAssayExperiments to h5mu files supported by muon and mudata. Muon is a Python framework for multimodal omics data analysis. It uses an HDF5-based format for data storage. biocViews: DataImport Author: Danila Bredikhin [aut, cre] (), Ilia Kats [aut] () Maintainer: Danila Bredikhin URL: https://github.com/ilia-kats/MuData VignetteBuilder: knitr BugReports: https://github.com/ilia-kats/MuData/issues git_url: https://git.bioconductor.org/packages/MuData git_branch: RELEASE_3_16 git_last_commit: e790d30 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MuData_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MuData_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MuData_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MuData_1.2.0.tgz vignettes: vignettes/MuData/inst/doc/Blood-CITE-seq.html, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.html, vignettes/MuData/inst/doc/Getting-Started.html vignetteTitles: Blood CITE-seq with MuData, Cord Blood CITE-seq with MuData, Getting started with MuDataMae hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MuData/inst/doc/Blood-CITE-seq.R, vignettes/MuData/inst/doc/Cord-Blood-CITE-seq.R, vignettes/MuData/inst/doc/Getting-Started.R dependencyCount: 52 Package: Mulcom Version: 1.48.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 MD5sum: 41674f0e93707fcbe2ec06e9c523b613 NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: RELEASE_3_16 git_last_commit: 4b1f50b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Mulcom_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Mulcom_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Mulcom_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Mulcom_1.48.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 43 Package: MultiAssayExperiment Version: 1.24.0 Depends: R (>= 4.2.0), SummarizedExperiment (>= 1.3.81) Imports: methods, GenomicRanges (>= 1.25.93), BiocBaseUtils, BiocGenerics, DelayedArray, S4Vectors (>= 0.23.19), IRanges, Biobase, stats, tidyr, utils Suggests: BiocStyle, HDF5Array (>= 1.19.17), knitr, maftools (>= 2.7.10), rmarkdown, R.rsp, RaggedExperiment, UpSetR, survival, survminer, testthat License: Artistic-2.0 Archs: x64 MD5sum: 8fba9eac1903147a9cbf40dfe8ec9162 NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: MultiAssayExperiment harmonizes data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre] (), Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr, R.rsp Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: RELEASE_3_16 git_last_commit: 7f63e33 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MultiAssayExperiment_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiAssayExperiment_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiAssayExperiment_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MultiAssayExperiment_1.24.0.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: CAGEr, cBioPortalData, ClassifyR, evaluomeR, glmSparseNet, hipathia, InTAD, mia, midasHLA, missRows, QFeatures, terraTCGAdata, TimiRGeN, curatedTCGAData, microbiomeDataSets, OMICsPCAdata, SingleCellMultiModal importsMe: AffiXcan, AMARETTO, animalcules, autonomics, biosigner, CoreGx, corral, ELMER, FindIT2, FLAMES, GOpro, hermes, LinkHD, metabolomicsWorkbenchR, MOMA, msqrob2, MuData, MultiBaC, OMICsPCA, omicsPrint, padma, PDATK, PharmacoGx, phenomis, ropls, scp, scPipe, TCGAutils, vsclust, xcore, curatedTBData, HMP2Data, MOCHA suggestsMe: BiocOncoTK, CNVRanger, deco, maftools, MOFA2, MultiDataSet, RaggedExperiment, brgedata, MOFAdata dependencyCount: 47 Package: MultiBaC Version: 1.8.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods, plotrix, grDevices, pcaMethods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 MD5sum: 10e45443f05de79a57e5f8869a4c0227 NeedsCompilation: no Title: Multiomic Batch effect Correction Description: MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered. biocViews: Software, StatisticalMethod, PrincipalComponent, DataRepresentation, GeneExpression, Transcription, BatchEffect Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"), person("Sonia", "Tarazona", email = "sotacam@gmail.com"), person("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: RELEASE_3_16 git_last_commit: 36028a8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MultiBaC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiBaC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiBaC_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MultiBaC_1.8.0.tgz vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html vignetteTitles: MultiBaC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R dependencyCount: 105 Package: multiClust Version: 1.28.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, rmarkdown, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 4cd925422c12ff049a1b487ea086c004 NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: RELEASE_3_16 git_last_commit: c87eaef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multiClust_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiClust_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiClust_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiClust_1.28.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 44 Package: multicrispr Version: 1.8.0 Depends: R (>= 4.0) Imports: assertive, BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, grid, karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi, tidyr, tidyselect, utils Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, knitr, magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-2 MD5sum: 02a2404864517558e021efec59eb93aa NeedsCompilation: no Title: Multi-locus multi-purpose Crispr/Cas design Description: This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries. biocViews: CRISPR, Software Author: Aditya Bhagwat [aut, cre], Rene Wiegandt [ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Johannes Graumann [sad], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://github.com/loosolab/multicrispr VignetteBuilder: knitr BugReports: https://github.com/loosolab/multicrispr/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: RELEASE_3_16 git_last_commit: 1f27204 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multicrispr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multicrispr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multicrispr_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multicrispr_1.8.0.tgz vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html, vignettes/multicrispr/inst/doc/genome_arithmetics.html, vignettes/multicrispr/inst/doc/prime_editing.html vignetteTitles: grna_design, genome_arithmetics, prime_editing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R, vignettes/multicrispr/inst/doc/genome_arithmetics.R, vignettes/multicrispr/inst/doc/prime_editing.R dependencyCount: 194 Package: MultiDataSet Version: 1.26.0 Depends: R (>= 4.1), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE MD5sum: 7df664216762ca1dc6e7e7a76516b9dd NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Xavier Escrib Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: RELEASE_3_16 git_last_commit: a83a80a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MultiDataSet_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiDataSet_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiDataSet_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MultiDataSet_1.26.0.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, phenomis, ropls dependencyCount: 57 Package: multiGSEA Version: 1.8.2 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, dplyr, fgsea, metap, rappdirs, rlang, methods Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, metaboliteIDmapping, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 MD5sum: afd0ff4c7eaea2209c9d53ea66ede688 NeedsCompilation: no Title: Combining GSEA-based pathway enrichment with multi omics data integration Description: Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score. biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta Author: Sebastian Canzler [aut, cre] (), Jörg Hackermüller [aut] () Maintainer: Sebastian Canzler URL: https://github.com/yigbt/multiGSEA VignetteBuilder: knitr BugReports: https://github.com/yigbt/multiGSEA/issues git_url: https://git.bioconductor.org/packages/multiGSEA git_branch: RELEASE_3_16 git_last_commit: bb914a2 git_last_commit_date: 2023-03-27 Date/Publication: 2023-03-27 source.ver: src/contrib/multiGSEA_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiGSEA_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/multiGSEA_1.8.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiGSEA_1.8.3.tgz vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html vignetteTitles: multiGSEA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R dependencyCount: 113 Package: multiHiCcompare Version: 1.16.0 Depends: R (>= 4.0.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, GenomeInfoDb, aggregation Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 2559d4722bdf10329ab5b2e4f9303006 NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield , Mikhail Dozmorov Maintainer: John Stansfield , Mikhail Dozmorov URL: https://github.com/dozmorovlab/multiHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: RELEASE_3_16 git_last_commit: 26aac53 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multiHiCcompare_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiHiCcompare_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiHiCcompare_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiHiCcompare_1.16.0.tgz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: juiceboxVisualization, multiHiCcompare hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R importsMe: HiCDOC suggestsMe: HiCcompare dependencyCount: 103 Package: MultiMed Version: 2.20.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 955d70d0e50ad01d551ff391004d83d3 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: RELEASE_3_16 git_last_commit: 24f998a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MultiMed_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MultiMed_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MultiMed_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MultiMed_2.20.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: multiMiR Version: 1.20.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: e828c9713bc9cd6be6545396da969f74 NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [cre, aut], Spencer Mahaffey [aut], Katerina Kechris [aut, cph, ths] Maintainer: Matt Mulvahill URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues git_url: https://git.bioconductor.org/packages/multiMiR git_branch: RELEASE_3_16 git_last_commit: dd1fe3c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multiMiR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiMiR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiMiR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiMiR_1.20.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R dependencyCount: 57 Package: multiOmicsViz Version: 1.22.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: 2f7ff70ef162efe674e1caaba2fe4a23 NeedsCompilation: no Title: Plot the effect of one omics data on other omics data along the chromosome Description: Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. biocViews: Software, Visualization, SystemsBiology Author: Jing Wang Maintainer: Jing Wang git_url: https://git.bioconductor.org/packages/multiOmicsViz git_branch: RELEASE_3_16 git_last_commit: 5ed6012 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multiOmicsViz_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiOmicsViz_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiOmicsViz_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiOmicsViz_1.22.0.tgz vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf vignetteTitles: multiOmicsViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R dependencyCount: 30 Package: multiscan Version: 1.58.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) MD5sum: 0fbfbbc76d415635f71e58202f7d856f NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: RELEASE_3_16 git_last_commit: 4fc2c34 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multiscan_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiscan_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiscan_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiscan_1.58.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 6 Package: multiSight Version: 1.6.0 Depends: R (>= 4.1) Imports: golem, config, R6, shiny, shinydashboard, DT, dplyr, stringr, anyLib, caret, biosigner, mixOmics, stats, DESeq2, clusterProfiler, rWikiPathways, ReactomePA, enrichplot, ppcor, metap, infotheo, igraph, networkD3, easyPubMed, utils, htmltools, rmarkdown, ggnewscale Suggests: org.Mm.eg.db, rlang, markdown, attempt, processx, testthat, knitr, BiocStyle License: CeCILL + file LICENSE Archs: x64 MD5sum: a98915438f0cd5efe7236ecad5630b18 NeedsCompilation: no Title: Multi-omics Classification, Functional Enrichment and Network Inference analysis Description: multiSight is an R package providing functions to analyze your omic datasets in a multi-omics manner based on Stouffer's p-value pooling and multi-block statistical methods. For each omic dataset you furnish, multiSight provides classification models with feature selection you can use as biosignature: (i) To forecast phenotypes (e.g. to diagnostic tasks, histological subtyping), (ii) To design Pathways and gene ontology enrichments (Over Representation Analysis), (iii) To build Network inference linked to PubMed querying to make assumptions easier and data-driven. Main analysis are embedded in an user-friendly graphical interface. biocViews: Software, RNASeq, miRNA, Network, NetworkInference, DifferentialExpression, Classification, Pathways, GeneSetEnrichment Author: Florian Jeanneret [cre, aut] (), Stephane Gazut [aut] Maintainer: Florian Jeanneret VignetteBuilder: knitr BugReports: https://github.com/Fjeanneret/multiSight/issues git_url: https://git.bioconductor.org/packages/multiSight git_branch: RELEASE_3_16 git_last_commit: 9dd2287 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multiSight_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multiSight_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multiSight_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multiSight_1.6.0.tgz vignettes: vignettes/multiSight/inst/doc/multiSight.html vignetteTitles: multiSight quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiSight/inst/doc/multiSight.R dependencyCount: 292 Package: multtest Version: 2.54.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL MD5sum: dc67747ec97ee5a093d9cbf63db165fc NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: RELEASE_3_16 git_last_commit: 4e2c9e9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/multtest_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/multtest_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/multtest_2.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/multtest_2.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq, siggenes, webbioc, cp4p, DiffCorr, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, GUIDEseq, mAPKL, metabomxtr, microbiomeMarker, nethet, OCplus, phyloseq, RTopper, SingleCellSignalR, singleCellTK, synapter, webbioc, hddplot, INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, ecolitk, factDesign, GOstats, GSEAlm, maigesPack, ropls, topGO, xcms, cherry, POSTm dependencyCount: 14 Package: mumosa Version: 1.6.0 Depends: SingleCellExperiment Imports: stats, utils, methods, igraph, Matrix, BiocGenerics, BiocParallel, IRanges, S4Vectors, DelayedArray, DelayedMatrixStats, SummarizedExperiment, BiocNeighbors, BiocSingular, ScaledMatrix, beachmat, scuttle, metapod, scran, batchelor, uwot Suggests: testthat, knitr, BiocStyle, rmarkdown, scater, bluster, DropletUtils, scRNAseq License: GPL-3 Archs: x64 MD5sum: e73ff26cf2e2508548bf4e25734c3575 NeedsCompilation: no Title: Multi-Modal Single-Cell Analysis Methods Description: Assorted utilities for multi-modal analyses of single-cell datasets. Includes functions to combine multiple modalities for downstream analysis, perform MNN-based batch correction across multiple modalities, and to compute correlations between assay values for different modalities. biocViews: ImmunoOncology, SingleCell, RNASeq Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: http://bioconductor.org/packages/mumosa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/mumosa git_branch: RELEASE_3_16 git_last_commit: 86eddca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mumosa_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mumosa_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mumosa_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mumosa_1.6.0.tgz vignettes: vignettes/mumosa/inst/doc/overview.html vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mumosa/inst/doc/overview.R dependsOnMe: OSCA.advanced dependencyCount: 67 Package: MungeSumstats Version: 1.6.0 Depends: R(>= 4.1) Imports: magrittr, data.table, utils, R.utils, dplyr, stats, GenomicRanges, IRanges, GenomeInfoDb, BSgenome, Biostrings, stringr, VariantAnnotation, googleAuthR, httr, jsonlite, methods, parallel, rtracklayer, RCurl Suggests: SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BiocGenerics, S4Vectors, rmarkdown, markdown, knitr, testthat (>= 3.0.0), UpSetR, BiocStyle, covr, Rsamtools, MatrixGenerics, badger, BiocParallel, GenomicFiles License: Artistic-2.0 MD5sum: 325f5862e6a05c1eddd929fa2031d83c NeedsCompilation: no Title: Standardise summary statistics from GWAS Description: The *MungeSumstats* package is designed to facilitate the standardisation of GWAS summary statistics. It reformats inputted summary statisitics to include SNP, CHR, BP and can look up these values if any are missing. It also pefrorms dozens of QC and filtering steps to ensure high data quality and minimise inter-study differences. biocViews: SNP, WholeGenome, Genetics, ComparativeGenomics, GenomeWideAssociation, GenomicVariation, Preprocessing Author: Alan Murphy [aut, cre] (), Brian Schilder [aut, ctb] (), Nathan Skene [aut] () Maintainer: Alan Murphy URL: https://github.com/neurogenomics/MungeSumstats VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/MungeSumstats/issues git_url: https://git.bioconductor.org/packages/MungeSumstats git_branch: RELEASE_3_16 git_last_commit: 00916cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MungeSumstats_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MungeSumstats_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MungeSumstats_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MungeSumstats_1.6.0.tgz vignettes: vignettes/MungeSumstats/inst/doc/docker.html, vignettes/MungeSumstats/inst/doc/MungeSumstats.html, vignettes/MungeSumstats/inst/doc/OpenGWAS.html vignetteTitles: docker, MungeSumstats, OpenGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MungeSumstats/inst/doc/docker.R, vignettes/MungeSumstats/inst/doc/MungeSumstats.R, vignettes/MungeSumstats/inst/doc/OpenGWAS.R dependencyCount: 106 Package: muscat Version: 1.12.1 Depends: R (>= 4.2) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, Matrix, matrixStats, methods, progress, purrr, S4Vectors, scales, scater, scuttle, sctransform, stats, SingleCellExperiment, SummarizedExperiment, variancePartition, viridis Suggests: BiocStyle, countsimQC, cowplot, ExperimentHub, iCOBRA, knitr, phylogram, RColorBrewer, reshape2, rmarkdown, statmod, testthat, UpSetR License: GPL-3 MD5sum: 2231991e13fd6fe7fa64e20084b486b4 NeedsCompilation: no Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/muscat VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/muscat/issues git_url: https://git.bioconductor.org/packages/muscat git_branch: RELEASE_3_16 git_last_commit: ae0feb7 git_last_commit_date: 2023-02-08 Date/Publication: 2023-02-08 source.ver: src/contrib/muscat_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/muscat_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.2/muscat_1.11.2.tgz vignettes: vignettes/muscat/inst/doc/analysis.html, vignettes/muscat/inst/doc/simulation.html vignetteTitles: "1. DS analysis", "2. Data simulation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/analysis.R, vignettes/muscat/inst/doc/simulation.R suggestsMe: muscData dependencyCount: 198 Package: muscle Version: 3.40.0 Depends: Biostrings License: Unlimited MD5sum: 063dc9427f7c9770a8b1cae7903edf1b NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: RELEASE_3_16 git_last_commit: 172d1aa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/muscle_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/muscle_3.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/muscle_3.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/muscle_3.40.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R importsMe: ptm suggestsMe: geneHapR, seqmagick dependencyCount: 18 Package: musicatk Version: 1.8.0 Depends: R (>= 4.0.0), NMF Imports: SummarizedExperiment, VariantAnnotation, cowplot, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, MCMCprecision, MASS, matrixTests, data.table, dplyr, rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, S4Vectors, uwot, ggplot2, stringr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, deconstructSigs, decompTumor2Sig, topicmodels, ggrepel, withr, plotly, utils, factoextra, cluster, ComplexHeatmap, philentropy, shinydashboard, sortable, maftools, shiny, shinyjs, shinyalert, shinybusy, shinyBS, TCGAbiolinks, shinyjqui, stringi Suggests: testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr License: LGPL-3 Archs: x64 MD5sum: 6e648f5b8f29a024ad5bd257ed471930 NeedsCompilation: no Title: Mutational Signature Comprehensive Analysis Toolkit Description: Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3. biocViews: Software, BiologicalQuestion, SomaticMutation, VariantAnnotation Author: Aaron Chevalier [cre] (0000-0002-3968-9250), Joshua D. Campbell [aut] () Maintainer: Aaron Chevalier VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: RELEASE_3_16 git_last_commit: a7a8454 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/musicatk_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/musicatk_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/musicatk_1.8.0.tgz vignettes: vignettes/musicatk/inst/doc/musicatk.html vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/musicatk/inst/doc/musicatk.R dependencyCount: 269 Package: MutationalPatterns Version: 3.8.1 Depends: R (>= 4.2.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>= 1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3), tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr (>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2), ggalluvial (>= 0.12.2), RColorBrewer, methods Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR (>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ff3a022f803fea68752cd1e3529f178b NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. biocViews: Genetics, SomaticMutation Author: Freek Manders [aut] (), Francis Blokzijl [aut] (), Roel Janssen [aut] (), Jurrian de Kanter [ctb] (), Rurika Oka [ctb] (), Mark van Roosmalen [cre], Ruben van Boxtel [aut, cph] (), Edwin Cuppen [aut] () Maintainer: Mark van Roosmalen URL: https://doi.org/doi:10.1186/s12864-022-08357-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: RELEASE_3_16 git_last_commit: b3e71ed git_last_commit_date: 2023-01-26 Date/Publication: 2023-01-26 source.ver: src/contrib/MutationalPatterns_3.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/MutationalPatterns_3.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/MutationalPatterns_3.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MutationalPatterns_3.8.1.tgz vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R suggestsMe: SUITOR dependencyCount: 129 Package: MVCClass Version: 1.72.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 8d2bdf02c38adac4521340159fb18f2c NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: RELEASE_3_16 git_last_commit: d20ad72 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MVCClass_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MVCClass_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MVCClass_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MVCClass_1.72.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.22.0 Depends: R (>= 3.5.0) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 MD5sum: 16c129f83577919dfc4443a2b71e7deb NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: RELEASE_3_16 git_last_commit: 285588c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/MWASTools_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/MWASTools_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/MWASTools_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/MWASTools_1.22.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 129 Package: mygene Version: 1.34.0 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: d6b637ff838fa5d7e12912878e8d380e NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: RELEASE_3_16 git_last_commit: dd08034 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mygene_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mygene_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mygene_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mygene_1.34.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal dependencyCount: 144 Package: myvariant Version: 1.28.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 14657acfd1876685469119cf20b73b3b NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: RELEASE_3_16 git_last_commit: 2978017 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/myvariant_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/myvariant_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/myvariant_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/myvariant_1.28.0.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 142 Package: mzID Version: 1.36.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: c61127d1e1a9e98c1f89fa02d7ba0c3d NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (), Thomas Pedersen [aut] (), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: RELEASE_3_16 git_last_commit: d6525ed git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mzID_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mzID_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mzID_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mzID_1.36.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R importsMe: MSnbase, MSnID, TargetDecoy suggestsMe: mzR, PSMatch, RforProteomics dependencyCount: 11 Package: mzR Version: 2.32.0 Depends: R (>= 4.0.0), Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML, rmarkdown License: Artistic-2.0 MD5sum: 9ae88dede29e362edba9ab68858a38c2 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: RELEASE_3_16 git_last_commit: ef57d59 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/mzR_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/mzR_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/mzR_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/mzR_2.32.0.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSnbase importsMe: adductomicsR, CluMSID, DIAlignR, MSnID, msPurity, peakPantheR, SIMAT, TargetDecoy, topdownr, xcms, yamss suggestsMe: AnnotationHub, MsBackendRawFileReader, MsExperiment, PSMatch, qcmetrics, Spectra, msdata, RforProteomics, chromConverter, erah dependencyCount: 10 Package: NADfinder Version: 1.22.0 Depends: R (>= 3.5.0), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager, rmarkdown License: GPL (>= 2) Archs: x64 MD5sum: 5e5188173dba2b30d63142df8335d3a8 NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: RELEASE_3_16 git_last_commit: 067b49b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NADfinder_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NADfinder_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NADfinder_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NADfinder_1.22.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 245 Package: NanoMethViz Version: 2.4.2 Depends: R (>= 4.0.0), methods, ggplot2 Imports: cpp11 (>= 0.2.5), readr, S4Vectors, SummarizedExperiment, BiocSingular, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, data.table, e1071, fs, GenomicRanges, ggrastr, glue, graphics, limma (>= 3.44.0), patchwork, purrr, rlang, R.utils, RSQLite, Rsamtools, scales (>= 1.2.0), scico, stats, stringr, tibble, tidyr, utils, withr, zlibbioc LinkingTo: Rcpp Suggests: DSS, Mus.musculus (>= 1.3.1), Homo.sapiens (>= 1.3.1), org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, knitr, rmarkdown, rtracklayer, testthat (>= 3.0.0), covr License: Apache License (>= 2.0) MD5sum: e335b5023ac96e95b570ec577908b3bc NeedsCompilation: yes Title: Visualise methlation data from Oxford Nanopore sequencing Description: NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features. biocViews: Software, Visualization, DifferentialMethylation Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues git_url: https://git.bioconductor.org/packages/NanoMethViz git_branch: RELEASE_3_16 git_last_commit: 8db7fbf git_last_commit_date: 2023-02-13 Date/Publication: 2023-02-13 source.ver: src/contrib/NanoMethViz_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoMethViz_2.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoMethViz_2.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NanoMethViz_2.4.2.tgz vignettes: vignettes/NanoMethViz/inst/doc/DimensionalityReduction.html, vignettes/NanoMethViz/inst/doc/ExonAnnotations.html, vignettes/NanoMethViz/inst/doc/ImportingExportingData.html, vignettes/NanoMethViz/inst/doc/Introduction.html vignetteTitles: Dimensionality Reduction, Exon Annotations, Importing/Exporting Data, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/DimensionalityReduction.R, vignettes/NanoMethViz/inst/doc/ExonAnnotations.R, vignettes/NanoMethViz/inst/doc/ImportingExportingData.R, vignettes/NanoMethViz/inst/doc/Introduction.R dependencyCount: 144 Package: NanoStringDiff Version: 1.28.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL Archs: x64 MD5sum: 3c9b3f3c442350bddfdce7ea57a39636 NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: RELEASE_3_16 git_last_commit: d4be9e4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NanoStringDiff_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoStringDiff_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoStringDiff_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NanoStringDiff_1.28.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R suggestsMe: NanoTube dependencyCount: 8 Package: NanoStringNCTools Version: 1.6.1 Depends: R (>= 3.6), Biobase, S4Vectors, ggplot2 Imports: BiocGenerics, Biostrings, ggbeeswarm, ggiraph, ggthemes, grDevices, IRanges, methods, pheatmap, RColorBrewer, stats, utils Suggests: biovizBase, ggbio, RUnit, rmarkdown, knitr, qpdf License: MIT Archs: x64 MD5sum: d988ce152a99d853768d79aec106698e NeedsCompilation: no Title: NanoString nCounter Tools Description: Tools for NanoString Technologies nCounter Technology. Provides support for reading RCC files into an ExpressionSet derived object. Also includes methods for QC and normalizaztion of NanoString data. biocViews: GeneExpression, Transcription, CellBasedAssays, DataImport, Transcriptomics, Proteomics, mRNAMicroarray, ProprietaryPlatforms, RNASeq Author: Patrick Aboyoun [aut], Nicole Ortogero [cre], Zhi Yang [ctb] Maintainer: Nicole Ortogero VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringNCTools git_branch: RELEASE_3_16 git_last_commit: d39ff7b git_last_commit_date: 2023-01-25 Date/Publication: 2023-01-25 source.ver: src/contrib/NanoStringNCTools_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoStringNCTools_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoStringNCTools_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NanoStringNCTools_1.6.1.tgz vignettes: vignettes/NanoStringNCTools/inst/doc/Introduction.html vignetteTitles: Introduction to the NanoStringRCCSet Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoStringNCTools/inst/doc/Introduction.R dependsOnMe: GeomxTools, GeoMxWorkflows importsMe: GeoDiff dependencyCount: 85 Package: NanoStringQCPro Version: 1.30.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: ed7ff1dca1386be4f3bcaddf079ba067 NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringQCPro git_branch: RELEASE_3_16 git_last_commit: d6922c8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NanoStringQCPro_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoStringQCPro_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoStringQCPro_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NanoStringQCPro_1.30.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 92 Package: nanotatoR Version: 1.14.0 Depends: R (>= 4.1), Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats, rlang, stringr, knitr, testthat, utils, AnnotationDbi, httr, GenomicRanges, tidyverse, VarfromPDB, org.Hs.eg.db, curl, dplyr, XML, XML2R Suggests: rmarkdown, yaml License: file LICENSE MD5sum: f4add801d9f5be7743e5b22427dd6179 NeedsCompilation: no Title: Next generation structural variant annotation and classification Description: Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions (INDELs) and, more recently, small copy number variants (CNVs). However, due to utilization of short reads, it is not well suited for identification of structural variants (SV). Optical mapping (OM) from Bionano Genomics, utilizes long fluorescently labeled megabase size DNA molecules for de novo genome assembly and identification of SVs with a much higher sensitivity than WGS. Nevertheless, currently available SV annotation tools have limited number of functions. NanotatoR is an R package written to provide a set of annotations for SVs identified by OM. It uses Database of Genomic Variants (DGV), Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources (DECIPHER) as well as a subset (154 samples) of 1000 Genome Project to calculate the population frequencies of the SVs (an optional internal cohort SV frequency calculation is also available). NanotatoR creates a primary gene list (PG) from NCBI databases based on proband’s phenotype specific keywords and compares the list to the set of genes overlapping/near SVs. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type (e.g., INDELs, Inversions, Translocations). Users then have a choice to filter SVs using the provided annotations for de novo (if parental samples are available) or inherited rare variants. biocViews: Software, WorkflowStep, GenomeAssembly, VariantAnnotation Author: Surajit Bhattacharya, Hayk Barsheghyan, Emmanuele C Delot and Eric Vilain Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/nanotatoR VignetteBuilder: knitr BugReports: https://github.com/VilainLab/nanotatoR/issues git_url: https://git.bioconductor.org/packages/nanotatoR git_branch: RELEASE_3_16 git_last_commit: b95ab56 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nanotatoR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nanotatoR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nanotatoR_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nanotatoR_1.14.0.tgz vignettes: vignettes/nanotatoR/inst/doc/nanotatoR.html vignetteTitles: nanotatoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nanotatoR/inst/doc/nanotatoR.R dependencyCount: 152 Package: NanoTube Version: 1.4.0 Depends: R (>= 4.1), Biobase, ggplot2, limma Imports: fgsea, methods, reshape, stats, utils Suggests: grid, kableExtra, knitr, NanoStringDiff, pheatmap, plotly, rlang, rmarkdown, ruv, RUVSeq, shiny, testthat, xlsx License: GPL-3 + file LICENSE MD5sum: 06a5830db2f083f0fc705764df674a42 NeedsCompilation: no Title: An Easy Pipeline for NanoString nCounter Data Analysis Description: NanoTube includes functions for the processing, quality control, analysis, and visualization of NanoString nCounter data. Analysis functions include differential analysis and gene set analysis methods, as well as postprocessing steps to help understand the results. Additional functions are included to enable interoperability with other Bioconductor NanoString data analysis packages. biocViews: Software, GeneExpression, DifferentialExpression, QualityControl Author: Caleb Class [cre, aut] (), Caiden Lukan [ctb] Maintainer: Caleb Class VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoTube git_branch: RELEASE_3_16 git_last_commit: 7d37b55 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NanoTube_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NanoTube_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NanoTube_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NanoTube_1.4.0.tgz vignettes: vignettes/NanoTube/inst/doc/NanoTube.html vignetteTitles: NanoTube Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NanoTube/inst/doc/NanoTube.R dependencyCount: 55 Package: NBAMSeq Version: 1.14.0 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: 86f836dfe9b4e1d16e52e803a7006d34 NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: RELEASE_3_16 git_last_commit: 6d83c7e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NBAMSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NBAMSeq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NBAMSeq_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NBAMSeq_1.14.0.tgz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 93 Package: NBSplice Version: 1.15.0 Depends: R (>= 3.5), methods Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2 Suggests: knitr, RUnit, BiocGenerics, BiocStyle, rmarkdown, markdown License: GPL (>=2) MD5sum: 9c7fc77071d1c21ffda86d62fcda008b NeedsCompilation: no Title: Negative Binomial Models to detect Differential Splicing Description: The package proposes a differential splicing evaluation method based on isoform quantification. It applies generalized linear models with negative binomial distribution to infer changes in isoform relative expression. biocViews: Software, StatisticalMethod, AlternativeSplicing, Regression, DifferentialExpression, DifferentialSplicing, RNASeq, ImmunoOncology Author: Gabriela A. Merino and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NBSplice git_branch: master git_last_commit: 06141d9 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/NBSplice_1.15.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NBSplice_1.15.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NBSplice_1.15.0.tgz vignettes: vignettes/NBSplice/inst/doc/NBSplice-vignette.html vignetteTitles: NBSplice-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBSplice/inst/doc/NBSplice-vignette.R dependencyCount: 95 Package: ncdfFlow Version: 2.44.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: cpp11,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: AGPL-3.0-only MD5sum: 24caf0dba4802c27180be2f7a7eb133a NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_16 git_last_commit: 9d6dbfa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ncdfFlow_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ncdfFlow_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ncdfFlow_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ncdfFlow_2.44.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace suggestsMe: COMPASS, cydar dependencyCount: 18 Package: ncGTW Version: 1.12.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 Archs: x64 MD5sum: 818305e642a0b0d4cee7026e2aab41bc NeedsCompilation: yes Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: RELEASE_3_16 git_last_commit: d282d41 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ncGTW_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ncGTW_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ncGTW_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ncGTW_1.12.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 93 Package: NCIgraph Version: 1.46.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 59760cbed2c32b6220be0f40607b9b6e NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: RELEASE_3_16 git_last_commit: 5f1a0bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NCIgraph_1.46.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 57 Package: ncRNAtools Version: 1.8.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 Archs: x64 MD5sum: a64aafbb2f826626d199d77de5465dfb NeedsCompilation: no Title: An R toolkit for non-coding RNA Description: ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, StructuralPrediction Author: Lara Selles Vidal [cre, aut] (), Rafael Ayala [aut] (), Guy-Bart Stan [aut] (), Rodrigo Ledesma-Amaro [aut] () Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues git_url: https://git.bioconductor.org/packages/ncRNAtools git_branch: RELEASE_3_16 git_last_commit: d37b901 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ncRNAtools_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ncRNAtools_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ncRNAtools_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ncRNAtools_1.8.0.tgz vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R dependencyCount: 55 Package: ndexr Version: 1.20.1 Depends: RCX Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD_3_clause MD5sum: fc176737899996278d88c1ee56b72859 NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer , Frank Kramer , Alex Ishkin , Dexter Pratt Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: RELEASE_3_16 git_last_commit: 7b81219 git_last_commit_date: 2022-12-09 Date/Publication: 2022-12-09 source.ver: src/contrib/ndexr_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ndexr_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ndexr_1.20.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ndexr_1.20.1.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R suggestsMe: netgsa dependencyCount: 41 Package: nearBynding Version: 1.8.0 Depends: R (>= 4.0) Imports: R.utils, matrixStats, plyranges, transport, Rsamtools, S4Vectors, grDevices, graphics, rtracklayer, dplyr, GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots, BiocGenerics, rlang Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 50a5c8c255d5023f3ae778db6027a995 NeedsCompilation: no Title: Discern RNA structure proximal to protein binding Description: Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats. biocViews: Visualization, MotifDiscovery, DataRepresentation, StructuralPrediction, Clustering, MultipleComparison Author: Veronica Busa [cre] Maintainer: Veronica Busa SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.22), CapR (>= 1.1.1) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nearBynding git_branch: RELEASE_3_16 git_last_commit: 5cd681f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nearBynding_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nearBynding_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nearBynding_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nearBynding_1.8.0.tgz vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf vignetteTitles: nearBynding Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R dependencyCount: 124 Package: Nebulosa Version: 1.8.0 Depends: R (>= 4.0), ggplot2, patchwork Imports: Seurat, SingleCellExperiment, SummarizedExperiment, ks, Matrix, stats, methods Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache, SeuratObject License: GPL-3 MD5sum: 65d85f9bdeea5af2e5197f777d885483 NeedsCompilation: no Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation Description: This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. biocViews: Software, GeneExpression, SingleCell, Visualization, DimensionReduction Author: Jose Alquicira-Hernandez [aut, cre] () Maintainer: Jose Alquicira-Hernandez URL: https://github.com/powellgenomicslab/Nebulosa VignetteBuilder: knitr BugReports: https://github.com/powellgenomicslab/Nebulosa/issues git_url: https://git.bioconductor.org/packages/Nebulosa git_branch: RELEASE_3_16 git_last_commit: 690d1d3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Nebulosa_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Nebulosa_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Nebulosa_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Nebulosa_1.8.0.tgz vignettes: vignettes/Nebulosa/inst/doc/introduction.html, vignettes/Nebulosa/inst/doc/nebulosa_seurat.html vignetteTitles: Visualization of gene expression with Nebulosa, Visualization of gene expression with Nebulosa (in Seurat) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Nebulosa/inst/doc/introduction.R, vignettes/Nebulosa/inst/doc/nebulosa_seurat.R suggestsMe: scCustomize, SCpubr dependencyCount: 174 Package: NeighborNet Version: 1.16.0 Depends: methods Imports: graph, stats License: CC BY-NC-ND 4.0 MD5sum: 8a264cc3bd45dd9414275aab20c9e085 NeedsCompilation: no Title: Neighbor_net analysis Description: Identify the putative mechanism explaining the active interactions between genes in the investigated phenotype. biocViews: Software, GeneExpression, StatisticalMethod, GraphAndNetwork Author: Sahar Ansari and Sorin Draghici Maintainer: Sahar Ansari git_url: https://git.bioconductor.org/packages/NeighborNet git_branch: RELEASE_3_16 git_last_commit: 195155b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NeighborNet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NeighborNet_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NeighborNet_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NeighborNet_1.16.0.tgz vignettes: vignettes/NeighborNet/inst/doc/neighborNet.pdf vignetteTitles: NeighborNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeighborNet/inst/doc/neighborNet.R dependencyCount: 7 Package: nempi Version: 1.6.0 Depends: R (>= 4.1), mnem Imports: e1071, nnet, randomForest, naturalsort, graphics, stats, utils, matrixStats, epiNEM Suggests: knitr, BiocGenerics, rmarkdown, RUnit License: GPL-3 MD5sum: eb3e264895e520c00b2babd848eafcd6 NeedsCompilation: no Title: Inferring unobserved perturbations from gene expression data Description: Takes as input an incomplete perturbation profile and differential gene expression in log odds and infers unobserved perturbations and augments observed ones. The inference is done by iteratively inferring a network from the perturbations and inferring perturbations from the network. The network inference is done by Nested Effects Models. biocViews: Software, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneSignaling, Pathways, Network, Classification, NeuralNetwork, NetworkInference, ATACSeq, DNASeq, RNASeq, PooledScreens, CRISPR, SingleCell, SystemsBiology Author: Martin Pirkl [aut, cre] Maintainer: Martin Pirkl URL: https://github.com/cbg-ethz/nempi/ VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/nempi/issues git_url: https://git.bioconductor.org/packages/nempi git_branch: RELEASE_3_16 git_last_commit: 443a6b4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nempi_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nempi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nempi_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nempi_1.6.0.tgz vignettes: vignettes/nempi/inst/doc/nempi.html vignetteTitles: nempi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nempi/inst/doc/nempi.R dependencyCount: 110 Package: NetActivity Version: 1.0.0 Depends: R (>= 4.1.0) Imports: airway, DelayedArray, DelayedMatrixStats, DESeq2, methods, methods, NetActivityData, SummarizedExperiment, utils Suggests: AnnotationDbi, BiocStyle, Fletcher2013a, knitr, org.Hs.eg.db, rmarkdown, testthat (>= 3.0.0), tidyverse License: MIT + file LICENSE MD5sum: 40094b89a7632cb31f9566d3b828bbcc NeedsCompilation: no Title: Compute gene set scores from a deep learning framework Description: #' NetActivity enables to compute gene set scores from previously trained sparsely-connected autoencoders. The package contains a function to prepare the data (`prepareSummarizedExperiment`) and a function to compute the gene set scores (`computeGeneSetScores`). The package `NetActivityData` contains different pre-trained models to be directly applied to the data. Alternatively, the users might use the package to compute gene set scores using custom models. biocViews: RNASeq, Microarray, Transcription, FunctionalGenomics, GO, GeneExpression, Pathways, Software Author: Carlos Ruiz-Arenas [aut, cre] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetActivity git_branch: RELEASE_3_16 git_last_commit: 639ed02 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NetActivity_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NetActivity_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NetActivity_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NetActivity_1.0.0.tgz vignettes: vignettes/NetActivity/inst/doc/NetActivity.html vignetteTitles: "Gene set scores computation with NetActivity" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NetActivity/inst/doc/NetActivity.R dependencyCount: 95 Package: netbiov Version: 1.31.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: d6911966ff8f74219e8087f6f0ea7a6a NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com git_url: https://git.bioconductor.org/packages/netbiov git_branch: master git_last_commit: 34349f7 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netbiov_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netbiov_1.31.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netbiov_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netbiov_1.32.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R dependencyCount: 13 Package: netboost Version: 2.6.1 Depends: R (>= 4.0.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr, markdown, rmarkdown License: GPL-3 OS_type: unix MD5sum: 1d590bba5a6b1c2365dfa7eaf978a18d NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: pascal.schlosser@uniklinik-freiburg.de git_url: https://git.bioconductor.org/packages/netboost git_branch: RELEASE_3_16 git_last_commit: 3b6aa9a git_last_commit_date: 2023-01-27 Date/Publication: 2023-01-29 source.ver: src/contrib/netboost_2.6.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/netboost_2.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netboost_2.6.1.tgz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 120 Package: netboxr Version: 1.9.0 Depends: R (>= 4.0.0), igraph (>= 1.2.4.1), parallel Imports: RColorBrewer, DT, stats, clusterProfiler, data.table, gplots, jsonlite, plyr Suggests: paxtoolsr, BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, cgdsr License: LGPL-3 + file LICENSE MD5sum: 330e11b123222efb899fdbda87e62272 NeedsCompilation: no Title: netboxr Description: NetBox is a network-based approach that combines prior knowledge with a network clustering algorithm. The algorithm allows for the identification of functional modules and allows for combining multiple data types, such as mutations and copy number alterations. NetBox performs network analysis on human interaction networks, and comes pre-loaded with a Human Interaction Network (HIN) derived from four literature curated data sources, including the Human Protein Reference Database (HPRD), Reactome, NCI-Nature Pathway Interaction (PID) Database, and the MSKCC Cancer Cell Map. biocViews: Software,Network,Pathways,GraphAndNetwork,Reactome, SystemsBiology, GeneSetEnrichment, NetworkEnrichment, KEGG Author: Eric Minwei Liu [aut,cre], Augustin Luna [aut], Ethan Cerami [aut], Chris Sander [aut] Maintainer: Eirc Minwei Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netboxr git_branch: master git_last_commit: f9dbdac git_last_commit_date: 2022-04-26 Date/Publication: 2022-08-24 source.ver: src/contrib/netboxr_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netboxr_1.9.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netboxr_1.9.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netboxr_1.10.0.tgz vignettes: vignettes/netboxr/inst/doc/netboxrTutorial.html vignetteTitles: NetBoxR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netboxr/inst/doc/netboxrTutorial.R dependencyCount: 154 Package: netDx Version: 1.9.0 Depends: R (>= 3.6) Imports: ROCR,pracma,ggplot2,glmnet,igraph,reshape2, parallel,stats,utils,MultiAssayExperiment,graphics,grDevices, methods,BiocFileCache,GenomicRanges, bigmemory,doParallel,foreach, combinat,rappdirs,GenomeInfoDb,S4Vectors, IRanges,RColorBrewer,Rtsne,httr,plotrix Suggests: curatedTCGAData, rmarkdown, testthat, knitr, BiocStyle, RCy3, clusterExperiment, netSmooth, scater License: MIT + file LICENSE MD5sum: 8eca5eebaff3e1df575aa96dbb492abf NeedsCompilation: no Title: Network-based patient classifier Description: netDx is a general-purpose algorithm to build a patient classifier from heterogenous patient data. The method converts data into patient similarity networks at the level of features. Feature selection identifies features of predictive value to each class. Methods are provided for versatile predictor design and performance evaluation using standard measures. netDx natively groups molecular data into pathway-level features and connects with Cytoscape for network visualization of pathway themes. For method details see: Pai et al. (2019). netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology. 15, e8497 biocViews: Classification, BiomedicalInformatics, Network, SystemsBiology Author: Shraddha Pai [aut, cre] (), Philipp Weber [aut], Ahmad Shah [aut], Luca Giudice [aut], Shirley Hui [aut], Anne Nøhr [ctb], Indy Ng [ctb], Ruth Isserlin [aut], Hussam Kaka [aut], Gary Bader [aut] Maintainer: Shraddha Pai URL: http://netdx.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netDx git_branch: master git_last_commit: 491b7a6 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/netDx_1.9.0.tar.gz vignettes: vignettes/netDx/inst/doc/RawDataConversion.html, vignettes/netDx/inst/doc/ThreeWayClassifier.html vignetteTitles: 02. Running netDx with data in table format, 01. Build & test classifier with clinical and multi-omic data & pathway features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netDx/inst/doc/RawDataConversion.R, vignettes/netDx/inst/doc/ThreeWayClassifier.R dependencyCount: 108 Package: nethet Version: 1.30.0 Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 MD5sum: 8526c28e957cf9037965a950dedc41ba NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: RELEASE_3_16 git_last_commit: 2dc51e7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nethet_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nethet_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nethet_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nethet_1.30.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 74 Package: netOmics Version: 1.4.0 Depends: R (>= 4.1) Imports: dplyr, ggplot2, igraph, magrittr, minet, purrr, tibble, tidyr, AnnotationDbi, GO.db, RandomWalkRestartMH, gprofiler2, methods, stats Suggests: mixOmics, timeOmics, tidyverse, BiocStyle, testthat, covr, rmarkdown, knitr License: GPL-3 Archs: x64 MD5sum: c4ad3bd9a8ac03b87bebc63a480ba919 NeedsCompilation: no Title: Multi-Omics (time-course) network-based integration and interpretation Description: netOmics is a multi-omics networks builder and explorer. It uses a combination of network inference algorithms and and knowledge-based graphs to build multi-layered networks. The package can be combined with timeOmics to incorporate time-course expression data and build sub-networks from multi-omics kinetic clusters. Finally, from the generated multi-omics networks, propagation analyses allow the identification of missing biological functions (1), multi-omics mechanisms (2) and molecules between kinetic clusters (3). This helps to resolve complex regulatory mechanisms. biocViews: GraphAndNetwork, Software, TimeCourse, WorkflowStep, SystemsBiology, NetworkInference, Network Author: Antoine Bodein [aut, cre] Maintainer: Antoine Bodein URL: https://github.com/abodein/netOmics VignetteBuilder: knitr BugReports: https://github.com/abodein/netOmics/issues git_url: https://git.bioconductor.org/packages/netOmics git_branch: RELEASE_3_16 git_last_commit: 0f6398e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/netOmics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netOmics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netOmics_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netOmics_1.4.0.tgz vignettes: vignettes/netOmics/inst/doc/netOmics.html vignetteTitles: netOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netOmics/inst/doc/netOmics.R dependencyCount: 122 Package: NetPathMiner Version: 1.34.2 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: d1ef3c92dd8901fc62744f2bc4c1bc92 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: RELEASE_3_16 git_last_commit: 4612114 git_last_commit_date: 2023-02-14 Date/Publication: 2023-02-15 source.ver: src/contrib/NetPathMiner_1.34.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/NetPathMiner_1.34.2.zip mac.binary.ver: bin/macosx/contrib/4.2/NetPathMiner_1.34.2.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 13 Package: netprioR Version: 1.24.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 Archs: x64 MD5sum: 92f9247970d5719e68ed67e5d975e1e5 NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: RELEASE_3_16 git_last_commit: b72e5f4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/netprioR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netprioR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netprioR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netprioR_1.24.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R dependencyCount: 48 Package: netresponse Version: 1.58.0 Depends: R (>= 2.15.1), BiocStyle, Rgraphviz, rmarkdown, methods, minet, mclust, reshape2 Imports: ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) MD5sum: b060124e28ae427e3870d8bafc382f76 NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, NetworkInference, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse VignetteBuilder: knitr BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: RELEASE_3_16 git_last_commit: acfe5bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/netresponse_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netresponse_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netresponse_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netresponse_1.58.0.tgz vignettes: vignettes/netresponse/inst/doc/NetResponse.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/NetResponse.R dependencyCount: 77 Package: NetSAM Version: 1.38.0 Depends: R (>= 3.0.0), seriation (>= 1.0-6), igraph (>= 0.6-1), tools (>= 3.0.0), WGCNA (>= 1.34.0), biomaRt (>= 2.18.0) Imports: methods, AnnotationDbi (>= 1.28.0), doParallel (>= 1.0.10), foreach (>= 1.4.0), survival (>= 2.37-7), GO.db (>= 2.10.0), R2HTML (>= 2.2.0), DBI (>= 0.5-1) Suggests: RUnit, BiocGenerics, org.Sc.sgd.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Dr.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.At.tair.db, rmarkdown, knitr, markdown License: LGPL Archs: x64 MD5sum: cf65c21f15b5a0d7e1974a1444604a8c NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a weighted or unweighted network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. The NetSAM package can also generate correlation network (e.g. co-expression network) based on the input matrix data, perform seriation and modularization analysis for the correlation network and calculate the associations between the sample features and modules or identify the associated GO terms for the modules. Author: Jing Wang Maintainer: Zhiao Shi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetSAM git_branch: RELEASE_3_16 git_last_commit: dd9a6db git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NetSAM_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NetSAM_1.37.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NetSAM_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NetSAM_1.38.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 138 Package: netSmooth Version: 1.18.0 Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array (>= 1.15.13) Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 MD5sum: f71307c594d18c5289745ff32b7a39c9 NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: RELEASE_3_16 git_last_commit: 0faef91 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/netSmooth_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netSmooth_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netSmooth_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netSmooth_1.18.0.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: Generation of PPI graph, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R suggestsMe: netDx dependencyCount: 176 Package: netZooR Version: 1.2.0 Depends: R (>= 4.2.0), igraph, reticulate, pandaR, yarn Imports: RCy3, viridisLite, STRINGdb, Biobase, GOstats, AnnotationDbi, matrixStats, GO.db, org.Hs.eg.db, Matrix, gplots, nnet, data.table, vegan, stats, utils, reshape, reshape2, penalized, parallel, doParallel, foreach, ggplot2, ggdendro, grid, MASS, assertthat, tidyr, methods, dplyr, graphics Suggests: testthat (>= 2.1.0), knitr, rmarkdown, pkgdown License: GPL-3 MD5sum: 321ba1f8cf38867733ad06da95546a4b NeedsCompilation: no Title: Unified methods for the inference and analysis of gene regulatory networks Description: netZooR unifies the implementations of several Network Zoo methods (netzoo, netzoo.github.io) into a single package by creating interfaces between network inference and network analysis methods. Currently, the package has 3 methods for network inference including PANDA and its optimized implementation OTTER (network reconstruction using mutliple lines of biological evidence), LIONESS (single-sample network inference), and EGRET (genotype-specific networks). Network analysis methods include CONDOR (community detection), ALPACA (differential community detection), CRANE (significance estimation of differential modules), MONSTER (estimation of network transition states). In addition, YARN allows to process gene expresssion data for tissue-specific analyses and SAMBAR infers missing mutation data based on pathway information. biocViews: NetworkInference, Network, GeneRegulation, GeneExpression, Transcription, Microarray, GraphAndNetwork Author: Marouen Ben Guebila [aut, cre] (), Tian Wang [aut] (), John Platig [aut], Marieke Kuijjer [aut] (), Megha Padi [aut] (), Rebekka Burkholz [aut], Des Weighill [aut] (), Kate Shutta [ctb] () Maintainer: Marouen Ben Guebila URL: https://github.com/netZoo/netZooR, https://netzoo.github.io/ VignetteBuilder: knitr BugReports: https://github.com/netZoo/netZooR/issues git_url: https://git.bioconductor.org/packages/netZooR git_branch: RELEASE_3_16 git_last_commit: 65d77ca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/netZooR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/netZooR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/netZooR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/netZooR_1.2.0.tgz vignettes: vignettes/netZooR/inst/doc/CONDOR.html vignetteTitles: CONDOR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netZooR/inst/doc/CONDOR.R dependencyCount: 207 Package: NeuCA Version: 1.4.0 Depends: R(>= 3.5.0), keras, limma, e1071, SingleCellExperiment Suggests: BiocStyle, knitr, rmarkdown, networkD3 License: GPL-2 MD5sum: 5313b43838e827fc634644c8c78c98f8 NeedsCompilation: no Title: NEUral network-based single-Cell Annotation tool Description: NeuCA is is a neural-network based method for scRNA-seq data annotation. It can automatically adjust its classification strategy depending on cell type correlations, to accurately annotate cell. NeuCA can automatically utilize the structure information of the cell types through a hierarchical tree to improve the annotation accuracy. It is especially helpful when the data contain closely correlated cell types. biocViews: SingleCell, Software, Classification, NeuralNetwork, RNASeq, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Ziyi Li [aut], Hao Feng [aut, cre] Maintainer: Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NeuCA git_branch: RELEASE_3_16 git_last_commit: b993a5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NeuCA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NeuCA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NeuCA_1.4.0.tgz vignettes: vignettes/NeuCA/inst/doc/NeuCA.html vignetteTitles: NeuCA Package User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeuCA/inst/doc/NeuCA.R dependencyCount: 62 Package: NewWave Version: 1.8.0 Depends: R (>= 4.0), SummarizedExperiment Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, SharedObject, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 MD5sum: d130baf9dc1f65cf813c0ac810c08705 NeedsCompilation: no Title: Negative binomial model for scRNA-seq Description: A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. biocViews: Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Sequencing, Coverage, Regression Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] Maintainer: Federico Agostinis VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues git_url: https://git.bioconductor.org/packages/NewWave git_branch: RELEASE_3_16 git_last_commit: 633f457 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NewWave_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NewWave_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NewWave_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NewWave_1.8.0.tgz vignettes: vignettes/NewWave/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NewWave/inst/doc/vignette.R dependencyCount: 43 Package: ngsReports Version: 2.0.3 Depends: R (>= 4.2.0), BiocGenerics, ggplot2 (>= 3.4.0), patchwork (>= 1.1.1), tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.0.0), DT, forcats, ggdendro, grDevices (>= 3.6.0), grid, lifecycle, lubridate, methods, pander, plotly (>= 4.9.4), readr, reshape2, rlang, rmarkdown, scales, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, zoo Suggests: BiocStyle, Cairo, knitr, testthat, truncnorm License: file LICENSE MD5sum: d4d7e4ca596e40ea607e88f0df4ff411 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R. As well as parsing files, multiple plotting methods have been implemented for visualising the parsed data. Plots can be generated as static ggplot objects or interactive plotly objects. biocViews: QualityControl, ReportWriting Author: Stephen Pederson [aut, cre], Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Stephen Pederson URL: https://github.com/steveped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/steveped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: RELEASE_3_16 git_last_commit: 244b086 git_last_commit_date: 2023-01-13 Date/Publication: 2023-01-13 source.ver: src/contrib/ngsReports_2.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/ngsReports_2.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/ngsReports_2.0.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ngsReports_2.0.3.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 112 Package: nnNorm Version: 2.62.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: b760b319dd5b2636cd93ab2411602821 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: RELEASE_3_16 git_last_commit: f563554 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nnNorm_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nnNorm_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nnNorm_2.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nnNorm_2.62.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 8 Package: nnSVG Version: 1.2.0 Depends: R (>= 4.2) Imports: SpatialExperiment, SingleCellExperiment, SummarizedExperiment, BRISC, BiocParallel, Matrix, matrixStats, stats, methods Suggests: BiocStyle, knitr, rmarkdown, STexampleData, scran, ggplot2, testthat License: MIT + file LICENSE MD5sum: d42af5f6f0c52ce3081fb37abf439706 NeedsCompilation: no Title: Scalable identification of spatially variable genes in spatially-resolved transcriptomics data Description: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or more spatial locations. biocViews: Spatial, SingleCell, Transcriptomics, GeneExpression, Preprocessing Author: Lukas M. Weber [aut, cre] (), Stephanie C. Hicks [aut] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/nnSVG VignetteBuilder: knitr BugReports: https://github.com/lmweber/nnSVG/issues git_url: https://git.bioconductor.org/packages/nnSVG git_branch: RELEASE_3_16 git_last_commit: 5c7178a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nnSVG_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nnSVG_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nnSVG_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nnSVG_1.2.0.tgz vignettes: vignettes/nnSVG/inst/doc/nnSVG.html vignetteTitles: nnSVG tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nnSVG/inst/doc/nnSVG.R dependencyCount: 102 Package: NOISeq Version: 2.42.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 433f6d077210efad072673e9abda93fa NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: RELEASE_3_16 git_last_commit: b92ee86 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NOISeq_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NOISeq_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NOISeq_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NOISeq_2.42.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: benchdamic, CNVPanelizer, ExpHunterSuite suggestsMe: compcodeR dependencyCount: 11 Package: nondetects Version: 2.28.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: 21c17d748286c53340304be7077590d8 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nondetects git_branch: RELEASE_3_16 git_last_commit: bb41a27 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nondetects_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nondetects_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nondetects_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nondetects_2.28.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 70 Package: NoRCE Version: 1.10.0 Depends: R (>= 4.2.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices,stringr,GenomeInfoDb, S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl, dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db,zlibbioc, biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi Suggests: knitr, TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene, testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown, TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics, org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, methods,markdown License: MIT + file LICENSE MD5sum: 0250f1d2e443bdb4393d29bba31ecaaa NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) are assigned critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic proximity on the sequence can hint to a functional association. We present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. Other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information can be incorporated to conduct a richer enrichment analysis. To this end, NoRCE includes several relevant datasets as part of its data repository, including cell-line specific TAD boundaries, functional gene sets, and expression data for coding & ncRNAs specific to cancer. Additionally, the users can utilize custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. biocViews: BiologicalQuestion, DifferentialExpression, GenomeAnnotation, GeneSetEnrichment, GeneTarget, GenomeAssembly, GO Author: Gulden Olgun [aut, cre] Maintainer: Gulden Olgun VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: RELEASE_3_16 git_last_commit: 39a8626 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NoRCE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NoRCE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NoRCE_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NoRCE_1.10.0.tgz vignettes: vignettes/NoRCE/inst/doc/NoRCE.html vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R dependencyCount: 122 Package: normalize450K Version: 1.26.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 924352a1df046043ccd03fb808a40552 NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: RELEASE_3_16 git_last_commit: f368620 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/normalize450K_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/normalize450K_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/normalize450K_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/normalize450K_1.26.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 12 Package: NormalyzerDE Version: 1.16.0 Depends: R (>= 3.6) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, Biobase, RcmdrMisc, raster, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: 83bcaa765199205037e9c4d01167dc80 NeedsCompilation: no Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Author: Jakob Willforss Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: RELEASE_3_16 git_last_commit: 083d31b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NormalyzerDE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NormalyzerDE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NormalyzerDE_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NormalyzerDE_1.16.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.pdf vignetteTitles: Differential expression and countering technical biases using NormalyzerDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R dependencyCount: 162 Package: NormqPCR Version: 1.44.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: d4d3aa3469db6111c010fff2ee691648 NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: RELEASE_3_16 git_last_commit: 86273c2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NormqPCR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NormqPCR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NormqPCR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NormqPCR_1.44.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R dependencyCount: 51 Package: normr Version: 1.24.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 MD5sum: ea7e051b690756e00a9ec7ec742f4f9b NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues git_url: https://git.bioconductor.org/packages/normr git_branch: RELEASE_3_16 git_last_commit: 8b0a01a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/normr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/normr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/normr_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/normr_1.24.0.tgz vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R dependencyCount: 80 Package: NPARC Version: 1.10.1 Depends: R (>= 4.0.0) Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr, stats, methods Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2, BiocStyle License: GPL-3 Archs: x64 MD5sum: 672dac7cf0b760c014bfab22bc1da289 NeedsCompilation: no Title: Non-parametric analysis of response curves for thermal proteome profiling experiments Description: Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins. biocViews: Software, Proteomics Author: Dorothee Childs, Nils Kurzawa Maintainer: Nils Kurzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: RELEASE_3_16 git_last_commit: 49435bb git_last_commit_date: 2023-03-19 Date/Publication: 2023-03-20 source.ver: src/contrib/NPARC_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/NPARC_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/NPARC_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NPARC_1.10.1.tgz vignettes: vignettes/NPARC/inst/doc/NPARC.html vignetteTitles: Analysing thermal proteome profiling data with the NPARC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NPARC/inst/doc/NPARC.R dependencyCount: 40 Package: npGSEA Version: 1.34.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: d6b8f4012dbb085ae2ecf4519cdf2833 NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: RELEASE_3_16 git_last_commit: ae5a4ea git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/npGSEA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/npGSEA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/npGSEA_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/npGSEA_1.34.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 51 Package: NTW Version: 1.48.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 91f8c0e9c074eacd20f850e9d9908c63 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: RELEASE_3_16 git_last_commit: 8aa0225 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NTW_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NTW_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NTW_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NTW_1.48.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 4 Package: nucleoSim Version: 1.26.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: edcd4a90610d012223c16bc66b9ee7ea NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The synthetic hybridization data of “Tiling Arrays” can also be generated. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. In addition, a visualization tool is provided to explore the synthetic nucleosome maps. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut] (), Pascal Belleau [aut] (), Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: RELEASE_3_16 git_last_commit: 64d80ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nucleoSim_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nucleoSim_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nucleoSim_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nucleoSim_1.26.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 8 Package: nucleR Version: 2.30.0 Depends: R (>= 3.5.0), methods Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: 671e8b924067a623cc3f6b6444d51fd8 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Alba Sala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: RELEASE_3_16 git_last_commit: 8697d8d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nucleR_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nucleR_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nucleR_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nucleR_2.30.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R dependencyCount: 79 Package: nuCpos Version: 1.16.3 Depends: R (>= 4.2.0) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: GPL-2 MD5sum: 9deecfcda71cd4ebd342d4310d619f3b NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. In nuCpos, a duration hidden Markov model is trained with a chemical map of nucleosomes either from budding yeast, fission yeast, or mouse embryonic stem cells. nuCpos outputs the Viterbi (most probable) path of nucleosome-linker states, predicted nucleosome occupancy scores and histone binding affinity (HBA) scores as NuPoP does. nuCpos can also calculate local and whole nucleosomal HBA scores for a given 147-bp sequence. Note: This package was designed to demonstrate the use of chemical maps in prediction. As the parental package NuPoP now provide chemical-map-based prediction, users are strongly encouraged to use it for dHMM-based prediction. biocViews: Genetics, Epigenetics, NucleosomePositioning, HiddenMarkovModel, ImmunoOncology Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: RELEASE_3_16 git_last_commit: d7c5314 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/nuCpos_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/nuCpos_1.16.3.zip mac.binary.ver: bin/macosx/contrib/4.2/nuCpos_1.16.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nuCpos_1.16.3.tgz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: nullranges Version: 1.4.0 Imports: stats, IRanges, GenomicRanges, GenomeInfoDb, methods, rlang, S4Vectors, scales, InteractionSet, ggplot2, grDevices, plyranges, ks, speedglm, data.table, progress, ggridges Suggests: testthat, knitr, rmarkdown, DNAcopy, RcppHMM, AnnotationHub, ExperimentHub, nullrangesData, excluderanges, ensembldb, EnsDb.Hsapiens.v86, microbenchmark, patchwork, plotgardener, magrittr, tidyr, cobalt License: GPL-3 Archs: x64 MD5sum: 3aa1affd80498c25281bc60321ecad08 NeedsCompilation: no Title: Generation of null ranges via bootstrapping or covariate matching Description: Modular package for generation of sets of ranges representing the null hypothesis. These can take the form of bootstrap samples of ranges (using the block bootstrap framework of Bickel et al 2010), or sets of control ranges that are matched across one or more covariates. nullranges is designed to be inter-operable with other packages for analysis of genomic overlap enrichment, including the plyranges Bioconductor package. biocViews: Visualization, GeneSetEnrichment, FunctionalGenomics, Epigenetics, GeneRegulation, GeneTarget, GenomeAnnotation, Annotation, GenomeWideAssociation, HistoneModification, ChIPSeq, ATACSeq, DNaseSeq, RNASeq, HiddenMarkovModel Author: Michael Love [aut, cre] (), Wancen Mu [aut] (), Eric Davis [aut] (), Douglas Phanstiel [aut] (), Stuart Lee [aut] (), Mikhail Dozmorov [ctb], Tim Triche [ctb], CZI [fnd] Maintainer: Michael Love URL: https://nullranges.github.io/nullranges, https://github.com/nullranges/nullranges VignetteBuilder: knitr BugReports: https://support.bioconductor.org/tag/nullranges/ git_url: https://git.bioconductor.org/packages/nullranges git_branch: RELEASE_3_16 git_last_commit: 4c18c56 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/nullranges_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/nullranges_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/nullranges_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/nullranges_1.4.0.tgz vignettes: vignettes/nullranges/inst/doc/matching_ginteractions.html, vignettes/nullranges/inst/doc/matching_granges.html, vignettes/nullranges/inst/doc/matching_ranges.html, vignettes/nullranges/inst/doc/nullranges.html, vignettes/nullranges/inst/doc/segmented_boot_ranges.html, vignettes/nullranges/inst/doc/unseg_boot_ranges.html vignetteTitles: 3. Case study II: CTCF orientation, 2. Case study I: CTCF occupancy, 1. Overview of matchRanges, 0. Introduction to nullranges, 4. Segmented block bootstrap, 5. Unsegmented block bootstrap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nullranges/inst/doc/matching_ginteractions.R, vignettes/nullranges/inst/doc/matching_granges.R, vignettes/nullranges/inst/doc/matching_ranges.R, vignettes/nullranges/inst/doc/nullranges.R, vignettes/nullranges/inst/doc/segmented_boot_ranges.R, vignettes/nullranges/inst/doc/unseg_boot_ranges.R dependencyCount: 96 Package: NuPoP Version: 2.6.3 Depends: R (>= 4.0) Imports: graphics, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 2680307841955b92ccdca768ef16f715 NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at https://github.com/jipingw. The Fortran codes have complete functonality as the R package. Note: NuPoP has two separate functions for prediction of nucleosome positioning, one for MNase-map trained models and the other for chemical map-trained models. The latter was implemented for four species including yeast, S.pombe, mouse and human, trained based on our recent publications. We noticed there is another package nuCpos by another group for prediction of nucleosome positioning trained with chemicals. A report to compare recent versions of NuPoP with nuCpos can be found at https://github.com/jiping/NuPoP_doc. Some more information can be found and will be posted at https://github.com/jipingw/NuPoP. biocViews: Genetics,Visualization,Classification,NucleosomePositioning, HiddenMarkovModel Author: Ji-Ping Wang ; Liqun Xi ; Oscar Zarate Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NuPoP git_branch: RELEASE_3_16 git_last_commit: 475b15f git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/NuPoP_2.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/NuPoP_2.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/NuPoP_2.6.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NuPoP_2.6.3.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP.html vignetteTitles: NuPoP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP.R suggestsMe: nuCpos dependencyCount: 2 Package: NxtIRFcore Version: 1.4.0 Depends: R (>= 3.5.0), NxtIRFdata Imports: methods, stats, utils, tools, parallel, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, IRanges, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors LinkingTo: Rcpp, zlibbioc, RcppProgress Suggests: knitr, rmarkdown, pheatmap, shiny, openssl, crayon, egg, DESeq2, limma, DoubleExpSeq, Rsubread, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: afc918ca864930fac309ccfb0a2a12e1 NeedsCompilation: yes Title: Core Engine for NxtIRF: a User-Friendly Intron Retention and Alternative Splicing Analysis using the IRFinder Engine Description: Interactively analyses Intron Retention and Alternative Splicing Events (ASE) in RNA-seq data. NxtIRF quantifies ASE events in BAM files aligned to the genome using a splice-aware aligner such as STAR. The core quantitation algorithm relies on the IRFinder/C++ engine ported via Rcpp for multi-platform compatibility. In addition, NxtIRF provides convenient pipelines for downstream analysis and publication-ready visualisation tools. Note that NxtIRFcore is now replaced by SpliceWiz in Bioconductor 3.16 onwards. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing Author: Alex Chit Hei Wong [aut, cre, cph], William Ritchie [cph], Ulf Schmitz [ctb] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/NxtIRFcore SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/NxtIRFcore git_branch: RELEASE_3_16 git_last_commit: c38d908 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/NxtIRFcore_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/NxtIRFcore_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/NxtIRFcore_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/NxtIRFcore_1.4.0.tgz vignettes: vignettes/NxtIRFcore/inst/doc/NxtIRF.html vignetteTitles: NxtIRFcore: Differential Alternative Splicing and Intron Retention analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NxtIRFcore/inst/doc/NxtIRF.R dependencyCount: 154 Package: occugene Version: 1.58.0 Depends: R (>= 2.0.0) License: GPL (>= 2) Archs: x64 MD5sum: be20bbf3dfe9ec4c2a1e1d292351a600 NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: RELEASE_3_16 git_last_commit: 4eb81e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/occugene_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/occugene_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/occugene_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/occugene_1.58.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.72.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, interp License: LGPL MD5sum: 7ba11213670ceeb53f1cc9d15a217d30 NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: RELEASE_3_16 git_last_commit: 5a4e900 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OCplus_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OCplus_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OCplus_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OCplus_1.72.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 19 Package: octad Version: 1.0.0 Depends: R (>= 4.2.0), magrittr, dplyr, ggplot2, edgeR, RUVSeq, DESeq2, limma, rhdf5, foreach, Rfast, octad.db, stats, httr, ExperimentHub, AnnotationHub, Biobase, S4Vectors Imports: EDASeq, GSVA, data.table, htmlwidgets, plotly, reshape2, grDevices, utils Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: b975f2e8d6df3f13b3e34dc2204052c3 NeedsCompilation: no Title: Open Cancer TherApeutic Discovery (OCTAD) Description: OCTAD provides a platform for virtually screening compounds targeting precise cancer patient groups. The essential idea is to identify drugs that reverse the gene expression signature of disease by tamping down over-expressed genes and stimulating weakly expressed ones. The package offers deep-learning based reference tissue selection, disease gene expression signature creation, pathway enrichment analysis, drug reversal potency scoring, cancer cell line selection, drug enrichment analysis and in silico hit validation. It currently covers ~20,000 patient tissue samples covering 50 cancer types, and expression profiles for ~12,000 distinct compounds. biocViews: Classification, GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, GeneSetEnrichment Author: E. Chekalin [aut, cre], S. Paithankar [aut], B. Zeng [aut], B. Glicksberg [ctb], P. Newbury [ctb], J. Xing [ctb], K. Liu [ctb], A. Wen [ctb], D. Joseph [ctb], B. Chen [aut] Maintainer: E. Chekalin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/octad git_branch: RELEASE_3_16 git_last_commit: 9ea3c9a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/octad_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/octad_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/octad_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/octad_1.0.0.tgz vignettes: vignettes/octad/inst/doc/octad.html vignetteTitles: octad hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/octad/inst/doc/octad.R dependencyCount: 186 Package: ODER Version: 1.4.0 Depends: R (>= 4.1) Imports: BiocGenerics, BiocFileCache, dasper, derfinder, dplyr, IRanges, GenomeInfoDb, GenomicRanges, ggplot2, ggpubr, ggrepel, magrittr, rtracklayer, S4Vectors, stringr, data.table, megadepth, methods, plyr, purrr, tibble, utils Suggests: BiocStyle, covr, knitr, recount, RefManageR, rmarkdown, sessioninfo, SummarizedExperiment, testthat (>= 3.0.0), GenomicFeatures, xfun License: Artistic-2.0 MD5sum: 849901628a61a7ea083ab06a26745dd4 NeedsCompilation: no Title: Optimising the Definition of Expressed Regions Description: The aim of ODER is to identify previously unannotated expressed regions (ERs) using RNA-sequencing data. For this purpose, ODER defines and optimises the definition of ERs, then connected these ERs to genes using junction data. In this way, ODER improves gene annotation. Gene annotation is a staple input of many bioinformatic pipelines and a more complete gene annotation can enable more accurate interpretation of disease associated variants. biocViews: Software, GenomeAnnotation, Transcriptomics, RNASeq, GeneExpression, Sequencing, DataImport Author: Emmanuel Olagbaju [aut], David Zhang [aut, cre] (), Sebastian Guelfi [ctb], Siddharth Sethi [ctb] Maintainer: David Zhang URL: https://github.com/eolagbaju/ODER VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/ODER git_url: https://git.bioconductor.org/packages/ODER git_branch: RELEASE_3_16 git_last_commit: 7a08fc0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ODER_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ODER_1.4.0.zip vignettes: vignettes/ODER/inst/doc/ODER_overview.html vignetteTitles: Introduction to ODER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ODER/inst/doc/ODER_overview.R dependencyCount: 215 Package: odseq Version: 1.26.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE MD5sum: 30cd1073e7924832ba39bdcf591fe6b3 NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: RELEASE_3_16 git_last_commit: b979fad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/odseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/odseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/odseq_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/odseq_1.26.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 32 Package: OGRE Version: 1.2.0 Depends: R (>= 4.1.0), S4Vectors Imports: GenomicRanges, methods, data.table, assertthat, ggplot2, Gviz, IRanges, AnnotationHub, grDevices, stats, GenomeInfoDb, shiny, shinyFiles, DT, rtracklayer, shinydashboard, shinyBS,tidyr Suggests: testthat (>= 3.0.0), knitr (>= 1.36), rmarkdown (>= 2.11) License: Artistic-2.0 MD5sum: 529ffcd31a1ed736f1144d6fce347e9b NeedsCompilation: no Title: Calculate, visualize and analyse overlap between genomic regions Description: OGRE calculates overlap between user defined genomic region datasets. Any regions can be supplied i.e. genes, SNPs, or reads from sequencing experiments. Key numbers help analyse the extend of overlaps which can also be visualized at a genomic level. biocViews: Software, WorkflowStep, BiologicalQuestion, Annotation, Metagenomics, Visualization, Sequencing Author: Sven Berres [aut, cre], Jörg Gromoll [ctb], Marius Wöste [ctb], Sarah Sandmann [ctb], Sandra Laurentino [ctb] Maintainer: Sven Berres URL: https://github.com/svenbioinf/OGRE/ VignetteBuilder: knitr BugReports: https://github.com/svenbioinf/OGRE/issues git_url: https://git.bioconductor.org/packages/OGRE git_branch: RELEASE_3_16 git_last_commit: dcfd724 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OGRE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OGRE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OGRE_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OGRE_1.2.0.tgz vignettes: vignettes/OGRE/inst/doc/OGRE.html vignetteTitles: OGRE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGRE/inst/doc/OGRE.R dependencyCount: 170 Package: oligo Version: 1.62.2 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, ACME, RCurl Enhances: doMC, doMPI License: LGPL (>= 2) MD5sum: 6b06135dd0df5f1d4116fa6fcf09ae59 NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: RELEASE_3_16 git_last_commit: 05a7e3c git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/oligo_1.62.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/oligo_1.62.2.zip mac.binary.ver: bin/macosx/contrib/4.2/oligo_1.62.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oligo_1.62.2.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, pumadata importsMe: ArrayExpress, cn.farms, crossmeta, frma, ITALICS, mimager suggestsMe: fastseg, frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 53 Package: oligoClasses Version: 1.60.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI, ff Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) Archs: x64 MD5sum: 950a81a0f6594b38b7cf294b761786ce NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: RELEASE_3_16 git_last_commit: cf9d76c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oligoClasses_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oligoClasses_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oligoClasses_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oligoClasses_1.60.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, VanillaICE suggestsMe: hapmapsnp6, aroma.affymetrix, scrime dependencyCount: 49 Package: OLIN Version: 1.76.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 36fce7f8e3e622d43d865acd7ffa3bf2 NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: RELEASE_3_16 git_last_commit: f2657ef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OLIN_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OLIN_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OLIN_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OLIN_1.76.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack dependencyCount: 10 Package: OLINgui Version: 1.72.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: ecac686f9559ddce5a53d0310ee20dbc NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: RELEASE_3_16 git_last_commit: 18425f3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OLINgui_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OLINgui_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OLINgui_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OLINgui_1.72.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 16 Package: omada Version: 1.0.0 Depends: pdfCluster (>= 1.0-3), kernlab (>= 0.9-29), R (>= 4.2), fpc (>= 2.2-9), Rcpp (>= 1.0.7), diceR (>= 0.6.0), ggplot2 (>= 3.3.5), reshape (>= 0.8.8), clusterCrit (>= 1.2.8), clValid (>= 0.7), glmnet (>= 4.1.3), dplyr(>= 1.0.7), stats (>= 4.1.2) Suggests: rmarkdown, knitr, testthat License: GPL-3 MD5sum: 3b2d84056e696e87e55dc340b8697f49 NeedsCompilation: no Title: Machine learning tools for automated transcriptome clustering analysis Description: Symptomatic heterogeneity in complex diseases reveals differences in molecular states that need to be investigated. However, selecting the numerous parameters of an exploratory clustering analysis in RNA profiling studies requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent and further gene association analyses need to be performed independently. We have developed a suite of tools to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with four datasets characterised by different expression signal strengths. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Even in datasets with less clear biological distinctions, stable subgroups with different expression profiles and clinical associations were found. biocViews: Software, Clustering, RNASeq, GeneExpression Author: Sokratis Kariotis [aut, cre] () Maintainer: Sokratis Kariotis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omada git_branch: RELEASE_3_16 git_last_commit: 754da01 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/omada_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omada_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omada_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/omada_1.0.0.tgz vignettes: vignettes/omada/inst/doc/omada-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omada/inst/doc/omada-vignette.R dependencyCount: 153 Package: OmaDB Version: 2.14.0 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: f857c3ef9976e930aae8ddca60207255 NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: RELEASE_3_16 git_last_commit: 50f3339 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OmaDB_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmaDB_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmaDB_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OmaDB_2.14.0.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R importsMe: PhyloProfile suggestsMe: orthogene dependencyCount: 60 Package: omicade4 Version: 1.38.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: c9dc47612e55ed92116cad4a755ed029 NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: This package performes multiple co-inertia analysis of omics datasets. biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: RELEASE_3_16 git_last_commit: 8eec0e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/omicade4_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicade4_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicade4_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/omicade4_1.38.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, phenomis, ropls dependencyCount: 38 Package: OmicCircos Version: 1.36.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 581d681adc8453d5a5d5179d76087b9a NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: RELEASE_3_16 git_last_commit: fbd58df git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OmicCircos_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmicCircos_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmicCircos_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OmicCircos_1.36.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 16 Package: omicplotR Version: 1.18.0 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: efffb7c37c2fcf5a62b56daf54d63a12 NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: RELEASE_3_16 git_last_commit: 5180b82 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/omicplotR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicplotR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicplotR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/omicplotR_1.18.0.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 102 Package: omicRexposome Version: 1.20.0 Depends: R (>= 3.5.0), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: 9a65650e556e8dbb9819982270d9e986 NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: RELEASE_3_16 git_last_commit: 351df06 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/omicRexposome_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicRexposome_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicRexposome_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/omicRexposome_1.20.0.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R dependencyCount: 216 Package: OmicsLonDA Version: 1.14.0 Depends: R(>= 3.6) Imports: SummarizedExperiment, gss, plyr, zoo, pracma, ggplot2, BiocParallel, parallel, grDevices, graphics, stats, utils, methods, BiocGenerics Suggests: knitr, rmarkdown, testthat, devtools, BiocManager License: MIT + file LICENSE MD5sum: 6498b69f43563fe73ea5c4fbde7f1872 NeedsCompilation: no Title: Omics Longitudinal Differential Analysis Description: Statistical method that provides robust identification of time intervals where omics features (such as proteomics, lipidomics, metabolomics, transcriptomics, microbiome, as well as physiological parameters captured by wearable sensors such as heart rhythm, body temperature, and activity level) are significantly different between groups. biocViews: TimeCourse, Survival, Microbiome, Metabolomics, Proteomics, Lipidomics, Transcriptomics, Regression Author: Ahmed A. Metwally, Tom Zhang, Michael Snyder Maintainer: Ahmed A. Metwally URL: https://github.com/aametwally/OmicsLonDA VignetteBuilder: knitr BugReports: https://github.com/aametwally/OmicsLonDA/issues git_url: https://git.bioconductor.org/packages/OmicsLonDA git_branch: RELEASE_3_16 git_last_commit: 0499e86 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OmicsLonDA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmicsLonDA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmicsLonDA_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OmicsLonDA_1.14.0.tgz vignettes: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.html vignetteTitles: OmicsLonDA Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.R dependencyCount: 67 Package: OMICsPCA Version: 1.16.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 2a9dbe435f1bb09185da740972988f06 NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: RELEASE_3_16 git_last_commit: 547017e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OMICsPCA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OMICsPCA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OMICsPCA_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OMICsPCA_1.16.0.tgz vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 220 Package: omicsPrint Version: 1.18.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: 3dd8e7e97050a0593e821e1a9693066d NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: RELEASE_3_16 git_last_commit: f5386bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/omicsPrint_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicsPrint_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicsPrint_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/omicsPrint_1.18.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 50 Package: omicsViewer Version: 1.2.0 Depends: R (>= 4.2) Imports: survminer, survival, fastmatch, reshape2, stringr, beeswarm, grDevices, DT, shiny, shinythemes, shinyWidgets, plotly, networkD3, httr, matrixStats, RColorBrewer, Biobase, fgsea, openxlsx, psych, shinybusy, ggseqlogo, htmlwidgets, graphics, grid, stats, utils, methods, shinyjs, curl, flatxml, ggplot2, S4Vectors, SummarizedExperiment, RSQLite, Matrix, shinycssloaders Suggests: BiocStyle, knitr, rmarkdown, unittest License: GPL-2 MD5sum: b6d8a0ba853c8953e37181519b9dd6b5 NeedsCompilation: no Title: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer Description: omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript. biocViews: Software, Visualization, GeneSetEnrichment, DifferentialExpression, MotifDiscovery, Network, NetworkEnrichment Author: Chen Meng [aut, cre] Maintainer: Chen Meng URL: https://github.com/mengchen18/omicsViewer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=0nirB-exquY&list=PLo2m88lJf-RRoLKMY8UEGqCpraKYrX5lk BugReports: https://github.com/mengchen18/omicsViewer git_url: https://git.bioconductor.org/packages/omicsViewer git_branch: RELEASE_3_16 git_last_commit: 6ba4f96 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/omicsViewer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/omicsViewer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/omicsViewer_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/omicsViewer_1.2.0.tgz vignettes: vignettes/omicsViewer/inst/doc/quickStart.html vignetteTitles: quickStart.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsViewer/inst/doc/quickStart.R dependencyCount: 188 Package: Omixer Version: 1.8.0 Depends: R (>= 4.0.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE MD5sum: 3e969b34b779b0f9ebc26f031e4d5163 NeedsCompilation: no Title: Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies Description: Omixer - an Bioconductor package for multivariate and reproducible sample randomization, which ensures optimal sample distribution across batches with well-documented methods. It outputs lab-friendly sample layouts, reducing the risk of sample mixups when manually pipetting randomized samples. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: https://github.com/molepi/Omixer/issues git_url: https://git.bioconductor.org/packages/Omixer git_branch: RELEASE_3_16 git_last_commit: 406c607 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Omixer_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Omixer_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Omixer_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Omixer_1.8.0.tgz vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R dependencyCount: 54 Package: OmnipathR Version: 3.5.25 Depends: R(>= 4.0) Imports: checkmate, crayon, curl, digest, dplyr, httr, igraph, jsonlite, later, logger, magrittr, progress, purrr, rappdirs, readr(>= 2.0.0), readxl, rlang, rmarkdown, rvest, stats, stringr, tibble, tidyr, tidyselect, tools, utils, withr, xml2, yaml Suggests: BiocStyle, biomaRt, bookdown, dnet, ggplot2, ggraph, gprofiler2, knitr, mlrMBO, parallelMap, ParamHelpers, Rgraphviz, smoof, supraHex, testthat License: MIT + file LICENSE MD5sum: 97f1a0eba3aaf914892cd974c6175f0c NeedsCompilation: no Title: OmniPath web service client and more Description: A client for the OmniPath web service (https://www.omnipathdb.org) and many other resources. It also includes functions to transform and pretty print some of the downloaded data, functions to access a number of other resources such as BioPlex, ConsensusPathDB, EVEX, Gene Ontology, Guide to Pharmacology (IUPHAR/BPS), Harmonizome, HTRIdb, Human Phenotype Ontology, InWeb InBioMap, KEGG Pathway, Pathway Commons, Ramilowski et al. 2015, RegNetwork, ReMap, TF census, TRRUST and Vinayagam et al. 2011. Furthermore, OmnipathR features a close integration with the NicheNet method for ligand activity prediction from transcriptomics data, and its R implementation `nichenetr` (available only on github). biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology, Transcriptomics, SingleCell, Annotation, KEGG Author: Alberto Valdeolivas [aut] (), Denes Turei [cre, aut] (), Attila Gabor [aut] () Maintainer: Denes Turei URL: https://saezlab.github.io/OmnipathR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: master git_last_commit: 2a734a1 git_last_commit_date: 2022-10-22 Date/Publication: 2022-10-23 source.ver: src/contrib/OmnipathR_3.5.25.tar.gz win.binary.ver: bin/windows/contrib/4.2/OmnipathR_3.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OmnipathR_3.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OmnipathR_3.6.6.tgz vignettes: vignettes/OmnipathR/inst/doc/bioc_workshop.html, vignettes/OmnipathR/inst/doc/db_manager.html, vignettes/OmnipathR/inst/doc/drug_targets.html, vignettes/OmnipathR/inst/doc/extra_attrs.html, vignettes/OmnipathR/inst/doc/nichenet.html, vignettes/OmnipathR/inst/doc/omnipath_intro.html, vignettes/OmnipathR/inst/doc/paths.html vignetteTitles: OmniPath Bioconductor workshop, Database manager, Building networks around drug-targets using OmnipathR, Extra attributes, Using NicheNet with OmnipathR, OmnipathR: an R client for the OmniPath web service, Pathway construction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/bioc_workshop.R, vignettes/OmnipathR/inst/doc/db_manager.R, vignettes/OmnipathR/inst/doc/drug_targets.R, vignettes/OmnipathR/inst/doc/extra_attrs.R, vignettes/OmnipathR/inst/doc/nichenet.R, vignettes/OmnipathR/inst/doc/omnipath_intro.R, vignettes/OmnipathR/inst/doc/paths.R importsMe: wppi suggestsMe: decoupleR dependencyCount: 79 Package: ompBAM Version: 1.2.0 Imports: utils, Rcpp, zlibbioc Suggests: RcppProgress, knitr, rmarkdown, roxygen2, devtools, usethis, desc, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 885c1ca56ac6107961f421b118f7ff73 NeedsCompilation: no Title: C++ Library for OpenMP-based multi-threaded sequential profiling of Binary Alignment Map (BAM) files Description: This packages provides C++ header files for developers wishing to create R packages that processes BAM files. ompBAM automates file access, memory management, and handling of multiple threads 'behind the scenes', so developers can focus on creating domain-specific functionality. The included vignette contains detailed documentation of this API, including quick-start instructions to create a new ompBAM-based package, and step-by-step explanation of the functionality behind the example packaged included within ompBAM. biocViews: Alignment, DataImport, RNASeq, Software, Sequencing, Transcriptomics, SingleCell Author: Alex Chit Hei Wong [aut, cre, cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/ompBAM VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/ompBAM git_branch: RELEASE_3_16 git_last_commit: 221f468 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ompBAM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ompBAM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ompBAM_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ompBAM_1.2.0.tgz vignettes: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.html vignetteTitles: ompBAM API Documentation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ompBAM/inst/doc/ompBAM-API-Docs.R importsMe: SpliceWiz linksToMe: SpliceWiz dependencyCount: 4 Package: oncomix Version: 1.20.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 2dab4409041a3d92c2ee2aaf18b42096 NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: RELEASE_3_16 git_last_commit: bfe3c9a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oncomix_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oncomix_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oncomix_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oncomix_1.20.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 55 Package: oncoscanR Version: 1.0.0 Depends: R (>= 4.2), IRanges (>= 2.30.0), GenomicRanges (>= 1.48.0), magrittr Imports: readr, S4Vectors, methods, utils Suggests: testthat (>= 3.1.4), jsonlite, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ac3eafa3f9413fd81ecb2f1f0707b1d1 NeedsCompilation: no Title: Secondary analyses of CNV data (HRD and more) Description: The software uses the copy number segments from a text file and identifies all chromosome arms that are globally altered and computes various genome-wide scores. The following HRD scores (characteristic of BRCA-mutated cancers) are included: LST, HR-LOH, nLST and gLOH. the package is tailored for the ThermoFisher Oncoscan assay analyzed with their Chromosome Alteration Suite (ChAS) but can be adapted to any input. biocViews: CopyNumberVariation, Microarray, Software Author: Yann Christinat [aut, cre], Geneva University Hospitals [aut, cph] Maintainer: Yann Christinat URL: https://github.com/yannchristinat/oncoscanR VignetteBuilder: knitr BugReports: https://github.com/yannchristinat/oncoscanR/issues git_url: https://git.bioconductor.org/packages/oncoscanR git_branch: RELEASE_3_16 git_last_commit: f40f5e3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oncoscanR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oncoscanR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oncoscanR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oncoscanR_1.0.0.tgz vignettes: vignettes/oncoscanR/inst/doc/oncoscanR.html vignetteTitles: oncoscanR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oncoscanR/inst/doc/oncoscanR.R dependencyCount: 42 Package: OncoScore Version: 1.26.0 Depends: R (>= 4.1.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: 617467cd0166870b3acb68a0a9365feb NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [aut] (), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [cre, aut] (), Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: RELEASE_3_16 git_last_commit: fa89848 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OncoScore_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OncoScore_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OncoScore_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OncoScore_1.26.0.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/vignette.R dependencyCount: 70 Package: OncoSimulR Version: 4.0.2 Depends: R (>= 3.5.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel, stringr LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) MD5sum: 0b3c2e89633b0f157f176814aa90df1b NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Fitness (including just birth, just death, or both birth and death) can also be a function of the relative and absolute frequencies of other genotypes (i.e., frequency-dependent fitness). Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulating multi-species scenarios and therapeutic interventions, including adaptive therapy, is also possible. Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Alberto Gonzalez Klein [aut], Javier Mu\~noz Haro [aut], Javier Lopez Cano [aut], Niklas Endres [ctb], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb], Tymoteusz Wolodzko [ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb], Theresa Niederberger [ctb], Christian Bender [ctb], Matthias Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: RELEASE_3_16 git_last_commit: eee6c7e git_last_commit_date: 2023-02-20 Date/Publication: 2023-02-21 source.ver: src/contrib/OncoSimulR_4.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/OncoSimulR_4.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/OncoSimulR_4.0.2.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 90 Package: oneSENSE Version: 1.20.0 Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils, methods, flowCore Suggests: knitr, rmarkdown License: GPL (>=3) Archs: x64 MD5sum: c89540d29c09f4cb0e00a650ca5a7f20 NeedsCompilation: no Title: One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (OneSENSE) Description: A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis, allowing for simultaneous visualization of two catergories across a two-dimensional plot. The cellular occupancy of the resulting plots alllows for direct assessment of the relationships between the categories. biocViews: ImmunoOncology, Software, FlowCytometry, GUI, DimensionReduction Author: Cheng Yang, Evan Newell, Yong Kee Tan Maintainer: Yong Kee Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oneSENSE git_branch: RELEASE_3_16 git_last_commit: 6811b90 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oneSENSE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oneSENSE_1.19.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oneSENSE_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oneSENSE_1.20.0.tgz vignettes: vignettes/oneSENSE/inst/doc/quickstart.html vignetteTitles: Introduction to oneSENSE GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R dependencyCount: 107 Package: onlineFDR Version: 2.6.0 Imports: stats, Rcpp, progress LinkingTo: Rcpp, RcppProgress Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: d692b479ec2674b46a49b0386981ea0a NeedsCompilation: yes Title: Online error rate control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online multiple hypothesis testing, where hypotheses arrive in a stream. In this framework, a null hypothesis is rejected based on the evidence against it and on the previous rejection decisions. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya Ramdas [aut], Adel Javanmard [ctb], Andrea Montanari [ctb], Jinjin Tian [ctb], Tijana Zrnic [ctb], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: RELEASE_3_16 git_last_commit: 8afdd3d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/onlineFDR_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/onlineFDR_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/onlineFDR_2.6.0.tgz vignettes: vignettes/onlineFDR/inst/doc/advanced-usage.html, vignettes/onlineFDR/inst/doc/onlineFDR.html, vignettes/onlineFDR/inst/doc/theory.html vignetteTitles: Advanced usage of onlineFDR, Using the onlineFDR package, The theory behind onlineFDR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/advanced-usage.R, vignettes/onlineFDR/inst/doc/onlineFDR.R, vignettes/onlineFDR/inst/doc/theory.R dependencyCount: 17 Package: ontoProc Version: 1.20.0 Depends: R (>= 4.0), ontologyIndex Imports: Biobase, S4Vectors, methods, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph, AnnotationHub, SummarizedExperiment Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex, rmarkdown, AnnotationDbi License: Artistic-2.0 Archs: x64 MD5sum: 1daa3e60bc2f12b3520325da46d0b57b NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vincent Carey [ctb, cre] (), Sara Stankiewicz [ctb] Maintainer: Vincent Carey URL: https://github.com/vjcitn/ontoProc VignetteBuilder: knitr BugReports: https://github.com/vjcitn/ontoProc/issues git_url: https://git.bioconductor.org/packages/ontoProc git_branch: RELEASE_3_16 git_last_commit: c0d8166 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ontoProc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ontoProc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ontoProc_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ontoProc_1.20.0.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R dependsOnMe: SingleRBook importsMe: pogos, tenXplore suggestsMe: BiocOncoTK, scDiffCom dependencyCount: 109 Package: openCyto Version: 2.10.1 Depends: R (>= 3.5.0) Imports: methods,Biobase,BiocGenerics,flowCore(>= 1.99.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>= 3.99.1),flowClust(>= 3.11.4),RBGL,graph,data.table,RColorBrewer LinkingTo: cpp11, BH Suggests: flowWorkspaceData, knitr, rmarkdown, markdown, testthat, utils, tools, parallel, ggcyto, CytoML, flowStats(>= 4.5.2), MASS License: AGPL-3.0-only MD5sum: be2c71e5a91bed8100704ea41696494a NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_16 git_last_commit: ad6b183 git_last_commit_date: 2022-12-01 Date/Publication: 2022-12-02 source.ver: src/contrib/openCyto_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/openCyto_2.10.1.zip vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowWorkspace, ggcyto dependencyCount: 74 Package: openPrimeR Version: 1.20.0 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), distr (>= 2.6), distrEx (>= 2.6), fitdistrplus (>= 1.0-7), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 Archs: x64 MD5sum: 9ed5c077b4649d787db87f1137d043e2 NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology, Coverage, MultipleComparison Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: RELEASE_3_16 git_last_commit: 08f93e2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/openPrimeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/openPrimeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/openPrimeR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/openPrimeR_1.20.0.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependsOnMe: openPrimeRui dependencyCount: 124 Package: openPrimeRui Version: 1.20.0 Depends: R (>= 4.0.0), openPrimeR (>= 0.99.0) Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>= 0.2), rmarkdown (>= 1.0) Suggests: knitr (>= 1.13) License: GPL-2 MD5sum: dcc8449cf04486baf4aaaa9363b6776a NeedsCompilation: no Title: Shiny Application for Multiplex PCR Primer Design and Analysis Description: A Shiny application providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeRui git_branch: RELEASE_3_16 git_last_commit: 51f5916 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/openPrimeRui_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/openPrimeRui_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/openPrimeRui_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/openPrimeRui_1.20.0.tgz vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html vignetteTitles: openPrimeRui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R dependencyCount: 137 Package: OpenStats Version: 1.10.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: 9ccb5135a70de805eb20c3e4bfddc688 NeedsCompilation: no Title: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput genotype-phenotype association Description: Package contains several methods for statistical analysis of genotype to phenotype association in high-throughput screening pipelines. biocViews: StatisticalMethod, BatchEffect, Bayesian Author: Hamed Haseli Mashhadi Maintainer: Hamed Haseli Mashhadi URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: RELEASE_3_16 git_last_commit: 3571a36 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OpenStats_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OpenStats_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OpenStats_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OpenStats_1.10.0.tgz vignettes: vignettes/OpenStats/inst/doc/OpenStats.html vignetteTitles: OpenStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R dependencyCount: 135 Package: oposSOM Version: 2.16.0 Depends: R (>= 4.0.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp, methods, graph, XML, png, RCurl LinkingTo: RcppParallel, Rcpp License: GPL (>=2) MD5sum: 81f1d624eafb33d39bd74485e5d2a9ea NeedsCompilation: yes Title: Comprehensive analysis of transcriptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: RELEASE_3_16 git_last_commit: 15105f8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oposSOM_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oposSOM_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oposSOM_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oposSOM_2.16.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 85 Package: oppar Version: 1.26.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 Archs: x64 MD5sum: 631743cc1331c4d9f0d2220f106b9cc1 NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: RELEASE_3_16 git_last_commit: 1d125d9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oppar_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oppar_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oppar_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oppar_1.26.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R dependencyCount: 82 Package: oppti Version: 1.12.0 Depends: R (>= 3.5) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools, parallelDist, Suggests: markdown License: MIT Archs: x64 MD5sum: 29a90b1b288a8d0d99e5772a4190bcf3 NeedsCompilation: no Title: Outlier Protein and Phosphosite Target Identifier Description: The aim of oppti is to analyze protein (and phosphosite) expressions to find outlying markers for each sample in the given cohort(s) for the discovery of personalized actionable targets. biocViews: Proteomics, Regression, DifferentialExpression, BiomedicalInformatics, GeneTarget, GeneExpression, Network Author: Abdulkadir Elmas Maintainer: Abdulkadir Elmas URL: https://github.com/Huang-lab/oppti VignetteBuilder: knitr BugReports: https://github.com/Huang-lab/oppti/issues git_url: https://git.bioconductor.org/packages/oppti git_branch: RELEASE_3_16 git_last_commit: 587a4f3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/oppti_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/oppti_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/oppti_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/oppti_1.12.0.tgz vignettes: vignettes/oppti/inst/doc/analysis.html vignetteTitles: Outlier Protein and Phosphosite Target Identifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppti/inst/doc/analysis.R dependencyCount: 126 Package: optimalFlow Version: 1.10.0 Depends: dplyr, optimalFlowData, rlang (>= 0.4.0) Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest, foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse Suggests: knitr, BiocStyle, rmarkdown, magick License: Artistic-2.0 MD5sum: 8bef2f2bc99a4eb24d914c9baa2f982e NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: RELEASE_3_16 git_last_commit: 8a57fbe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/optimalFlow_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/optimalFlow_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/optimalFlow_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/optimalFlow_1.10.0.tgz vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html vignetteTitles: optimalFlow: optimal-transport approach to Flow Cytometry analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R dependencyCount: 99 Package: OPWeight Version: 1.20.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: ad21b94e92be77f99ff32c3c9eb2e325 NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: RELEASE_3_16 git_last_commit: 417952d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OPWeight_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OPWeight_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OPWeight_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OPWeight_1.20.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 42 Package: OrderedList Version: 1.70.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: c468e5a04913b6e2bdbc069b12d14e81 NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: RELEASE_3_16 git_last_commit: 470b4bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OrderedList_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OrderedList_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OrderedList_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OrderedList_1.70.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 9 Package: ORFhunteR Version: 1.6.0 Depends: Biostrings, rtracklayer, Peptides Imports: Rcpp (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg38, data.table, stringr, randomForest, xfun, stats, utils, parallel, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: c5466308777bb491eed8747f444a523f NeedsCompilation: yes Title: Predict open reading frames in nucleotide sequences Description: The ORFhunteR package is a R and C++ library for an automatic determination and annotation of open reading frames (ORF) in a large set of RNA molecules. It efficiently implements the machine learning model based on vectorization of nucleotide sequences and the random forest classification algorithm. The ORFhunteR package consists of a set of functions written in the R language in conjunction with C++. The efficiency of the package was confirmed by the examples of the analysis of RNA molecules from the NCBI RefSeq and Ensembl databases. The package can be used in basic and applied biomedical research related to the study of the transcriptome of normal as well as altered (for example, cancer) human cells. biocViews: Technology, StatisticalMethod, Sequencing, RNASeq, Classification, FeatureExtraction Author: Vasily V. Grinev [aut, cre] (), Mikalai M. Yatskou [aut], Victor V. Skakun [aut], Maryna Chepeleva [aut], Petr V. Nazarov [aut] () Maintainer: Vasily V. Grinev VignetteBuilder: knitr BugReports: https://github.com/rfctbio-bsu/ORFhunteR/issues git_url: https://git.bioconductor.org/packages/ORFhunteR git_branch: RELEASE_3_16 git_last_commit: 475022f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ORFhunteR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ORFhunteR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ORFhunteR_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ORFhunteR_1.6.0.tgz vignettes: vignettes/ORFhunteR/inst/doc/ORFhunteR.html vignetteTitles: The ORFhunteR package: User’s manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ORFhunteR/inst/doc/ORFhunteR.R dependencyCount: 61 Package: ORFik Version: 1.18.2 Depends: R (>= 3.6.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: AnnotationDbi (>= 1.45.0), Biostrings (>= 2.51.1), biomaRt, biomartr, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, cowplot (>= 1.0.0), data.table (>= 1.11.8), DESeq2 (>= 1.24.0), fst (>= 0.9.2), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), httr (>= 1.3.0), jsonlite, methods (>= 3.6.0), R.utils, Rcpp (>= 1.0.0), Rsamtools (>= 1.35.0), rtracklayer (>= 1.43.0), stats, SummarizedExperiment (>= 1.14.0), S4Vectors (>= 0.21.3), tools, utils, xml2 (>= 1.2.0) LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE Archs: x64 MD5sum: e827871a085a52b6921f0fbff2417045 NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph], Michal Swirski [ctb], Katarzyna Chyzynska [ctb, dtc], Yamila Torres Cleuren [ctb, ths], Evind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues git_url: https://git.bioconductor.org/packages/ORFik git_branch: RELEASE_3_16 git_last_commit: 4ab60c3 git_last_commit_date: 2023-01-13 Date/Publication: 2023-01-15 source.ver: src/contrib/ORFik_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/ORFik_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/ORFik_1.18.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ORFik_1.18.2.tgz vignettes: vignettes/ORFik/inst/doc/Annotation_Alignment.html, vignettes/ORFik/inst/doc/ORFikExperiment.html, vignettes/ORFik/inst/doc/ORFikOverview.html, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.html vignetteTitles: ORFik_Annotation_Alignment, Data management, ORFik Overview, Ribo-seq pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/Annotation_Alignment.R, vignettes/ORFik/inst/doc/ORFikExperiment.R, vignettes/ORFik/inst/doc/ORFikOverview.R, vignettes/ORFik/inst/doc/Ribo-seq_pipeline.R dependsOnMe: RiboCrypt importsMe: TFHAZ dependencyCount: 136 Package: Organism.dplyr Version: 1.26.0 Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges, GenomicFeatures, AnnotationDbi, rlang, methods, tools, utils, BiocFileCache, DBI, dbplyr, tibble Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, BiocStyle, ggplot2 License: Artistic-2.0 Archs: x64 MD5sum: e73900859791a888fc08ca8dd5b80c9e NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng [aut] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: RELEASE_3_16 git_last_commit: 9ed7b45 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Organism.dplyr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Organism.dplyr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Organism.dplyr_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Organism.dplyr_1.26.0.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R dependsOnMe: annotation importsMe: Ularcirc dependencyCount: 98 Package: OrganismDbi Version: 1.40.0 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.39.4) Imports: Biobase, BiocManager, GenomicRanges (>= 1.31.13), graph, IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer, biomaRt, RUnit, RMariaDB License: Artistic-2.0 MD5sum: 8f9943ec3d92a011fc16d7595fad7391 NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès, Martin Morgan, Valerie Obenchain Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: RELEASE_3_16 git_last_commit: fac971d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OrganismDbi_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OrganismDbi_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OrganismDbi_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OrganismDbi_1.40.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus importsMe: AnnotationHubData, epivizrData, ggbio, uncoverappLib, MOCHA suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 99 Package: orthogene Version: 1.4.2 Depends: R (>= 4.1) Imports: dplyr, methods, stats, utils, Matrix, jsonlite, homologene, gprofiler2, babelgene, data.table, parallel, ggplot2, ggpubr, patchwork, DelayedArray, grr, repmis, ggtree, tools Suggests: rworkflows, remotes, knitr, BiocStyle, markdown, rmarkdown, testthat (>= 3.0.0), piggyback, magick, GenomeInfoDbData, ape, phytools, rphylopic (>= 1.0.0), TreeTools, ggimage, OmaDB License: GPL-3 MD5sum: 9e10c4beaa2e00e6d3cfe497f31736a3 NeedsCompilation: no Title: Interspecies gene mapping Description: `orthogene` is an R package for easy mapping of orthologous genes across hundreds of species. It pulls up-to-date gene ortholog mappings across **700+ organisms**. It also provides various utility functions to aggregate/expand common objects (e.g. data.frames, gene expression matrices, lists) using **1:1**, **many:1**, **1:many** or **many:many** gene mappings, both within- and between-species. biocViews: Genetics, ComparativeGenomics, Preprocessing, Phylogenetics, Transcriptomics, GeneExpression Author: Brian Schilder [cre] () Maintainer: Brian Schilder URL: https://github.com/neurogenomics/orthogene VignetteBuilder: knitr BugReports: https://github.com/neurogenomics/orthogene/issues git_url: https://git.bioconductor.org/packages/orthogene git_branch: RELEASE_3_16 git_last_commit: 767afba git_last_commit_date: 2023-04-03 Date/Publication: 2023-04-05 source.ver: src/contrib/orthogene_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/orthogene_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/orthogene_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/orthogene_1.4.2.tgz vignettes: vignettes/orthogene/inst/doc/docker.html, vignettes/orthogene/inst/doc/infer_species.html, vignettes/orthogene/inst/doc/orthogene.html vignetteTitles: docker, Infer species, orthogene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/orthogene/inst/doc/docker.R, vignettes/orthogene/inst/doc/infer_species.R, vignettes/orthogene/inst/doc/orthogene.R importsMe: EWCE suggestsMe: MACP dependencyCount: 157 Package: OSAT Version: 1.46.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 8cfa6c8bf4efb64ca8801d9afdda2acd NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: RELEASE_3_16 git_last_commit: 5c92b0c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OSAT_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OSAT_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OSAT_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OSAT_1.46.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R dependencyCount: 2 Package: Oscope Version: 1.28.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: b029ef5797f581df21a82d730467099e NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/Oscope git_branch: RELEASE_3_16 git_last_commit: e05772b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Oscope_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Oscope_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Oscope_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Oscope_1.28.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R importsMe: scDDboost dependencyCount: 58 Package: OTUbase Version: 1.48.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: a2a6420ab5fef742a272ba845c2404cd NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: RELEASE_3_16 git_last_commit: 4b45815 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OTUbase_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OTUbase_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OTUbase_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OTUbase_1.48.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependencyCount: 57 Package: OUTRIDER Version: 1.16.3 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, data.table, methods Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, grDevices, heatmaply, pheatmap, graphics, IRanges, matrixStats, plotly, plyr, pcaMethods, PRROC, RColorBrewer, reshape2, S4Vectors, scales, splines, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr License: MIT + file LICENSE MD5sum: c1022a732b92312ef6d118e9a26939b4 NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut], Christian Mertes [aut, cre] (), Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez [aut] (), Julien Gagneur [aut] () Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: RELEASE_3_16 git_last_commit: c5f288d git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/OUTRIDER_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/OUTRIDER_1.16.3.zip mac.binary.ver: bin/macosx/contrib/4.2/OUTRIDER_1.16.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OUTRIDER_1.16.3.tgz vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R importsMe: FRASER dependencyCount: 166 Package: OVESEG Version: 1.14.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 Archs: x64 MD5sum: cec3b974f9dea1da0815dcb4638741f4 NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: RELEASE_3_16 git_last_commit: 3ea0dff git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/OVESEG_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/OVESEG_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/OVESEG_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/OVESEG_1.14.0.tgz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 38 Package: PAA Version: 1.32.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: d19918868939513b5e7fef5d822b6fac NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: RELEASE_3_16 git_last_commit: a51339b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PAA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PAA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PAA_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PAA_1.32.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 85 Package: packFinder Version: 1.10.0 Depends: R (>= 4.1.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 Archs: x64 MD5sum: b55b4991cf92e3ea22e8fd04dfb1a9ab NeedsCompilation: no Title: de novo Annotation of Pack-TYPE Transposable Elements Description: Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)). biocViews: Genetics, SequenceMatching, Annotation Author: Jack Gisby [aut, cre] (), Marco Catoni [aut] () Maintainer: Jack Gisby URL: https://github.com/jackgisby/packFinder VignetteBuilder: knitr BugReports: https://github.com/jackgisby/packFinder/issues git_url: https://git.bioconductor.org/packages/packFinder git_branch: RELEASE_3_16 git_last_commit: 69650ed git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/packFinder_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/packFinder_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/packFinder_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/packFinder_1.10.0.tgz vignettes: vignettes/packFinder/inst/doc/packFinder.html vignetteTitles: packFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/packFinder/inst/doc/packFinder.R dependencyCount: 31 Package: padma Version: 1.8.0 Depends: R (>= 4.1.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot License: GPL (>=3) MD5sum: 26e4209effc63d9a2a5ae6a59a17acb4 NeedsCompilation: no Title: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis Description: Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. biocViews: Software, StatisticalMethod, PrincipalComponent, GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq Author: Andrea Rau [cre, aut] (), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: RELEASE_3_16 git_last_commit: 38711cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/padma_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/padma_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/padma_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/padma_1.8.0.tgz vignettes: vignettes/padma/inst/doc/padma.html vignetteTitles: padma package:Quick-start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/padma/inst/doc/padma.R dependencyCount: 133 Package: PADOG Version: 1.40.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGGREST, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: 6e585902b1277b95d355f4c274afd9c6 NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi L. Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: RELEASE_3_16 git_last_commit: 3351ce4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PADOG_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PADOG_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PADOG_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PADOG_1.40.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA dependencyCount: 62 Package: pageRank Version: 1.8.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 MD5sum: 4a9dc633ec19c8f896b45e589bbbca70 NeedsCompilation: no Title: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis Description: Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. biocViews: StatisticalMethod, GeneTarget, Network Author: Hongxu Ding [aut, cre, ctb, cph] Maintainer: Hongxu Ding URL: https://github.com/hd2326/pageRank BugReports: https://github.com/hd2326/pageRank/issues git_url: https://git.bioconductor.org/packages/pageRank git_branch: RELEASE_3_16 git_last_commit: b9d6c66 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pageRank_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pageRank_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pageRank_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pageRank_1.8.0.tgz vignettes: vignettes/pageRank/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pageRank/inst/doc/introduction.R dependencyCount: 124 Package: PAIRADISE Version: 1.14.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 1ee7761d6c5486a5f99ddf26c85f7e17 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: RELEASE_3_16 git_last_commit: 9743a83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PAIRADISE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PAIRADISE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PAIRADISE_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PAIRADISE_1.14.0.tgz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 68 Package: paircompviz Version: 1.36.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 4e547653ab22b2bd5fb0e30056d5283c NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: RELEASE_3_16 git_last_commit: f09e31b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/paircompviz_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/paircompviz_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/paircompviz_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/paircompviz_1.36.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 10 Package: pairkat Version: 1.4.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, KEGGREST, igraph, data.table, methods, stats, magrittr, CompQuadForm, tibble Suggests: rmarkdown, knitr, BiocStyle, dplyr License: GPL-3 MD5sum: 784ddd758db00fd278bf53f4c173ce9d NeedsCompilation: no Title: PaIRKAT Description: PaIRKAT is model framework for assessing statistical relationships between networks of metabolites (pathways) and an outcome of interest (phenotype). PaIRKAT queries the KEGG database to determine interactions between metabolites from which network connectivity is constructed. This model framework improves testing power on high dimensional data by including graph topography in the kernel machine regression setting. Studies on high dimensional data can struggle to include the complex relationships between variables. The semi-parametric kernel machine regression model is a powerful tool for capturing these types of relationships. They provide a framework for testing for relationships between outcomes of interest and high dimensional data such as metabolomic, genomic, or proteomic pathways. PaIRKAT uses known biological connections between high dimensional variables by representing them as edges of ‘graphs’ or ‘networks.’ It is common for nodes (e.g. metabolites) to be disconnected from all others within the graph, which leads to meaningful decreases in testing power whether or not the graph information is included. We include a graph regularization or ‘smoothing’ approach for managing this issue. biocViews: Software, Metabolomics, KEGG, Pathways, Network, GraphAndNetwork, Regression Author: Charlie Carpenter [aut], Cameron Severn [aut], Max McGrath [cre, aut] Maintainer: Max McGrath VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/pairkat/issues git_url: https://git.bioconductor.org/packages/pairkat git_branch: RELEASE_3_16 git_last_commit: af39cf9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pairkat_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pairkat_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pairkat_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pairkat_1.4.0.tgz vignettes: vignettes/pairkat/inst/doc/using-pairkat.html vignetteTitles: using-pairkat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pairkat/inst/doc/using-pairkat.R dependencyCount: 52 Package: pandaR Version: 1.30.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr, rmarkdown License: GPL-2 MD5sum: df61a51a0c6376703322d922fc63538e NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: RELEASE_3_16 git_last_commit: ed4d857 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pandaR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pandaR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pandaR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pandaR_1.30.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependsOnMe: netZooR dependencyCount: 45 Package: panelcn.mops Version: 1.20.0 Depends: R (>= 3.5.0), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: 795516c0f78034bea1c1b7780185f7d3 NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid [aut], Gundula Povysil [aut, cre] Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: RELEASE_3_16 git_last_commit: 498d88f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/panelcn.mops_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/panelcn.mops_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/panelcn.mops_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/panelcn.mops_1.20.0.tgz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 34 Package: PanomiR Version: 1.2.0 Depends: R (>= 4.2.0) Imports: clusterProfiler, dplyr, forcats, GSEABase, igraph, limma, metap, org.Hs.eg.db, parallel, preprocessCore, RColorBrewer, rlang, tibble, withr, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 6a42f1f253aa2fd2f89b095f513a97a0 NeedsCompilation: no Title: Detection of miRNAs that regulate interacting groups of pathways Description: PanomiR is a package to detect miRNAs that target groups of pathways from gene expression data. This package provides functionality for generating pathway activity profiles, determining differentially activated pathways between user-specified conditions, determining clusters of pathways via the PCxN package, and generating miRNAs targeting clusters of pathways. These function can be used separately or sequentially to analyze RNA-Seq data. biocViews: GeneExpression, GeneSetEnrichment, GeneTarget, miRNA, Pathways Author: Pourya Naderi [aut, cre], Yue Yang (Alan) Teo [aut], Ilya Sytchev [aut], Winston Hide [aut] Maintainer: Pourya Naderi URL: https://github.com/pouryany/PanomiR VignetteBuilder: knitr BugReports: https://github.com/pouryany/PanomiR/issues git_url: https://git.bioconductor.org/packages/PanomiR git_branch: RELEASE_3_16 git_last_commit: 25280d9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PanomiR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PanomiR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PanomiR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PanomiR_1.2.0.tgz vignettes: vignettes/PanomiR/inst/doc/PanomiR.html vignetteTitles: PanomiR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PanomiR/inst/doc/PanomiR.R dependencyCount: 161 Package: panp Version: 1.68.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 019428c38c81cf30c084bc931667e016 NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: RELEASE_3_16 git_last_commit: 4d6eb04 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/panp_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/panp_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/panp_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/panp_1.68.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 12 Package: PANR Version: 1.44.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: 32dc6542d3abc00c0a41fa46d8ae6052 NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: RELEASE_3_16 git_last_commit: 95b80dd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PANR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PANR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PANR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PANR_1.44.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 16 Package: PanViz Version: 1.0.0 Depends: R (>= 4.2.0) Imports: tidyr, stringr, dplyr, tibble, magrittr, futile.logger, utils, easycsv, rentrez, igraph, RColorBrewer, data.table, colorspace, grDevices, rlang, methods Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, networkD3, License: Artistic-2.0 MD5sum: 6a5c362f0a72a6abf32510aef77b68d8 NeedsCompilation: no Title: Integrating Multi-Omic Network Data With Summay-Level GWAS Data Description: This pacakge integrates data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) with summary-level genome-wide association (GWAS) data, such as that provided by the GWAS Catalog or GWAS Central databases, or a user's own study or dataset, in order to produce biological networks, termed IMONs (Integrated Multi-Omic Networks). IMONs can be used to analyse trait-specific polymorphic data within the context of biochemical and metabolic reaction networks, providing greater biological interpretability for GWAS data. biocViews: GenomeWideAssociation, Reactome, Metabolomics, SNP, GraphAndNetwork, Network, KEGG Author: Luca Anholt [cre, aut] Maintainer: Luca Anholt URL: https://github.com/LucaAnholt/PanViz VignetteBuilder: knitr BugReports: https://github.com/LucaAnholt/PanViz/issues git_url: https://git.bioconductor.org/packages/PanViz git_branch: RELEASE_3_16 git_last_commit: 3b05719 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PanViz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PanViz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PanViz_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PanViz_1.0.0.tgz vignettes: vignettes/PanViz/inst/doc/my-vignette.html vignetteTitles: PanViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanViz/inst/doc/my-vignette.R dependencyCount: 48 Package: pareg Version: 1.2.0 Depends: R (>= 4.2), tensorflow (>= 2.2.0), tfprobability (>= 0.10.0) Imports: stats, tidyr, purrr, furrr, tibble, glue, tidygraph, igraph, proxy, dplyr, magrittr, ggplot2, ggraph, rlang, progress, Matrix, matrixLaplacian, keras, nloptr, shadowtext, methods, DOSE, stringr, reticulate Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle, formatR, devtools, plotROC, PRROC, mgsa, topGO, msigdbr, betareg, fgsea, ComplexHeatmap, GGally, ggsignif, circlize, enrichplot, ggnewscale, tidyverse, cowplot, ggfittext License: GPL-3 MD5sum: ca1fc7821f8bffdcb39136a9b46cbff5 NeedsCompilation: no Title: Pathway enrichment using a regularized regression approach Description: Compute pathway enrichment scores while accounting for term-term relations. This package uses a regularized multiple linear regression to regress differential expression p-values obtained from multi-condition experiments on a pathway membership matrix. By doing so, it is able to incorporate additional biological knowledge into the enrichment analysis and to estimate pathway enrichment scores more robustly. biocViews: Software, StatisticalMethod, GraphAndNetwork, Regression, GeneExpression, DifferentialExpression, NetworkEnrichment, Network Author: Kim Philipp Jablonski [aut, cre] () Maintainer: Kim Philipp Jablonski URL: https://github.com/cbg-ethz/pareg VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/pareg/issues git_url: https://git.bioconductor.org/packages/pareg git_branch: RELEASE_3_16 git_last_commit: 1b98e91 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pareg_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pareg_1.2.0.zip vignettes: vignettes/pareg/inst/doc/pareg.html, vignettes/pareg/inst/doc/pathway_similarities.html vignetteTitles: Get started, Pathway similarities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pareg/inst/doc/pareg.R, vignettes/pareg/inst/doc/pathway_similarities.R dependencyCount: 155 Package: parglms Version: 1.30.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 55bbe9f3d2b9406cb3083f82b5224ac6 NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: RELEASE_3_16 git_last_commit: 3ede080 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/parglms_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/parglms_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/parglms_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/parglms_1.30.0.tgz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 37 Package: parody Version: 1.56.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle, testthat, rmarkdown License: Artistic-2.0 MD5sum: 12692ac0d5c1eb01e6ecc67cdd631f81 NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics. biocViews: MultipleComparison Author: Vince Carey [aut, cre] () Maintainer: Vince Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: RELEASE_3_16 git_last_commit: 5363dc0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/parody_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/parody_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/parody_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/parody_1.56.0.tgz vignettes: vignettes/parody/inst/doc/parody.html vignetteTitles: parody: parametric and resistant outlier dytection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 2 Package: PAST Version: 1.14.0 Depends: R (>= 4.0) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: 20b5c250a365a417233dcb3ebff43899 NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: RELEASE_3_16 git_last_commit: 63b4808 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PAST_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PAST_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PAST_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PAST_1.14.0.tgz vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 85 Package: Path2PPI Version: 1.28.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: ae69e228709427ac44cedaefabfb9e41 NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: RELEASE_3_16 git_last_commit: 71dd5cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Path2PPI_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Path2PPI_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Path2PPI_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Path2PPI_1.28.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 13 Package: pathifier Version: 1.36.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: f1c80d82787aab53a20a3a6ebf096df8 NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: RELEASE_3_16 git_last_commit: dbe4224 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pathifier_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathifier_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathifier_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pathifier_1.36.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R dependencyCount: 9 Package: PathNet Version: 1.38.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: d338f895d806194c645215682f11b50f NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: RELEASE_3_16 git_last_commit: a12f0a8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PathNet_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PathNet_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PathNet_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PathNet_1.38.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R dependencyCount: 0 Package: PathoStat Version: 1.24.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: ef2d509dad70cad92a110d8b536330b8 NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues git_url: https://git.bioconductor.org/packages/PathoStat git_branch: RELEASE_3_16 git_last_commit: 0a8d481 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PathoStat_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PathoStat_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PathoStat_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PathoStat_1.24.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 214 Package: pathRender Version: 1.66.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: f6beb0530c7f499593d74d907b479f22 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: RELEASE_3_16 git_last_commit: ee26e86 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pathRender_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathRender_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathRender_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pathRender_1.66.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 51 Package: pathVar Version: 1.28.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: 81a7ce01202ec4e3bc5c18c0cbc3cabf NeedsCompilation: no Title: Methods to Find Pathways with Significantly Different Variability Description: This package contains the functions to find the pathways that have significantly different variability than a reference gene set. It also finds the categories from this pathway that are significant where each category is a cluster of genes. The genes are separated into clusters by their level of variability. biocViews: GeneticVariability, GeneSetEnrichment, Pathways Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/pathVar git_branch: RELEASE_3_16 git_last_commit: de85e15 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pathVar_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathVar_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathVar_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pathVar_1.28.0.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{PathVar} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathVar/inst/doc/pathVar.R dependencyCount: 40 Package: pathview Version: 1.38.0 Depends: R (>= 3.5.0) Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, org.Hs.eg.db, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 74fb173381014b8c5d4cdbdbdd1e2e21 NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/pathview, https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: RELEASE_3_16 git_last_commit: 8229376 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pathview_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathview_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathview_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pathview_1.38.0.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: BioNetStat, EGSEA, RNASeqR, SBGNview importsMe: debrowser, EnrichmentBrowser, GDCRNATools, MAGeCKFlute, TCGAbiolinksGUI, lilikoi, SQMtools suggestsMe: gage, TCGAbiolinks, gageData, CAGEWorkflow dependencyCount: 52 Package: pathwayPCA Version: 1.14.0 Depends: R (>= 3.1) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: b530975ed7189e68cdf9e954a17c71da NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: RELEASE_3_16 git_last_commit: 40823ce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pathwayPCA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pathwayPCA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pathwayPCA_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pathwayPCA_1.14.0.tgz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R importsMe: fcoex dependencyCount: 12 Package: paxtoolsr Version: 1.32.0 Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr, rappdirs Suggests: testthat, knitr, BiocStyle, formatR, rmarkdown, RColorBrewer, foreach, doSNOW, parallel, org.Hs.eg.db, clusterProfiler License: LGPL-3 MD5sum: 1e4277e9841c1b115e60072c9625584a NeedsCompilation: no Title: Access Pathways from Multiple Databases Through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database. Pathway Commons is a project by the Memorial Sloan-Kettering Cancer Center (MSKCC), Dana-Farber Cancer Institute (DFCI), and the University of Toronto. Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG Author: Augustin Luna [aut, cre] Maintainer: Augustin Luna URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/paxtoolsr git_branch: RELEASE_3_16 git_last_commit: cecb363 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/paxtoolsr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/paxtoolsr_1.31.0.zip mac.binary.ver: bin/macosx/contrib/4.2/paxtoolsr_1.31.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/paxtoolsr_1.32.0.tgz vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html vignetteTitles: Using PaxtoolsR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R suggestsMe: netboxr dependencyCount: 51 Package: pcaExplorer Version: 2.24.0 Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: 4ab5a9e0980f63a76b400f2986503c61 NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues git_url: https://git.bioconductor.org/packages/pcaExplorer git_branch: RELEASE_3_16 git_last_commit: 15ff062 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pcaExplorer_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pcaExplorer_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pcaExplorer_2.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pcaExplorer_2.24.0.tgz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html, vignettes/pcaExplorer/inst/doc/upandrunning.html vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R, vignettes/pcaExplorer/inst/doc/upandrunning.R importsMe: ideal dependencyCount: 182 Package: pcaMethods Version: 1.90.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) MD5sum: 700a3beac1bd134bf8817b07f12cc64a NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: RELEASE_3_16 git_last_commit: 52474bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pcaMethods_1.90.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pcaMethods_1.90.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pcaMethods_1.90.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pcaMethods_1.90.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq, crmn, DiffCorr, imputeLCMD importsMe: autonomics, consensusDE, destiny, FRASER, MAI, MatrixQCvis, MSnbase, MSPrep, MultiBaC, OUTRIDER, PhosR, pmp, scde, SomaticSignatures, ADAPTS, CopSens, geneticae, LOST, MetabolomicsBasics, missCompare, multiDimBio, polyRAD, promor, RAMClustR, santaR, scMappR suggestsMe: cardelino, MsCoreUtils, QFeatures, qmtools, mtbls2, pagoda2, rsvddpd dependencyCount: 9 Package: PCAN Version: 1.26.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 Archs: x64 MD5sum: 53712e79df7e0535f804722a7da781bf NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: RELEASE_3_16 git_last_commit: afda38c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PCAN_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PCAN_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PCAN_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PCAN_1.26.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 14 Package: PCAtools Version: 2.10.0 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt, DESeq2, airway, org.Hs.eg.db, magrittr, rmarkdown License: GPL-3 MD5sum: 946d754922b2d19569ad834cd3ba4460 NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. biocViews: RNASeq, ATACSeq, GeneExpression, Transcription, SingleCell, PrincipalComponent Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: RELEASE_3_16 git_last_commit: 99d6841 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PCAtools_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PCAtools_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PCAtools_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PCAtools_2.10.0.tgz vignettes: vignettes/PCAtools/inst/doc/PCAtools.html vignetteTitles: PCAtools: everything Principal Component Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R dependsOnMe: OSCA.advanced suggestsMe: scDataviz dependencyCount: 69 Package: pcxn Version: 2.20.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE Archs: x64 MD5sum: 29841d9469ed9a99e0a298e4da5f1c67 NeedsCompilation: no Title: Exploring, analyzing and visualizing functions utilizing the pcxnData package Description: Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint). biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO, Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei Maintainer: Sokratis Kariotis git_url: https://git.bioconductor.org/packages/pcxn git_branch: RELEASE_3_16 git_last_commit: 46bb6ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pcxn_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pcxn_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pcxn_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pcxn_2.20.0.tgz vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf vignetteTitles: pcxn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R suggestsMe: pcxnData dependencyCount: 21 Package: PDATK Version: 1.6.0 Depends: R (>= 4.1), SummarizedExperiment Imports: data.table, MultiAssayExperiment, ConsensusClusterPlus, igraph, ggplotify, matrixStats, RColorBrewer, clusterRepro, CoreGx, caret, survminer, methods, S4Vectors, BiocGenerics, survival, stats, plyr, dplyr, MatrixGenerics, BiocParallel, rlang, piano, scales, survcomp, genefu, ggplot2, switchBox, reportROC, pROC, verification, utils Suggests: testthat (>= 3.0.0), msigdbr, BiocStyle, rmarkdown, knitr, HDF5Array License: MIT + file LICENSE MD5sum: 6dc56d7f10125083b10b352ac47ff6ab NeedsCompilation: no Title: Pancreatic Ductal Adenocarcinoma Tool-Kit Description: Pancreatic ductal adenocarcinoma (PDA) has a relatively poor prognosis and is one of the most lethal cancers. Molecular classification of gene expression profiles holds the potential to identify meaningful subtypes which can inform therapeutic strategy in the clinical setting. The Pancreatic Cancer Adenocarcinoma Tool-Kit (PDATK) provides an S4 class-based interface for performing unsupervised subtype discovery, cross-cohort meta-clustering, gene-expression-based classification, and subsequent survival analysis to identify prognostically useful subtypes in pancreatic cancer and beyond. Two novel methods, Consensus Subtypes in Pancreatic Cancer (CSPC) and Pancreatic Cancer Overall Survival Predictor (PCOSP) are included for consensus-based meta-clustering and overall-survival prediction, respectively. Additionally, four published subtype classifiers and three published prognostic gene signatures are included to allow users to easily recreate published results, apply existing classifiers to new data, and benchmark the relative performance of new methods. The use of existing Bioconductor classes as input to all PDATK classes and methods enables integration with existing Bioconductor datasets, including the 21 pancreatic cancer patient cohorts available in the MetaGxPancreas data package. PDATK has been used to replicate results from Sandhu et al (2019) [https://doi.org/10.1200/cci.18.00102] and an additional paper is in the works using CSPC to validate subtypes from the included published classifiers, both of which use the data available in MetaGxPancreas. The inclusion of subtype centroids and prognostic gene signatures from these and other publications will enable researchers and clinicians to classify novel patient gene expression data, allowing the direct clinical application of the classifiers included in PDATK. Overall, PDATK provides a rich set of tools to identify and validate useful prognostic and molecular subtypes based on gene-expression data, benchmark new classifiers against existing ones, and apply discovered classifiers on novel patient data to inform clinical decision making. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification, Survival, Clustering, GenePrediction Author: Vandana Sandhu [aut], Heewon Seo [aut], Christopher Eeles [aut], Neha Rohatgi [ctb], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PDATK/issues git_url: https://git.bioconductor.org/packages/PDATK git_branch: RELEASE_3_16 git_last_commit: 41ee49d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PDATK_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PDATK_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PDATK_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PDATK_1.6.0.tgz vignettes: vignettes/PDATK/inst/doc/PCOSP_model_analysis.html, vignettes/PDATK/inst/doc/PDATK_introduction.html vignetteTitles: PCOSP: Pancreatic Cancer Overall Survival Predictor, PDATK_introduction.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PDATK/inst/doc/PCOSP_model_analysis.R, vignettes/PDATK/inst/doc/PDATK_introduction.R dependencyCount: 267 Package: pdInfoBuilder Version: 1.62.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 MD5sum: 9da5c6b724867d8bd3c1d1576ccf2d6a NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: RELEASE_3_16 git_last_commit: c329152 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pdInfoBuilder_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pdInfoBuilder_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pdInfoBuilder_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pdInfoBuilder_1.62.0.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 54 Package: PeacoQC Version: 1.8.0 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) Archs: x64 MD5sum: 6c5052ec6bf9ae07b5163f282eb04d41 NeedsCompilation: no Title: Peak-based selection of high quality cytometry data Description: This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data. biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection Author: Annelies Emmaneel [aut, cre] Maintainer: Annelies Emmaneel URL: http://github.com/saeyslab/PeacoQC VignetteBuilder: knitr BugReports: http://github.com/saeyslab/PeacoQC/issues git_url: https://git.bioconductor.org/packages/PeacoQC git_branch: RELEASE_3_16 git_last_commit: fd22a75 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PeacoQC_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PeacoQC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PeacoQC_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PeacoQC_1.8.0.tgz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.html vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R dependencyCount: 80 Package: peakPantheR Version: 1.12.2 Depends: R (>= 4.2) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 3.3.0), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>= 1.0.5), bslib, shinycssloaders (>= 1.0.0), DT (>= 0.15), pracma (>= 2.2.3), utils, lubridate Suggests: testthat, devtools, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: 8d0b0922281818b7f362ef302bc385e6 NeedsCompilation: no Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (), Goncalo Correia [aut] (), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new git_url: https://git.bioconductor.org/packages/peakPantheR git_branch: RELEASE_3_16 git_last_commit: 8547bbd git_last_commit_date: 2022-12-11 Date/Publication: 2022-12-12 source.ver: src/contrib/peakPantheR_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/peakPantheR_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/peakPantheR_1.12.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/peakPantheR_1.12.2.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, peakPantheR Graphical User Interface, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 119 Package: PECA Version: 1.34.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: b753e2409744304c760fcd3ef6d08fe3 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: RELEASE_3_16 git_last_commit: 64fce76 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PECA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PECA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PECA_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PECA_1.34.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 87 Package: peco Version: 1.10.0 Depends: R (>= 3.5.0) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) MD5sum: 96a70530ba2ff34c227b310522a6e28a NeedsCompilation: no Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data Description: Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data. biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics, SingleCell, Software, StatisticalMethod, Classification, Visualization Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb] Maintainer: Chiaowen Joyce Hsiao URL: https://github.com/jhsiao999/peco VignetteBuilder: knitr BugReports: https://github.com/jhsiao999/peco/issues git_url: https://git.bioconductor.org/packages/peco git_branch: RELEASE_3_16 git_last_commit: b4c1a1b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/peco_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/peco_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/peco_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/peco_1.10.0.tgz vignettes: vignettes/peco/inst/doc/vignette.html vignetteTitles: An example of predicting cell cycle phase using peco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peco/inst/doc/vignette.R dependencyCount: 109 Package: pengls Version: 1.4.0 Depends: R (>= 4.2.0) Imports: glmnet, nlme, stats, BiocParallel Suggests: knitr,rmarkdown,testthat License: GPL-2 MD5sum: e799feb5d1bdd469f5bd727d6d65f463 NeedsCompilation: no Title: Fit Penalised Generalised Least Squares models Description: Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data. biocViews: Transcriptomics, Regression, TimeCourse, Spatial Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/sthawinke/pengls git_url: https://git.bioconductor.org/packages/pengls git_branch: RELEASE_3_16 git_last_commit: 58fb64b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pengls_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pengls_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pengls_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pengls_1.4.0.tgz vignettes: vignettes/pengls/inst/doc/penglsVignette.html vignetteTitles: Vignette of the pengls package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pengls/inst/doc/penglsVignette.R dependencyCount: 27 Package: PepsNMR Version: 1.16.0 Depends: R (>= 3.6) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE MD5sum: fe20af94cf4dce41b0c409b7d90b4721 NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: RELEASE_3_16 git_last_commit: 1f90e01 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PepsNMR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PepsNMR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PepsNMR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PepsNMR_1.16.0.tgz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 46 Package: pepStat Version: 1.32.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 38910e377dd84136891dc8768dbecf7e NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: RELEASE_3_16 git_last_commit: 09b4937 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pepStat_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pepStat_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pepStat_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pepStat_1.32.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 58 Package: pepXMLTab Version: 1.32.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2f30f83653f1f245c06f9f31e53ad946 NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: RELEASE_3_16 git_last_commit: ef6704d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pepXMLTab_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pepXMLTab_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pepXMLTab_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pepXMLTab_1.32.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: PERFect Version: 1.12.0 Depends: R (>= 3.6.0), sn (>= 1.5.2) Imports: ggplot2 (>= 3.0.0), phyloseq (>= 1.28.0), zoo (>= 1.8.3), psych (>= 1.8.4), stats (>= 3.6.0), Matrix (>= 1.2.14), fitdistrplus (>= 1.0.12), parallel (>= 3.6.0) Suggests: knitr, rmarkdown, kableExtra, ggpubr License: Artistic-2.0 MD5sum: 9cfb9d69dc1a083cd026134cfd3b2e21 NeedsCompilation: no Title: Permutation filtration for microbiome data Description: PERFect is a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds, and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. biocViews: Software, Microbiome, Sequencing, Classification, Metagenomics Author: Ekaterina Smirnova , Quy Cao Maintainer: Quy Cao URL: https://github.com/cxquy91/PERFect VignetteBuilder: knitr BugReports: https://github.com/cxquy91/PERFect/issues git_url: https://git.bioconductor.org/packages/PERFect git_branch: RELEASE_3_16 git_last_commit: cca89c3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PERFect_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PERFect_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PERFect_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PERFect_1.12.0.tgz vignettes: vignettes/PERFect/inst/doc/MethodIllustration.html vignetteTitles: Method Illustration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PERFect/inst/doc/MethodIllustration.R dependencyCount: 87 Package: periodicDNA Version: 1.8.0 Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome, BiocParallel Imports: S4Vectors, rtracklayer, stats, GenomeInfoDb, magrittr, zoo, ggplot2, methods, parallel, cowplot Suggests: BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 + file LICENSE MD5sum: f2492ea9bcd450e9142dee76875bb75f NeedsCompilation: no Title: Set of tools to identify periodic occurrences of k-mers in DNA sequences Description: This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website. biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation, Sequencing, Coverage, Alignment, DataImport Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/periodicDNA VignetteBuilder: knitr BugReports: https://github.com/js2264/periodicDNA/issues git_url: https://git.bioconductor.org/packages/periodicDNA git_branch: RELEASE_3_16 git_last_commit: 270e929 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/periodicDNA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/periodicDNA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/periodicDNA_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/periodicDNA_1.8.0.tgz vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html vignetteTitles: Introduction to periodicDNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R dependencyCount: 76 Package: PFP Version: 1.6.0 Depends: R (>= 4.0) Imports: graph, igraph, KEGGgraph, clusterProfiler, ggplot2, plyr, tidyr, magrittr, stats, methods, utils Suggests: knitr, testthat, rmarkdown, org.Hs.eg.db License: GPL-2 MD5sum: 4c195508b80cdd6e2d182965a32dd8d8 NeedsCompilation: no Title: Pathway Fingerprint Framework in R Description: An implementation of the pathway fingerprint framework that introduced in paper "Pathway Fingerprint: a novel pathway knowledge and topology based method for biomarker discovery and characterization". This method provides a systematic comparisons between a gene set (such as a list of differentially expressed genes) and well-studied "basic pathway networks" (KEGG pathways), measuring the importance of pathways and genes for the gene set. The package is helpful for researchers to find the biomarkers and its function. biocViews: Software, Pathways, RNASeq Author: XC Zhang [aut, cre] Maintainer: XC Zhang URL: https://github.com/aib-group/PFP VignetteBuilder: knitr BugReports: https://github.com/aib-group/PFP/issues git_url: https://git.bioconductor.org/packages/PFP git_branch: RELEASE_3_16 git_last_commit: 4a8a24f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PFP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PFP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PFP_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PFP_1.6.0.tgz vignettes: vignettes/PFP/inst/doc/PFP.html vignetteTitles: Pathway fingerprint: a tool for biomarker discovery based on gene expression data and pathway knowledge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PFP/inst/doc/PFP.R dependencyCount: 133 Package: pgca Version: 1.22.0 Imports: utils, stats Suggests: knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: a5ea5002df59d9dded6354cf769c1a6b NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: RELEASE_3_16 git_last_commit: b0da32d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pgca_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pgca_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pgca_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pgca_1.22.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: phantasus Version: 1.18.4 Depends: R (>= 3.5) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, data.table, curl Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: c542ef259ff4029a0a4f06e289ddddd5 NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Daria Zenkova [aut], Vladislav Kamenev [aut], Rita Sablina [ctb], Maxim Kleverov [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://genome.ifmo.ru/phantasus, https://artyomovlab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: RELEASE_3_16 git_last_commit: 4458180 git_last_commit_date: 2022-12-21 Date/Publication: 2022-12-25 source.ver: src/contrib/phantasus_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/phantasus_1.18.4.zip mac.binary.ver: bin/macosx/contrib/4.2/phantasus_1.18.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phantasus_1.18.4.tgz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 147 Package: PharmacoGx Version: 3.2.0 Depends: R (>= 3.6), CoreGx Imports: BiocGenerics, Biobase, S4Vectors, SummarizedExperiment, MultiAssayExperiment, BiocParallel, ggplot2, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table, checkmate, boot, coop LinkingTo: Rcpp Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, markdown, BiocStyle License: GPL (>= 3) MD5sum: d48ca1013ad8dbe88df73174ad0a2ff2 NeedsCompilation: yes Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov [aut], Christopher Eeles [aut], Zhaleh Safikhani [aut], Mark Freeman [aut], Feifei Li [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: RELEASE_3_16 git_last_commit: 58af756 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PharmacoGx_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PharmacoGx_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PharmacoGx_3.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PharmacoGx_3.2.0.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.html, vignettes/PharmacoGx/inst/doc/PharmacoGx.html vignetteTitles: Creating a PharmacoSet Object, PharmacoGx: An R Package for Analysis of Large Pharmacogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: Xeva suggestsMe: ToxicoGx dependencyCount: 147 Package: phemd Version: 1.14.1 Depends: R (>= 3.5), monocle, Seurat Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma, cluster, Rtsne, destiny, RANN, ggplot2, maptree, pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics, stats, utils, cowplot, S4Vectors, BiocGenerics, SummarizedExperiment, Biobase, phateR, reticulate Suggests: knitr License: GPL-2 Archs: x64 MD5sum: 2f8a7d63e9747a7b2f8f099d1009ce00 NeedsCompilation: no Title: Phenotypic EMD for comparison of single-cell samples Description: Package for comparing and generating a low-dimensional embedding of multiple single-cell samples. biocViews: Clustering, ComparativeGenomics, Proteomics, Transcriptomics, Sequencing, DimensionReduction, SingleCell, DataRepresentation, Visualization, MultipleComparison Author: William S Chen Maintainer: William S Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phemd git_branch: RELEASE_3_16 git_last_commit: 8e5420a git_last_commit_date: 2023-03-27 Date/Publication: 2023-03-27 source.ver: src/contrib/phemd_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/phemd_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/phemd_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phemd_1.14.1.tgz vignettes: vignettes/phemd/inst/doc/phemd.html vignetteTitles: PhEMD vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phemd/inst/doc/phemd.R dependencyCount: 236 Package: PhenoGeneRanker Version: 1.6.0 Imports: igraph, Matrix, foreach, doParallel, dplyr, stats, utils, parallel Suggests: knitr, rmarkdown License: Creative Commons Attribution 4.0 International License MD5sum: b5b666ffec19642d6d1a2f7f77a56b82 NeedsCompilation: no Title: PhenoGeneRanker: A gene and phenotype prioritization tool Description: This package is a gene/phenotype prioritization tool that utilizes multiplex heterogeneous gene phenotype network. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p-values of gene/phenotype ranking using random stratified sampling of genes/phenotypes based on their connectivity degree in the network. https://dl.acm.org/doi/10.1145/3307339.3342155. biocViews: BiomedicalInformatics, GenePrediction, GraphAndNetwork, Network, NetworkInference, Pathways, Software, SystemsBiology Author: Cagatay Dursun [aut, cre] Maintainer: Cagatay Dursun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhenoGeneRanker git_branch: RELEASE_3_16 git_last_commit: 76eed11 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PhenoGeneRanker_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhenoGeneRanker_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhenoGeneRanker_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PhenoGeneRanker_1.6.0.tgz vignettes: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.html vignetteTitles: PhenoGeneRanker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PhenoGeneRanker/inst/doc/PhenoGeneRanker.R dependencyCount: 31 Package: phenomis Version: 1.0.2 Depends: SummarizedExperiment Imports: Biobase, biodb, biodbChebi, data.table, futile.logger, ggplot2, ggrepel, graphics, grDevices, grid, htmlwidgets, igraph, limma, methods, MultiAssayExperiment, MultiDataSet, PMCMRplus, plotly, ranger, RColorBrewer, ropls, stats, tibble, tidyr, utils, VennDiagram Suggests: BiocGenerics, BiocStyle, biosigner, CLL, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 1490b92264a0a57b473e57cc8409f1f4 NeedsCompilation: no Title: Postprocessing and univariate analysis of omics data Description: The 'phenomis' package provides methods to perform post-processing (i.e. quality control and normalization) as well as univariate statistical analysis of single and multi-omics data sets. These methods include quality control metrics, signal drift and batch effect correction, intensity transformation, univariate hypothesis testing, but also clustering (as well as annotation of metabolomics data). The data are handled in the standard Bioconductor formats (i.e. SummarizedExperiment and MultiAssayExperiment for single and multi-omics datasets, respectively; the alternative ExpressionSet and MultiDataSet formats are also supported for convenience). As a result, all methods can be readily chained as workflows. The pipeline can be further enriched by multivariate analysis and feature selection, by using the 'ropls' and 'biosigner' packages, which support the same formats. Data can be conveniently imported from and exported to text files. Although the methods were initially targeted to metabolomics data, most of the methods can be applied to other types of omics data (e.g., transcriptomics, proteomics). biocViews: BatchEffect, Clustering, Coverage, KEGG, MassSpectrometry, Metabolomics, Normalization, Proteomics, QualityControl, Sequencing, StatisticalMethod, Transcriptomics Author: Etienne A. Thevenot [aut, cre] (), Natacha Lenuzza [ctb], Marie Tremblay-Franco [ctb], Alyssa Imbert [ctb], Pierrick Roger [ctb], Eric Venot [ctb], Sylvain Dechaumet [ctb] Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1038/s41597-021-01095-3 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenomis git_branch: RELEASE_3_16 git_last_commit: 99bb60e git_last_commit_date: 2022-11-06 Date/Publication: 2022-11-06 source.ver: src/contrib/phenomis_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/phenomis_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/phenomis_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phenomis_1.0.2.tgz vignettes: vignettes/phenomis/inst/doc/phenomis-vignette.html vignetteTitles: phenomis-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenomis/inst/doc/phenomis-vignette.R dependencyCount: 150 Package: phenopath Version: 1.22.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) MD5sum: 88ebeb09839decbce45974019138fad2 NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: RELEASE_3_16 git_last_commit: 8124a22 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/phenopath_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phenopath_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phenopath_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phenopath_1.22.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 61 Package: phenoTest Version: 1.46.0 Depends: R (>= 3.6.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, Hmisc Imports: survival, limma, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, hgu133a.db, ellipse Suggests: GSEABase, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) Archs: x64 MD5sum: 51930f0e486e33a43f414dd8c7b9a312 NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet git_url: https://git.bioconductor.org/packages/phenoTest git_branch: RELEASE_3_16 git_last_commit: da0d349 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/phenoTest_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phenoTest_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phenoTest_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phenoTest_1.46.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR dependencyCount: 143 Package: PhenStat Version: 2.34.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: 7dced586e94fcf13cacbfd0f581fd56d NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi git_url: https://git.bioconductor.org/packages/PhenStat git_branch: RELEASE_3_16 git_last_commit: d0f8883 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PhenStat_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhenStat_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhenStat_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PhenStat_2.34.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf vignetteTitles: PhenStat Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R dependencyCount: 103 Package: philr Version: 1.24.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree, methods Suggests: testthat, knitr, ecodist, rmarkdown, BiocStyle, phyloseq, SummarizedExperiment, TreeSummarizedExperiment, glmnet, dplyr, mia License: GPL-3 MD5sum: 988e7af713c22b59fc49b9eb7d73d14a NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman [aut, cre], Leo Lahti [ctb] () Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: RELEASE_3_16 git_last_commit: 321e1c2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/philr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/philr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/philr_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/philr_1.24.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R dependencyCount: 63 Package: PhIPData Version: 1.6.0 Depends: R (>= 4.1.0), SummarizedExperiment (>= 1.3.81) Imports: BiocFileCache, BiocGenerics, methods, GenomicRanges, IRanges, S4Vectors, edgeR, cli, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, covr, dplyr, readr, withr License: MIT + file LICENSE MD5sum: fa207d90ab4338e50b62dec150973f27 NeedsCompilation: no Title: Container for PhIP-Seq Experiments Description: PhIPData defines an S4 class for phage-immunoprecipitation sequencing (PhIP-seq) experiments. Buliding upon the RangedSummarizedExperiment class, PhIPData enables users to coordinate metadata with experimental data in analyses. Additionally, PhIPData provides specialized methods to subset and identify beads-only samples, subset objects using virus aliases, and use existing peptide libraries to populate object parameters. biocViews: Infrastructure, DataRepresentation, Sequencing, Coverage Author: Athena Chen [aut, cre] (), Rob Scharpf [aut], Ingo Ruczinski [aut] Maintainer: Athena Chen VignetteBuilder: knitr BugReports: https://github.com/athchen/PhIPData/issues git_url: https://git.bioconductor.org/packages/PhIPData git_branch: RELEASE_3_16 git_last_commit: 0b4522b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PhIPData_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhIPData_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhIPData_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PhIPData_1.6.0.tgz vignettes: vignettes/PhIPData/inst/doc/PhIPData.html vignetteTitles: PhIPData: A Container for PhIP-Seq Experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhIPData/inst/doc/PhIPData.R dependsOnMe: beer dependencyCount: 70 Package: phosphonormalizer Version: 1.22.0 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 6ec7cdb7b0592ce2e4c249e76eeea5c0 NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb], Laura Elo [ths] Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: RELEASE_3_16 git_last_commit: 277444b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/phosphonormalizer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phosphonormalizer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phosphonormalizer_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phosphonormalizer_1.22.0.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhosR Version: 1.8.0 Depends: R (>= 4.1.0) Imports: ruv, e1071, dendextend, limma, pcaMethods, stats, RColorBrewer, circlize, dplyr, igraph, pheatmap, preprocessCore, tidyr, rlang, graphics, grDevices, utils, SummarizedExperiment, methods, S4Vectors, BiocGenerics, ggplot2, GGally, ggdendro, ggpubr, network, reshape2, ggtext Suggests: testthat, knitr, rgl, sna, ClueR, directPA, rmarkdown, org.Rn.eg.db, org.Mm.eg.db, reactome.db, annotate, BiocStyle, stringr, calibrate License: GPL-3 + file LICENSE MD5sum: 316c8ece1f21947509b638a657eea0eb NeedsCompilation: no Title: A set of methods and tools for comprehensive analysis of phosphoproteomics data Description: PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways. biocViews: Software, ResearchField, Proteomics Author: Pengyi Yang [aut], Taiyun Kim [aut, cre], Hani Jieun Kim [aut] Maintainer: Taiyun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: RELEASE_3_16 git_last_commit: f83ed58 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PhosR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhosR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PhosR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PhosR_1.8.0.tgz vignettes: vignettes/PhosR/inst/doc/PhosR.html vignetteTitles: An introduction to PhosR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhosR/inst/doc/PhosR.R dependencyCount: 142 Package: PhyloProfile Version: 1.12.6 Depends: R (>= 4.2.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, RCurl, shiny, shinyBS, shinyFiles, shinyjs, OmaDB, plyr, xml2, zoo, yaml Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 914a7c7fc33188eb0eff65587c3981bf NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction Author: Vinh Tran [aut, cre] (), Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: RELEASE_3_16 git_last_commit: f886d6a git_last_commit_date: 2023-02-24 Date/Publication: 2023-02-24 source.ver: src/contrib/PhyloProfile_1.12.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/PhyloProfile_1.12.6.zip mac.binary.ver: bin/macosx/contrib/4.2/PhyloProfile_1.12.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PhyloProfile_1.12.6.tgz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 140 Package: phyloseq Version: 1.42.0 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7-4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: edf40c9718a0242aec1731026fa9c610 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: RELEASE_3_16 git_last_commit: de6be71 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/phyloseq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/phyloseq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/phyloseq_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/phyloseq_1.42.0.tgz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT, phyloseqGraphTest importsMe: benchdamic, combi, MBECS, metavizr, microbiomeDASim, microbiomeMarker, PathoStat, PERFect, RCM, reconsi, RPA, SimBu, SPsimSeq, HMP2Data, adaptiveGPCA, breakaway, corncob, ggpicrust2, HTSSIP, microbial, MicrobiomeStat, mixKernel, SigTree, SIPmg, treeDA suggestsMe: CBEA, decontam, mia, MicrobiotaProcess, MMUPHin, philr, HMP16SData, fido, file2meco, metacoder dependencyCount: 77 Package: Pi Version: 2.10.0 Depends: igraph, dnet, ggplot2, graphics Imports: Matrix, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, lattice, caret, plot3D, stats, methods, MASS, IRanges, BiocGenerics, dplyr, tidyr, ggnetwork, osfr, RCircos, purrr, readr, tibble Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ggforce, fgsea, RColorBrewer, ggpubr, rtracklayer, ggbio, Gviz, data.tree, jsonlite License: GPL-3 MD5sum: bb7b0d9d7b90347c7459a00e0ca2eb9a NeedsCompilation: no Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level Description: Priority index or Pi is developed as a genomic-led target prioritisation system. It integrates functional genomic predictors, knowledge of network connectivity and immune ontologies to prioritise potential drug targets at the gene and pathway level. biocViews: Software, Genetics, GraphAndNetwork, Pathways, GeneExpression, GeneTarget, GenomeWideAssociation, LinkageDisequilibrium, Network, HiC Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight Maintainer: Hai Fang URL: http://pi314.r-forge.r-project.org VignetteBuilder: knitr BugReports: https://github.com/hfang-bristol/Pi/issues git_url: https://git.bioconductor.org/packages/Pi git_branch: RELEASE_3_16 git_last_commit: a5ca530 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Pi_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Pi_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Pi_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Pi_2.10.0.tgz vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html vignetteTitles: Pi User Manual (R/Bioconductor package) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R dependencyCount: 145 Package: piano Version: 2.14.0 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: 56f7a2c1893fa5af9bf5f5ba40b20fb6 NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: RELEASE_3_16 git_last_commit: 11f1964 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/piano_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/piano_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/piano_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/piano_2.14.0.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: CoreGx, PDATK suggestsMe: cosmosR, BloodCancerMultiOmics2017 dependencyCount: 104 Package: pickgene Version: 1.70.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 4aea3a16f350f432867f032e1700ec2e NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: RELEASE_3_16 git_last_commit: 4b92a02 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pickgene_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pickgene_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pickgene_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pickgene_1.70.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: PICS Version: 2.42.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 MD5sum: 7430d3f4c1ba2ed9e655f8fc3caa0fdf NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPseq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud URL: https://github.com/SRenan/PICS VignetteBuilder: knitr BugReports: https://github.com/SRenan/PICS/issues git_url: https://git.bioconductor.org/packages/PICS git_branch: RELEASE_3_16 git_last_commit: 135440e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PICS_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PICS_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PICS_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PICS_2.42.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.html vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING dependencyCount: 40 Package: Pigengene Version: 1.24.0 Depends: R (>= 4.0.3), graph, BiocStyle (>= 2.18.1) Imports: bnlearn (>= 4.7), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata, clusterProfiler, ReactomePA, ggplot2, openxlsx, DBI, DOSE Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, AnnotationDbi, energy License: GPL (>=2) Archs: x64 MD5sum: 1b24f875454e673dbe3b6be48985c978 NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, Meghan Short, Isha Mehta, and Neda Emami Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: RELEASE_3_16 git_last_commit: ecba84d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Pigengene_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Pigengene_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Pigengene_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Pigengene_1.24.0.tgz vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf vignetteTitles: Pigengene: Computing and using eigengenes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R dependencyCount: 186 Package: PING Version: 2.42.0 Depends: R(>= 3.5.0) Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, GenomicRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 MD5sum: fb4ec5041e863c1038b8ba014992ec12 NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud git_url: https://git.bioconductor.org/packages/PING git_branch: RELEASE_3_16 git_last_commit: 1364f14 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PING_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PING_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PING_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PING_2.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 173 Package: pipeComp Version: 1.8.0 Depends: R (>= 4.1) Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment, SummarizedExperiment, Seurat, matrixStats, Matrix, cluster, aricode, methods, utils, dplyr, grid, scales, scran, viridisLite, clue, randomcoloR, ggplot2, cowplot, intrinsicDimension, scater, knitr, reshape2, stats, Rtsne, uwot, circlize, RColorBrewer Suggests: BiocStyle, rmarkdown License: GPL Archs: x64 MD5sum: b6913a4fdd99bc7d42ccd1b617b2bf2a NeedsCompilation: no Title: pipeComp pipeline benchmarking framework Description: A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data. biocViews: GeneExpression, Transcriptomics, Clustering, DataRepresentation Author: Pierre-Luc Germain [cre, aut] (), Anthony Sonrel [aut] (), Mark D. Robinson [aut, fnd] () Maintainer: Pierre-Luc Germain URL: https://doi.org/10.1186/s13059-020-02136-7 VignetteBuilder: knitr BugReports: https://github.com/plger/pipeComp git_url: https://git.bioconductor.org/packages/pipeComp git_branch: RELEASE_3_16 git_last_commit: 964a400 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pipeComp_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pipeComp_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pipeComp_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pipeComp_1.8.0.tgz vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html, vignettes/pipeComp/inst/doc/pipeComp_scRNA.html, vignettes/pipeComp/inst/doc/pipeComp.html vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R, vignettes/pipeComp/inst/doc/pipeComp_scRNA.R, vignettes/pipeComp/inst/doc/pipeComp.R dependencyCount: 214 Package: pipeFrame Version: 1.14.0 Depends: R (>= 4.0.0), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils, rmarkdown Suggests: BiocManager, knitr, rtracklayer, testthat License: GPL-3 MD5sum: 2555bb338c143020a5268ce24dee123a NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: RELEASE_3_16 git_last_commit: 6bfd083 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pipeFrame_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pipeFrame_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pipeFrame_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pipeFrame_1.14.0.tgz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: enrichTF, esATAC dependencyCount: 79 Package: pkgDepTools Version: 1.63.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocManager License: GPL-2 MD5sum: 920edaddf79dcd16bd4f2a2ca6e7c72b NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/pkgDepTools git_branch: master git_last_commit: 223ca25 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-28 source.ver: src/contrib/pkgDepTools_1.63.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pkgDepTools_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pkgDepTools_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pkgDepTools_1.64.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R dependencyCount: 9 Package: planet Version: 1.6.0 Depends: R (>= 4.0) Imports: methods, tibble, magrittr, dplyr Suggests: ggplot2, testthat, tidyr, scales, minfi, EpiDISH, knitr, rmarkdown License: GPL-2 MD5sum: 55e48dd368edd0bb33733605ffc4bed8 NeedsCompilation: no Title: Placental DNA methylation analysis tools Description: This package contains R functions to infer additional biological variables to supplemental DNA methylation analysis of placental data. This includes inferring ethnicity/ancestry, gestational age, and cell composition from placental DNA methylation array (450k/850k) data. The package comes with an example processed placental dataset. biocViews: Software, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, DNAMethylation, CpGIsland Author: Victor Yuan [aut, cre], Wendy P. Robinson [ctb] Maintainer: Victor Yuan URL: https://victor.rbind.io/planet, http://github.com/wvictor14/planet VignetteBuilder: knitr BugReports: http://github.com/wvictor14/planet/issues git_url: https://git.bioconductor.org/packages/planet git_branch: RELEASE_3_16 git_last_commit: 20b35d5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/planet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/planet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/planet_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/planet_1.6.0.tgz vignettes: vignettes/planet/inst/doc/planet.html vignetteTitles: planet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/planet/inst/doc/planet.R importsMe: methylclock dependencyCount: 21 Package: plethy Version: 1.36.0 Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1), BiocGenerics, S4Vectors Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 449d5f82b30430a259505be9cecae231 NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly git_url: https://git.bioconductor.org/packages/plethy git_branch: RELEASE_3_16 git_last_commit: d6b74a5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/plethy_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plethy_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plethy_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/plethy_1.36.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R dependencyCount: 61 Package: plgem Version: 1.70.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: b6743e303cbb3acbb4e764a0991f490f NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: RELEASE_3_16 git_last_commit: 47de614 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/plgem_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plgem_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plgem_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/plgem_1.70.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 8 Package: plier Version: 1.68.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) MD5sum: e3fcc02d9b478c31ea494c34a81447b2 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: RELEASE_3_16 git_last_commit: 378aa06 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/plier_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plier_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plier_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/plier_1.68.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 12 Package: PloGO2 Version: 1.10.0 Depends: R (>= 4.0), GO.db, GOstats Imports: lattice, httr, openxlsx, xtable License: GPL-2 Archs: x64 MD5sum: 35d47bf501fc13ea48bac0ba216d1f4e NeedsCompilation: no Title: Plot Gene Ontology and KEGG pathway Annotation and Abundance Description: Functions for enrichment analysis and plotting gene ontology or KEGG pathway information for multiple data subsets at the same time. It also enables encorporating multiple conditions and abundance data. biocViews: Annotation, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison, Pathways, Software, Visualization Author: Dana Pascovici, Jemma Wu Maintainer: Jemma Wu , Dana Pascovici git_url: https://git.bioconductor.org/packages/PloGO2 git_branch: RELEASE_3_16 git_last_commit: 4ea8b55 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PloGO2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PloGO2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PloGO2_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PloGO2_1.10.0.tgz vignettes: vignettes/PloGO2/inst/doc/PloGO2_vignette.pdf, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PloGO2/inst/doc/PloGO2_vignette.R, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.R dependencyCount: 68 Package: plotgardener Version: 1.4.2 Depends: R (>= 4.1.0) Imports: curl, data.table, dplyr, grDevices, grid, ggplotify, IRanges, methods, plyranges, purrr, Rcpp, RColorBrewer, rlang, stats, strawr, tools, utils LinkingTo: Rcpp Suggests: AnnotationDbi, AnnotationHub, BSgenome, BSgenome.Hsapiens.UCSC.hg19, ComplexHeatmap, GenomicFeatures, GenomeInfoDb, GenomicRanges, ggplot2, InteractionSet, knitr, org.Hs.eg.db, rtracklayer, plotgardenerData, png, rmarkdown, scales, showtext, testthat (>= 3.0.0), TxDb.Hsapiens.UCSC.hg19.knownGene License: MIT + file LICENSE MD5sum: 1e90b16ac8b11e3aabf7fe9a1314c572 NeedsCompilation: yes Title: Coordinate-Based Genomic Visualization Package for R Description: Coordinate-based genomic visualization package for R. It grants users the ability to programmatically produce complex, multi-paneled figures. Tailored for genomics, plotgardener allows users to visualize large complex genomic datasets and provides exquisite control over how plots are placed and arranged on a page. biocViews: Visualization, GenomeAnnotation, FunctionalGenomics, GenomeAssembly, HiC Author: Nicole Kramer [aut, cre] (), Eric S. Davis [aut] (), Craig Wenger [aut] (), Sarah Parker [ctb], Erika Deoudes [art], Michael Love [ctb], Douglas H. Phanstiel [aut, cph] Maintainer: Nicole Kramer URL: https://phanstiellab.github.io/plotgardener, https://github.com/PhanstielLab/plotgardener VignetteBuilder: knitr BugReports: https://github.com/PhanstielLab/plotgardener/issues git_url: https://git.bioconductor.org/packages/plotgardener git_branch: RELEASE_3_16 git_last_commit: e5ae277 git_last_commit_date: 2022-12-09 Date/Publication: 2022-12-09 source.ver: src/contrib/plotgardener_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/plotgardener_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/plotgardener_1.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/plotgardener_1.4.2.tgz vignettes: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.html vignetteTitles: Introduction to plotgardener hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/plotgardener/inst/doc/introduction_to_plotgardener.R importsMe: Ularcirc suggestsMe: nullranges dependencyCount: 85 Package: plotGrouper Version: 1.16.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 6287474de924766cf7bbf00fde0a1cae NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: RELEASE_3_16 git_last_commit: daba8c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/plotGrouper_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plotGrouper_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plotGrouper_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/plotGrouper_1.16.0.tgz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 142 Package: PLPE Version: 1.58.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 95abd992d420577d056009d4ff143956 NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: RELEASE_3_16 git_last_commit: c009eb8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PLPE_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PLPE_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PLPE_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PLPE_1.58.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 9 Package: plyranges Version: 1.18.0 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 MD5sum: 0f987f6850662c460cd5d9785f6b9122 NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut, cre] (), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] () Maintainer: Stuart Lee VignetteBuilder: knitr BugReports: https://github.com/sa-lee/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: RELEASE_3_16 git_last_commit: 580a5e1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/plyranges_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/plyranges_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/plyranges_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/plyranges_1.18.0.tgz vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R importsMe: BOBaFIT, BUSpaRse, cfDNAPro, dasper, InPAS, katdetectr, methylCC, multicrispr, nearBynding, nullranges, plotgardener, fluentGenomics, MOCHA suggestsMe: EpiCompare, extraChIPs, memes, svaNUMT, svaRetro, CTCF dependencyCount: 62 Package: pmm Version: 1.30.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: e32231e0857b47f160d73470632206bd NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: RELEASE_3_16 git_last_commit: c71a174 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pmm_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pmm_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pmm_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pmm_1.30.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 50 Package: pmp Version: 1.10.0 Depends: R (>= 4.0) Imports: stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick License: GPL-3 MD5sum: fe8eae5ee1b752b11508d83c17ad7550 NeedsCompilation: no Title: Peak Matrix Processing and signal batch correction for metabolomics datasets Description: Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets. biocViews: MassSpectrometry, Metabolomics, Software, QualityControl, BatchEffect Author: Andris Jankevics [aut], Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: RELEASE_3_16 git_last_commit: 63c1c32 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pmp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pmp_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pmp_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pmp_1.10.0.tgz vignettes: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html vignetteTitles: Peak Matrix Processing for metabolomics datasets, Signal drift and batch effect correction and mass spectral quality assessment, Signal drift and batch effect correction for mass spectrometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R suggestsMe: metabolomicsWorkbenchR, structToolbox dependencyCount: 69 Package: PoDCall Version: 1.6.0 Depends: R (>= 4.2) Imports: ggplot2, gridExtra, mclust, diptest, rlist, shiny, DT, LaplacesDemon, purrr, shinyjs, readr, grDevices, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 5873a3a7cbfe65a0402aed8e7b1cc20a NeedsCompilation: no Title: Positive Droplet Calling for DNA Methylation Droplet Digital PCR Description: Reads files exported from 'QuantaSoft' containing amplitude values from a run of ddPCR (96 well plate) and robustly sets thresholds to determine positive droplets for each channel of each individual well. Concentration and normalized concentration in addition to other metrics is then calculated for each well. Results are returned as a table, optionally written to file, as well as optional plots (scatterplot and histogram) for both channels per well written to file. The package includes a shiny application which provides an interactive and user-friendly interface to the full functionality of PoDCall. biocViews: Classification, Epigenetics, ddPCR, DifferentialMethylation, CpGIsland, DNAMethylation, Author: Hans Petter Brodal [aut, cre], Marine Jeanmougin [aut], Guro Elisabeth Lind [aut] Maintainer: Hans Petter Brodal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoDCall git_branch: RELEASE_3_16 git_last_commit: 27621d0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PoDCall_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PoDCall_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PoDCall_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PoDCall_1.6.0.tgz vignettes: vignettes/PoDCall/inst/doc/PoDCall.html vignetteTitles: PoDCall: Positive Droplet Caller for DNA Methylation ddPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoDCall/inst/doc/PoDCall.R dependencyCount: 94 Package: podkat Version: 1.30.0 Depends: R (>= 3.5.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) MD5sum: c7a6518f5940692d915312889811b4b2 NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: RELEASE_3_16 git_last_commit: e58e132 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/podkat_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/podkat_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/podkat_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/podkat_1.30.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 48 Package: pogos Version: 1.18.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: a4e1366a8ffe214852f79e2f99c6e596 NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: RELEASE_3_16 git_last_commit: 6a85cb4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pogos_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pogos_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pogos_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pogos_1.18.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R suggestsMe: BiocOncoTK dependencyCount: 125 Package: polyester Version: 1.34.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown, markdown License: Artistic-2.0 MD5sum: 590c94919c358a4ea6381c2b76f427d7 NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek Maintainer: Jack Fu , Jeff Leek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/polyester git_branch: RELEASE_3_16 git_last_commit: a8dc31b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/polyester_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/polyester_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/polyester_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/polyester_1.34.0.tgz vignettes: vignettes/polyester/inst/doc/polyester.html vignetteTitles: The Polyester package for simulating RNA-seq reads hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R dependencyCount: 20 Package: POMA Version: 1.8.0 Depends: R (>= 4.0) Imports: broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, ggplot2, ggrepel, glasso (>= 1.11), glmnet, impute, limma, magrittr, mixOmics, randomForest, RankProd (>= 3.14), rmarkdown, SummarizedExperiment, tibble, tidyr, uwot, vegan Suggests: BiocStyle, covr, ggraph, knitr, patchwork, plotly, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 62f4d7ae06dc971d3ac6cfa99cac45e4 NeedsCompilation: no Title: Tools for Omics Data Analysis Description: A reproducible and easy-to-use toolkit for visualization, pre-processing, exploration, and statistical analysis of omics datasets. The main aim of POMA is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. This package has a Shiny app version called POMAShiny that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny. See Castellano-Escuder P, González-Domínguez R, Carmona-Pontaque F, et al. (2021) for more details. biocViews: BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, Regression, RNASeq, Software, StatisticalMethod, Visualization Author: Pol Castellano-Escuder [aut, cre] () Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/POMA VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/POMA/issues git_url: https://git.bioconductor.org/packages/POMA git_branch: RELEASE_3_16 git_last_commit: 3a1c9c6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/POMA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/POMA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/POMA_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/POMA_1.8.0.tgz vignettes: vignettes/POMA/inst/doc/POMA-demo.html, vignettes/POMA/inst/doc/POMA-eda.html, vignettes/POMA/inst/doc/POMA-normalization.html vignetteTitles: POMA Workflow, POMA EDA Example, POMA Normalization Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-demo.R, vignettes/POMA/inst/doc/POMA-eda.R, vignettes/POMA/inst/doc/POMA-normalization.R suggestsMe: fobitools dependencyCount: 188 Package: PoTRA Version: 1.13.0 Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph, graph, graphite Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis License: LGPL MD5sum: 02bf318cec1add1135188fe30a7bbb1d NeedsCompilation: no Title: PoTRA: Pathways of Topological Rank Analysis Description: The PoTRA analysis is based on topological ranks of genes in biological pathways. PoTRA can be used to detect pathways involved in disease (Li, Liu & Dinu, 2018). We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fishers Exact test to determine if the number of hub genes in each pathway is altered from normal to cancer (Li, Liu & Dinu, 2018). Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer (Li, Liu & Dinu, 2018). Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes (Li, Liu & Dinu, 2018). PoTRA can be used with the KEGG, Reactome, SMPDB and PharmGKB, Panther, PathBank, etc databases from the devel graphite library. biocViews: GraphAndNetwork, StatisticalMethod, GeneExpression, DifferentialExpression, Pathways, Reactome, Network, KEGG, PathBank, Panther Author: Chaoxing Li, Li Liu and Valentin Dinu Maintainer: Margaret Linan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoTRA git_branch: master git_last_commit: 34eec3c git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/PoTRA_1.13.0.tar.gz vignettes: vignettes/PoTRA/inst/doc/PoTRA.html vignetteTitles: Pathways of Topological Rank Analysis (PoTRA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoTRA/inst/doc/PoTRA.R dependencyCount: 56 Package: powerTCR Version: 1.18.0 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c06a9c5ae01529c0e3d125c3628d3df7 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: RELEASE_3_16 git_last_commit: 3e9b227 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/powerTCR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/powerTCR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/powerTCR_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/powerTCR_1.18.0.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R importsMe: scRepertoire dependencyCount: 36 Package: POWSC Version: 1.6.0 Depends: R (>= 4.1), Biobase, SingleCellExperiment, MAST Imports: pheatmap, ggplot2, RColorBrewer, grDevices, SummarizedExperiment, limma Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle License: GPL-2 MD5sum: a25ab9cad5c3baaebf03860213214377 NeedsCompilation: no Title: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq Description: Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship. biocViews: DifferentialExpression, ImmunoOncology, SingleCell, Software Author: Kenong Su [aut, cre], Hao Wu [aut] Maintainer: Kenong Su VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/POWSC git_branch: RELEASE_3_16 git_last_commit: 696b672 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/POWSC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/POWSC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/POWSC_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/POWSC_1.6.0.tgz vignettes: vignettes/POWSC/inst/doc/POWSC.html vignetteTitles: The POWSC User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POWSC/inst/doc/POWSC.R dependencyCount: 68 Package: ppcseq Version: 1.6.0 Depends: R (>= 4.1.0) Imports: methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0), tibble, dplyr, magrittr, purrr, future, furrr, tidyr (>= 0.8.3.9000), lifecycle, ggplot2, foreach, tidybayes, edgeR, benchmarkme, parallel, rlang, stats, utils, graphics LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: knitr, testthat, BiocStyle, rmarkdown License: GPL-3 MD5sum: 26ba8f7f77732973d7cdcd1229cf2b97 NeedsCompilation: yes Title: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models Description: Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers. biocViews: RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] () Maintainer: Stefano Mangiola SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/ppcseq/issues git_url: https://git.bioconductor.org/packages/ppcseq git_branch: RELEASE_3_16 git_last_commit: 366ea46 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ppcseq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ppcseq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ppcseq_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ppcseq_1.6.0.tgz vignettes: vignettes/ppcseq/inst/doc/introduction.html vignetteTitles: Overview of the ppcseq package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppcseq/inst/doc/introduction.R dependencyCount: 102 Package: PPInfer Version: 1.24.2 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData Imports: httr, grDevices, graphics, stats, utils License: Artistic-2.0 Archs: x64 MD5sum: 98bd1ba94f206e55440b649211e88db9 NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: RELEASE_3_16 git_last_commit: 27b75e9 git_last_commit_date: 2022-11-15 Date/Publication: 2022-11-15 source.ver: src/contrib/PPInfer_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/PPInfer_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.2/PPInfer_1.24.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PPInfer_1.24.2.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 117 Package: pqsfinder Version: 2.14.1 Depends: R (>= 3.5.0), Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.78.0) Suggests: BiocStyle, knitr, rmarkdown, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE MD5sum: 122f1398e999e70312dd2c8d3f68b77c NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: RELEASE_3_16 git_last_commit: 2ef4735 git_last_commit_date: 2023-01-01 Date/Publication: 2023-01-01 source.ver: src/contrib/pqsfinder_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/pqsfinder_2.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/pqsfinder_2.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pqsfinder_2.14.1.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 21 Package: pram Version: 1.14.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0), GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: 24f0079cc585ffd54a5ab028a12633f7 NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq, BiologicalQuestion, GenePrediction, GenomeAnnotation, ResearchField, Transcriptomics Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: RELEASE_3_16 git_last_commit: 5eb27f7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pram_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pram_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pram_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pram_1.14.0.tgz vignettes: vignettes/pram/inst/doc/pram.html vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 47 Package: prebs Version: 1.38.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 MD5sum: c37527c055244dccf97b8fbd66130bca NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: RELEASE_3_16 git_last_commit: 542ffb3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/prebs_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/prebs_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/prebs_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/prebs_1.38.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R dependencyCount: 120 Package: preciseTAD Version: 1.8.0 Depends: R (>= 4.1) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel, gtools, rCGH Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: 0aa4053f4198be4b863ff0e9613d16e4 NeedsCompilation: no Title: preciseTAD: A machine learning framework for precise TAD boundary prediction Description: preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line. biocViews: Software, HiC, Sequencing, Clustering, Classification, FunctionalGenomics, FeatureExtraction Author: Spiro Stilianoudakis [aut], Mikhail Dozmorov [aut, cre] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/preciseTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/preciseTAD/issues git_url: https://git.bioconductor.org/packages/preciseTAD git_branch: RELEASE_3_16 git_last_commit: d9e548e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/preciseTAD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/preciseTAD_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/preciseTAD_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/preciseTAD_1.8.0.tgz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R suggestsMe: preciseTADhub dependencyCount: 183 Package: PREDA Version: 1.44.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 5859c6d37d24cd88d1dfa5232716db3e NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: RELEASE_3_16 git_last_commit: a69196b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PREDA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PREDA_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PREDA_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PREDA_1.44.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependsOnMe: PREDAsampledata dependencyCount: 58 Package: preprocessCore Version: 1.60.2 Imports: stats License: LGPL (>= 2) MD5sum: 803d83c635bc9d859a287ea8e714076a NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: RELEASE_3_16 git_last_commit: 71fbf1d git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/preprocessCore_1.60.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/preprocessCore_1.60.2.zip mac.binary.ver: bin/macosx/contrib/4.2/preprocessCore_1.60.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/preprocessCore_1.60.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus, SCATE importsMe: affy, BloodGen3Module, bnbc, cn.farms, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, ImmuneSpaceR, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, methylclock, mimager, minfi, MSPrep, MSstats, NormalyzerDE, oligo, PanomiR, PECA, PhosR, Pigengene, proBatch, qPLEXanalyzer, quantiseqr, sesame, soGGi, tidybulk, yarn, GSE13015, ADAPTS, bulkAnalyseR, cinaR, FARDEEP, HEMDAG, lilikoi, MetaIntegrator, MiDA, noise, noisyr, oncoPredict, RAMClustR, retriever, SMDIC, WGCNA suggestsMe: DAPAR, MsCoreUtils, multiClust, QFeatures, scp, splatter, wateRmelon, aroma.affymetrix, aroma.core, glycanr, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.16.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 798bacb57857e819725e01cee1adc693 NeedsCompilation: no Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues git_url: https://git.bioconductor.org/packages/primirTSS git_branch: RELEASE_3_16 git_last_commit: 62bfcf3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/primirTSS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/primirTSS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/primirTSS_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/primirTSS_1.16.0.tgz vignettes: vignettes/primirTSS/inst/doc/primirTSS.html vignetteTitles: primirTSS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R dependencyCount: 194 Package: PrInCE Version: 1.14.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 6180c413739ff4fe86825d9f5698044a NeedsCompilation: no Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: RELEASE_3_16 git_last_commit: 527645c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PrInCE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PrInCE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PrInCE_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PrInCE_1.14.0.tgz vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html vignetteTitles: Interactome reconstruction from co-elution data with PrInCE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R dependencyCount: 145 Package: proActiv Version: 1.8.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocParallel, data.table, dplyr, DESeq2, IRanges, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, ggplot2, gplots, graphics, methods, rlang, scales, S4Vectors, SummarizedExperiment, stats, tibble Suggests: testthat, rmarkdown, knitr, Rtsne, gridExtra License: MIT + file LICENSE MD5sum: 0c03533d936a898a6628a06ae53d9788 NeedsCompilation: no Title: Estimate Promoter Activity from RNA-Seq data Description: Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. proActiv also allows visualization of promoter activity across conditions. biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing, GeneRegulation, DifferentialSplicing, FunctionalGenomics, Epigenetics, Transcriptomics, Preprocessing Author: Deniz Demircioglu [aut] (), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proActiv git_branch: RELEASE_3_16 git_last_commit: dc9233c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/proActiv_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proActiv_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proActiv_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/proActiv_1.8.0.tgz vignettes: vignettes/proActiv/inst/doc/proActiv.html vignetteTitles: Identifying Active and Alternative Promoters from RNA-Seq data with proActiv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/proActiv/inst/doc/proActiv.R dependencyCount: 122 Package: proBAMr Version: 1.32.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 5f9cac892c5059c2d7561865953aaccb NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/proBAMr git_branch: RELEASE_3_16 git_last_commit: e64ea32 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/proBAMr_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proBAMr_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proBAMr_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/proBAMr_1.32.0.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R dependencyCount: 96 Package: proBatch Version: 1.14.0 Depends: R (>= 3.6) Imports: Biobase, corrplot, dplyr, data.table, ggfortify, ggplot2, grDevices, lazyeval, lubridate, magrittr, pheatmap, preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang, scales, stats, sva, tidyr, tibble, tools, utils, viridis, wesanderson, WGCNA Suggests: knitr, rmarkdown, devtools, ggpubr, gtable, gridExtra, roxygen2, testthat (>= 2.1.0), spelling License: GPL-3 MD5sum: be52699bbc2cd8ba94c17ca720cc1142 NeedsCompilation: no Title: Tools for Diagnostics and Corrections of Batch Effects in Proteomics Description: These tools facilitate batch effects analysis and correction in high-throughput experiments. It was developed primarily for mass-spectrometry proteomics (DIA/SWATH), but could also be applicable to most omic data with minor adaptations. The package contains functions for diagnostics (proteome/genome-wide and feature-level), correction (normalization and batch effects correction) and quality control. Non-linear fitting based approaches were also included to deal with complex, mass spectrometry-specific signal drifts. biocViews: BatchEffect, Normalization, Preprocessing, Software, MassSpectrometry,Proteomics, QualityControl Author: Jelena Cuklina , Chloe H. Lee , Patrick Pedrioli Maintainer: Chloe H. Lee URL: https://github.com/symbioticMe/proBatch VignetteBuilder: knitr BugReports: https://github.com/symbioticMe/proBatch/issues git_url: https://git.bioconductor.org/packages/proBatch git_branch: RELEASE_3_16 git_last_commit: f5abbc9 git_last_commit_date: 2022-11-01 Date/Publication: 2023-03-20 source.ver: src/contrib/proBatch_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proBatch_1.13.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proBatch_1.13.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/proBatch_1.14.0.tgz vignettes: vignettes/proBatch/inst/doc/proBatch.pdf vignetteTitles: proBatch package overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBatch/inst/doc/proBatch.R dependencyCount: 167 Package: PROcess Version: 1.74.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 781bdf154b8ddf4f5fb5a12d8669fe31 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: RELEASE_3_16 git_last_commit: a64808b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PROcess_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROcess_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROcess_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PROcess_1.74.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.26.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: b441b211f5eb973da6a69dbc1744dc5c NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: RELEASE_3_16 git_last_commit: ee50422 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/procoil_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/procoil_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/procoil_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/procoil_2.26.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 30 Package: proDA Version: 1.12.0 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown License: GPL-3 MD5sum: 01b5c145bf64cfacebbfec97b92d659e NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (), Simon Anders [ths] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: RELEASE_3_16 git_last_commit: 290b667 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/proDA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proDA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proDA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/proDA_1.12.0.tgz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R importsMe: MatrixQCvis suggestsMe: protti dependencyCount: 27 Package: proFIA Version: 1.23.0 Depends: R (>= 2.5.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, minpack.lm, BiocParallel, missForest, ropls Suggests: BiocGenerics, plasFIA, knitr, License: CeCILL MD5sum: 125bb66775833222118ebd1e40a1a0b7 NeedsCompilation: yes Title: Preprocessing of FIA-HRMS data Description: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced. biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing, PeakDetection, Proteomics Author: Alexis Delabriere and Etienne Thevenot. Maintainer: Alexis Delabriere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proFIA git_branch: master git_last_commit: c897664 git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/proFIA_1.23.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proFIA_1.23.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proFIA_1.23.0.tgz vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html vignetteTitles: processing FIA-HRMS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R dependsOnMe: plasFIA dependencyCount: 147 Package: profileplyr Version: 1.14.1 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, GenomeInfoDb, grDevices, rlang, tiff, Rsamtools Suggests: BiocStyle, testthat, knitr, rmarkdown, png, Cairo License: GPL (>= 3) Archs: x64 MD5sum: a96b7654cc18aa3ad5bfd22f44ba4ab4 NeedsCompilation: no Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: RELEASE_3_16 git_last_commit: 415d29e git_last_commit_date: 2023-01-18 Date/Publication: 2023-01-19 source.ver: src/contrib/profileplyr_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/profileplyr_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/profileplyr_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/profileplyr_1.14.1.tgz vignettes: vignettes/profileplyr/inst/doc/profileplyr.html vignetteTitles: Visualization and annotation of read signal over genomic ranges with profileplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R dependencyCount: 214 Package: profileScoreDist Version: 1.26.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE MD5sum: 1736d929cbb6f3257ba9a33d26c41b5e NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: RELEASE_3_16 git_last_commit: eead483 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/profileScoreDist_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/profileScoreDist_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/profileScoreDist_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/profileScoreDist_1.26.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 6 Package: progeny Version: 1.20.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra, decoupleR, reshape2 Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, rmarkdown, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE Archs: x64 MD5sum: 8baf46a4ce12dd912ee8a0b922c732c2 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: PROGENy is resource that leverages a large compendium of publicly available signaling perturbation experiments to yield a common core of pathway responsive genes for human and mouse. These, coupled with any statistical method, can be used to infer pathway activities from bulk or single-cell transcriptomics. biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [ctb] (), Christian H. Holland [ctb] (), Igor Bulanov [ctb], Aurélien Dugourd [cre, ctb] Maintainer: Aurélien Dugourd URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues git_url: https://git.bioconductor.org/packages/progeny git_branch: RELEASE_3_16 git_last_commit: b012c10 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/progeny_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/progeny_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/progeny_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/progeny_1.20.0.tgz vignettes: vignettes/progeny/inst/doc/progeny.html vignetteTitles: PROGENy pathway signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progeny.R importsMe: easier dependencyCount: 55 Package: projectR Version: 1.14.0 Imports: methods, cluster, stats, limma, CoGAPS, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, reshape2, viridis, scales, ggplot2 Suggests: BiocStyle, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap License: GPL (==2) MD5sum: 368115c6fc3d01d73938f1bf4788bfa5 NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: RELEASE_3_16 git_last_commit: bb6acba git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/projectR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/projectR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/projectR_1.14.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.pdf vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R importsMe: ATACCoGAPS dependencyCount: 97 Package: pRoloc Version: 1.38.2 Depends: R (>= 3.5), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10), methods, Rcpp (>= 0.10.3), BiocParallel Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.15.3), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape, magick License: GPL-2 MD5sum: 20588072a032f202e9f06090b392f12d NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: RELEASE_3_16 git_last_commit: f939f2e git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/pRoloc_1.38.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/pRoloc_1.38.2.zip mac.binary.ver: bin/macosx/contrib/4.2/pRoloc_1.38.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pRoloc_1.38.2.tgz vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.html, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html vignetteTitles: Using pRoloc for spatial proteomics data analysis, Machine learning techniques available in pRoloc, Bayesian spatial proteomics with pRoloc, Annotating spatial proteomics data, A transfer learning algorithm for spatial proteomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: bandle, pRolocGUI suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 221 Package: pRolocGUI Version: 2.8.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2, shinydashboardPlus (>= 2.0.0), colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, stats, grDevices, grid, BiocGenerics, shinydashboard Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown, testthat (>= 3.0.0) License: GPL-2 Archs: x64 MD5sum: 400bff2d87e9ba8dae97dcb9126469ec NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise spatial proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut, cre] (), Thomas Naake [aut], Laurent Gatto [aut] () Maintainer: Lisa Breckels URL: https://github.com/lgatto/pRolocGUI VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: RELEASE_3_16 git_last_commit: 0a9a69c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pRolocGUI_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pRolocGUI_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pRolocGUI_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pRolocGUI_2.8.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R dependencyCount: 235 Package: PROMISE Version: 1.50.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: 49ce20be97f0cc7638cd58cf2b6dba3b NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: RELEASE_3_16 git_last_commit: cfa0adf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PROMISE_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROMISE_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROMISE_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PROMISE_1.50.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 51 Package: PROPER Version: 1.30.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq2,DSS,knitr License: GPL MD5sum: 7f36d105300d6e3604e654eb41eb8bce NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: RELEASE_3_16 git_last_commit: bf62862 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PROPER_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROPER_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROPER_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PROPER_1.30.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R dependencyCount: 11 Package: PROPS Version: 1.20.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 96a01cb749fdbc3fc36266ab9532263b NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: RELEASE_3_16 git_last_commit: c19477b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PROPS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PROPS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PROPS_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PROPS_1.20.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 78 Package: Prostar Version: 1.30.7 Depends: R (>= 4.2.0) Imports: DAPAR (>= 1.30.5), DAPARdata (>= 1.28.0), rhandsontable, data.table, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, shinythemes, later, shinycssloaders, future, promises, shinyjqui, tibble, ggplot2, gplots, shinyjs, vioplot Suggests: BiocStyle, BiocManager, testthat, shinyTree, knitr, colourpicker, gtools, XML, R.utils, RColorBrewer, DT, shinyWidgets, sass, rclipboard License: Artistic-2.0 MD5sum: 7cd4f1914a062176bd49cb06a3ac8434 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, Software, GUI Author: Thomas Burger [aut], Florence Combes [aut], Samuel Wieczorek [cre, aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/prostarproteomics/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_16 git_last_commit: 11efef3 git_last_commit_date: 2023-02-22 Date/Publication: 2023-02-22 source.ver: src/contrib/Prostar_1.30.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/Prostar_1.30.7.zip mac.binary.ver: bin/macosx/contrib/4.2/Prostar_1.30.7.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Prostar_1.30.7.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar User Manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R dependencyCount: 153 Package: proteasy Version: 1.0.0 Depends: R (>= 4.2.0) Imports: data.table, stringr, ensembldb, AnnotationFilter, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v79, Rcpi, methods, utils Suggests: BiocStyle, knitr, rmarkdown, igraph, ComplexHeatmap, viridis, License: GPL-3 MD5sum: 17acfcc126e9ee9181c801bc5afab88e NeedsCompilation: no Title: Protease Mapping Description: Retrieval of experimentally derived protease- and cleavage data derived from the MEROPS database. Proteasy contains functions for mapping peptide termini to known sites where a protease cleaves. This package also makes it possible to quickly look up known substrates based on a list of (potential) proteases, or vice versa - look up proteases based on a list of substrates. biocViews: Proteomics, BiomedicalInformatics, FunctionalGenomics Author: Martin Rydén [aut, cre] () Maintainer: Martin Rydén URL: https://github.com/martinry/proteasy VignetteBuilder: knitr BugReports: https://github.com/martinry/proteasy/issues git_url: https://git.bioconductor.org/packages/proteasy git_branch: RELEASE_3_16 git_last_commit: 2a71339 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/proteasy_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proteasy_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proteasy_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/proteasy_1.0.0.tgz vignettes: vignettes/proteasy/inst/doc/proteasy.html vignetteTitles: Using proteasy to Retrieve and Analyze Protease Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteasy/inst/doc/proteasy.R dependencyCount: 116 Package: proteinProfiles Version: 1.38.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: fd0032fb672c3dcd381b9fb59166d4a2 NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: RELEASE_3_16 git_last_commit: 4e5216d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/proteinProfiles_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/proteinProfiles_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/proteinProfiles_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/proteinProfiles_1.38.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteoDisco Version: 1.4.0 Depends: R (>= 4.1.0), Imports: BiocGenerics (>= 0.38.0), BiocParallel (>= 1.26.0), Biostrings (>= 2.60.1), checkmate (>= 2.0.0), cleaver (>= 1.30.0), dplyr (>= 1.0.6), GenomeInfoDb (>= 1.28.0), GenomicFeatures (>= 1.44.0), GenomicRanges (>= 1.44.0), IRanges (>= 2.26.0), methods (>= 4.1.0), ParallelLogger (>= 2.0.1), plyr (>= 1.8.6), rlang (>= 0.4.11), S4Vectors (>= 0.30.0), tibble (>= 3.1.2), tidyr (>= 1.1.3), VariantAnnotation (>= 1.36.0), XVector (>= 0.32.0), Suggests: AnnotationDbi (>= 1.54.1), BSgenome (>= 1.60.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.3), BiocStyle (>= 2.20.1), DelayedArray (>= 0.18.0), devtools (>= 2.4.2), knitr (>= 1.33), matrixStats (>= 0.59.0), markdown (>= 1.1), org.Hs.eg.db (>= 3.13.0), purrr (>= 0.3.4), RCurl (>= 1.98.1.3), readr (>= 1.4.0), ggplot2 (>= 3.3.5), rmarkdown (>= 2.9), rtracklayer (>= 1.52.0), seqinr (>= 4.2.8), stringr (>= 1.4.0), reshape2 (>= 1.4.4), scales (>= 1.1.1), testthat (>= 3.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2) License: GPL-3 MD5sum: 1cf5531210ad22874467ec57e6dab4a8 NeedsCompilation: no Title: Generation of customized protein variant databases from genomic variants, splice-junctions and manual sequences Description: ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings. biocViews: Software, Proteomics, RNASeq, SNP, Sequencing, VariantAnnotation, DataImport Author: Job van Riet [cre], Wesley van de Geer [aut], Harmen van de Werken [ths] Maintainer: Job van Riet URL: https://github.com/ErasmusMC-CCBC/ProteoDisco VignetteBuilder: knitr BugReports: https://github.com/ErasmusMC-CCBC/ProteoDisco/issues git_url: https://git.bioconductor.org/packages/ProteoDisco git_branch: RELEASE_3_16 git_last_commit: 9c301d7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ProteoDisco_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ProteoDisco_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ProteoDisco_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ProteoDisco_1.4.0.tgz vignettes: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.html vignetteTitles: Overview_Proteodisco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoDisco/inst/doc/Overview_ProteoDisco.R dependencyCount: 104 Package: ProteoMM Version: 1.16.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: ab59f87cb72ba500bc72a8ef9d630bb7 NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: RELEASE_3_16 git_last_commit: fbb1390 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ProteoMM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ProteoMM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ProteoMM_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ProteoMM_1.16.0.tgz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R suggestsMe: mi4p dependencyCount: 92 Package: protGear Version: 1.2.0 Depends: R (>= 4.2), dplyr (>= 0.8.0) , limma (>= 3.40.2) ,vsn (>= 3.54.0) Imports: magrittr (>= 1.5) , stats (>= 3.6) , ggplot2 (>= 3.3.0) , tidyr (>= 1.1.3) , data.table (>= 1.14.0), ggpubr (>= 0.4.0), gtools (>= 3.8.2) , tibble (>= 3.1.0) , rmarkdown (>= 2.9) , knitr (>= 1.33), utils (>= 3.6), genefilter (>= 1.74.0), readr (>= 2.0.1) , Biobase (>= 2.52.0), plyr (>= 1.8.6) , Kendall (>= 2.2) , shiny (>= 1.0.0) , purrr (>= 0.3.4), plotly (>= 4.9.0) , MASS (>= 7.3) , htmltools (>= 0.4.0) , flexdashboard (>= 0.5.2) , shinydashboard (>= 0.7.1) , kableExtra (>= 1.3.4), GGally (>= 2.1.2) , pheatmap (>= 1.0.12) , grid(>= 4.1.1), styler (>= 1.6.1) , factoextra (>= 1.0.7) ,FactoMineR (>= 2.4) , rlang (>= 0.4.11), remotes (>= 2.4.0) Suggests: gridExtra (>= 2.3), png (>= 0.1-7) , magick (>= 2.7.3) , ggplotify (>= 0.1.0) , scales (>= 1.1.1) , shinythemes (>= 1.2.0) , shinyjs (>= 2.0.0) , shinyWidgets (>= 0.6.2) , shinycssloaders (>= 1.0.0) , shinyalert (>= 3.0.0) , shinyFiles (>= 0.9.1) , shinyFeedback (>= 0.3.0) License: GPL-3 MD5sum: 1056efb679e5f6629e0f4bbe4bf6752b NeedsCompilation: no Title: Protein Micro Array Data Management and Interactive Visualization Description: A generic three-step pre-processing package for protein microarray data. This package contains different data pre-processing procedures to allow comparison of their performance.These steps are background correction, the coefficient of variation (CV) based filtering, batch correction and normalization. biocViews: Microarray, OneChannel, Preprocessing , BiomedicalInformatics , Proteomics , BatchEffect, Normalization , Bayesian, Clustering, Regression,SystemsBiology, ImmunoOncology Author: Kennedy Mwai [cre, aut], James Mburu [aut], Jacqueline Waeni [ctb] Maintainer: Kennedy Mwai URL: https://github.com/Keniajin/protGear VignetteBuilder: knitr BugReports: https://github.com/Keniajin/protGear/issues git_url: https://git.bioconductor.org/packages/protGear git_branch: RELEASE_3_16 git_last_commit: 1d2a370 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/protGear_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/protGear_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/protGear_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/protGear_1.2.0.tgz vignettes: vignettes/protGear/inst/doc/vignette.html vignetteTitles: protGear hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/protGear/inst/doc/vignette.R dependencyCount: 200 Package: ProtGenerics Version: 1.30.0 Depends: methods Suggests: testthat License: Artistic-2.0 MD5sum: 0401af6e41ec629a173a1e8dacb12823 NeedsCompilation: no Title: Generic infrastructure for Bioconductor mass spectrometry packages Description: S4 generic functions and classes needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto , Johannes Rainer Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: RELEASE_3_16 git_last_commit: fcd566b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ProtGenerics_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ProtGenerics_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ProtGenerics_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ProtGenerics_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MsExperiment, MSnbase, Spectra, topdownr importsMe: CompoundDb, ensembldb, matter, MetaboAnnotation, MsBackendMassbank, MsFeatures, MSnID, mzID, mzR, PSMatch, QFeatures, xcms dependencyCount: 1 Package: PSEA Version: 1.32.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: dc1274dd2db6fce4075de1ee4a52e6da NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/PSEA git_branch: RELEASE_3_16 git_last_commit: 6b3f2e9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PSEA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PSEA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PSEA_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PSEA_1.32.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf, vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods paper, PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R, vignettes/PSEA/inst/doc/PSEA.R dependencyCount: 8 Package: psichomics Version: 1.24.0 Depends: R (>= 4.0), shiny (>= 1.7.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, purrr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE MD5sum: e0b5dd013455ac8afc6b30b9dd965645 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (), Nuno Luís Barbosa-Morais [aut, led, ths] (), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_16 git_last_commit: ff95655 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/psichomics_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/psichomics_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/psichomics_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/psichomics_1.24.0.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading user-provided data, Case study: visual interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 204 Package: PSMatch Version: 1.2.0 Depends: S4Vectors Imports: utils, stats, igraph, methods, Matrix, BiocParallel, BiocGenerics, ProtGenerics (>= 1.27.1), QFeatures, MsCoreUtils Suggests: msdata, rpx, mzID, mzR, Spectra, SummarizedExperiment, BiocStyle, rmarkdown, knitr, factoextra, testthat License: Artistic-2.0 Archs: x64 MD5sum: d6129bf161baf69afb4dad71ca3a965b NeedsCompilation: no Title: Handling and Managing Peptide Spectrum Matches Description: The PSMatch package helps proteomics practitioners to load, handle and manage Peptide Spectrum Matches. It provides functions to model peptide-protein relations as adjacency matrices and connected components, visualise these as graphs and make informed decision about shared peptide filtering. The package also provides functions to calculate and visualise MS2 fragment ions. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto [aut, cre] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Samuel Wieczorek [ctb], Thomas Burger [ctb] Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/PSM VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/PSM/issues git_url: https://git.bioconductor.org/packages/PSMatch git_branch: RELEASE_3_16 git_last_commit: 13c2042 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PSMatch_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PSMatch_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PSMatch_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PSMatch_1.2.0.tgz vignettes: vignettes/PSMatch/inst/doc/AdjacencyMatrix.html, vignettes/PSMatch/inst/doc/Fragments.html, vignettes/PSMatch/inst/doc/PSM.html vignetteTitles: Understanding protein groups with adjacency matrices, MS2 fragment ions, Working with PSM data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSMatch/inst/doc/AdjacencyMatrix.R, vignettes/PSMatch/inst/doc/Fragments.R, vignettes/PSMatch/inst/doc/PSM.R dependencyCount: 113 Package: psygenet2r Version: 1.30.0 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db Suggests: testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 7dcb0f99dcb2623989bccdec5afa4274 NeedsCompilation: no Title: psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders Description: Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data. biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure, DataImport, DataRepresentation Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer [aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut] Maintainer: Alba Gutierrez-Sacristan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/psygenet2r git_branch: RELEASE_3_16 git_last_commit: 3a53458 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/psygenet2r_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/psygenet2r_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/psygenet2r_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/psygenet2r_1.30.0.tgz vignettes: vignettes/psygenet2r/inst/doc/case_study.html, vignettes/psygenet2r/inst/doc/general_overview.html vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder, psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psygenet2r/inst/doc/case_study.R, vignettes/psygenet2r/inst/doc/general_overview.R dependencyCount: 102 Package: ptairMS Version: 1.6.0 Imports: Biobase, bit64, chron, data.table, doParallel, DT, enviPat, foreach, ggplot2, graphics, grDevices, ggpubr, gridExtra, Hmisc, methods, minpack.lm, MSnbase, parallel, plotly, rhdf5, rlang, Rcpp, shiny, shinyscreenshot, signal, scales, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), ptairData, ropls License: GPL-3 MD5sum: b1e96e522b44dbb2f4270e8a93d68288 NeedsCompilation: yes Title: Pre-processing PTR-TOF-MS Data Description: This package implements a suite of methods to preprocess data from PTR-TOF-MS instruments (HDF5 format) and generates the 'sample by features' table of peak intensities in addition to the sample and feature metadata (as a single ExpressionSet object for subsequent statistical analysis). This package also permit usefull tools for cohorts management as analyzing data progressively, visualization tools and quality control. The steps include calibration, expiration detection, peak detection and quantification, feature alignment, missing value imputation and feature annotation. Applications to exhaled air and cell culture in headspace are described in the vignettes and examples. This package was used for data analysis of Gassin Delyle study on adults undergoing invasive mechanical ventilation in the intensive care unit due to severe COVID-19 or non-COVID-19 acute respiratory distress syndrome (ARDS), and permit to identfy four potentiel biomarquers of the infection. biocViews: Software, MassSpectrometry, Preprocessing, Metabolomics, PeakDetection, Alignment Author: camille Roquencourt [aut, cre] Maintainer: camille Roquencourt VignetteBuilder: knitr BugReports: https://github.com/camilleroquencourt/ptairMS/issues git_url: https://git.bioconductor.org/packages/ptairMS git_branch: RELEASE_3_16 git_last_commit: 48f0986 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ptairMS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ptairMS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ptairMS_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ptairMS_1.6.0.tgz vignettes: vignettes/ptairMS/inst/doc/ptairMS.html vignetteTitles: ptaiMS: Processing and analysis of PTR-TOF-MS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ptairMS/inst/doc/ptairMS.R dependencyCount: 180 Package: puma Version: 3.40.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL Archs: x64 MD5sum: e76d81a75d32dc58facd4040fd4a9402 NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: RELEASE_3_16 git_last_commit: 2bdf0aa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/puma_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/puma_3.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/puma_3.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/puma_3.40.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 56 Package: PureCN Version: 2.4.0 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biobase, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, mclust, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, R.utils, TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr, optparse, org.Hs.eg.db, jsonlite, markdown, rmarkdown, testthat Enhances: genomicsdb (>= 0.0.3) License: Artistic-2.0 Archs: x64 MD5sum: e27dab92290f8bc87a24f45d433893e3 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PureCN git_branch: RELEASE_3_16 git_last_commit: 639f2a8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PureCN_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PureCN_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PureCN_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PureCN_2.4.0.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 120 Package: pvac Version: 1.46.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: a60782744069194b06fca1ad7a0cb0e6 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: RELEASE_3_16 git_last_commit: e1455d6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pvac_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pvac_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pvac_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pvac_1.46.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 12 Package: pvca Version: 1.38.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 3e0a756f9bd0beab58434428b1b8f400 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: RELEASE_3_16 git_last_commit: 037420b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pvca_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pvca_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pvca_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pvca_1.38.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: proBatch, ExpressionNormalizationWorkflow, statVisual dependencyCount: 70 Package: Pviz Version: 1.32.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: 0e38d5e45932616453d5a646f08912eb NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: RELEASE_3_16 git_last_commit: ea953c5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Pviz_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Pviz_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Pviz_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Pviz_1.32.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R suggestsMe: pepStat dependencyCount: 153 Package: PWMEnrich Version: 4.34.0 Depends: R (>= 3.5.0), methods, BiocGenerics, Biostrings Imports: grid, seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 1c95f91b66ee9a37e25eb425ec3753a5 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: RELEASE_3_16 git_last_commit: 01af84f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/PWMEnrich_4.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/PWMEnrich_4.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/PWMEnrich_4.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/PWMEnrich_4.34.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R dependsOnMe: PWMEnrich.Dmelanogaster.background, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background suggestsMe: rTRM dependencyCount: 23 Package: pwOmics Version: 1.30.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges, graph, grDevices, stats, utils Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: cb288b27c48b06fd430bc6f2bffbfb4d NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and phosphoproteins. A separate downstream analysis of phosphoproteomic data including pathway identification, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and potential proteomic regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. In addition, it provides functions to identify individual signaling axes based on data integration. biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling Author: Astrid Wachter Maintainer: Maren Sitte git_url: https://git.bioconductor.org/packages/pwOmics git_branch: RELEASE_3_16 git_last_commit: f59da6c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pwOmics_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pwOmics_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pwOmics_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pwOmics_1.30.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 122 Package: pwrEWAS Version: 1.12.0 Depends: shinyBS, foreach Imports: doParallel, abind, truncnorm, CpGassoc, shiny, ggplot2, parallel, shinyWidgets, BiocManager, doSNOW, limma, genefilter, stats, grDevices, methods, utils, graphics, pwrEWAS.data Suggests: knitr, RUnit, BiocGenerics, rmarkdown License: Artistic-2.0 MD5sum: 1dba43c7af73d8ff0c270b3c7a4380d9 NeedsCompilation: no Title: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS) Description: pwrEWAS is a user-friendly tool to assists researchers in the design and planning of EWAS to help circumvent under- and overpowered studies. biocViews: DNAMethylation, Microarray, DifferentialMethylation, TissueMicroarray Author: Stefan Graw Maintainer: Stefan Graw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pwrEWAS git_branch: RELEASE_3_16 git_last_commit: 64c11e9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/pwrEWAS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/pwrEWAS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/pwrEWAS_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/pwrEWAS_1.12.0.tgz vignettes: vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf vignetteTitles: pwrEWAS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwrEWAS/inst/doc/pwrEWAS.R dependencyCount: 133 Package: qckitfastq Version: 1.14.0 Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table, reshape2, grDevices, graphics, stats, utils, Rcpp, rlang, RSeqAn LinkingTo: Rcpp, RSeqAn Suggests: knitr, rmarkdown, kableExtra, testthat License: Artistic-2.0 MD5sum: 8acd24f1897e667998346af3b2234562 NeedsCompilation: yes Title: FASTQ Quality Control Description: Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers. biocViews: Software,QualityControl,Sequencing Author: Wenyue Xing [aut], August Guang [aut, cre] Maintainer: August Guang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qckitfastq git_branch: RELEASE_3_16 git_last_commit: 49d700a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qckitfastq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qckitfastq_1.13.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qckitfastq_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qckitfastq_1.14.0.tgz vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf vignetteTitles: Quality control analysis and visualization using qckitfastq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R dependencyCount: 48 Package: qcmetrics Version: 1.36.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, mzR, BiocStyle, rmarkdown License: GPL-2 Archs: x64 MD5sum: f626ab88d5050d30c030ededbcac0029 NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://lgatto.github.io/qcmetrics/articles/qcmetrics.html VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: RELEASE_3_16 git_last_commit: 865dc77 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qcmetrics_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qcmetrics_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qcmetrics_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qcmetrics_1.36.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.html vignetteTitles: Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R importsMe: MSstatsQC dependencyCount: 19 Package: QDNAseq Version: 1.34.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.60.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future.apply (>= 1.8.1) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), future (>= 1.22.1), parallelly (>= 1.28.1), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: b4ee187a398ad47ca39997dac68121bf NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: RELEASE_3_16 git_last_commit: 727c1cb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QDNAseq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QDNAseq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QDNAseq_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QDNAseq_1.34.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10 importsMe: ACE, biscuiteer, HiCcompare dependencyCount: 49 Package: QFeatures Version: 1.8.0 Depends: R (>= 4.0), MultiAssayExperiment Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.27.1), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.7.2), igraph, plotly Suggests: SingleCellExperiment, Matrix, HDF5Array, msdata, ggplot2, gplots, dplyr, limma, magrittr, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm, ComplexHeatmap License: Artistic-2.0 MD5sum: 488137c8d538bdf55926ad143b2be1c8 NeedsCompilation: no Title: Quantitative features for mass spectrometry data Description: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (), Christophe Vanderaa [aut] () Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/QFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/QFeatures/issues git_url: https://git.bioconductor.org/packages/QFeatures git_branch: RELEASE_3_16 git_last_commit: 5b151d2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QFeatures_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QFeatures_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QFeatures_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QFeatures_1.8.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html, vignettes/QFeatures/inst/doc/Visualization.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data, Data visualization from a QFeatures object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R, vignettes/QFeatures/inst/doc/Visualization.R dependsOnMe: msqrob2, scp, scpdata importsMe: MetaboAnnotation, MsExperiment, PSMatch dependencyCount: 103 Package: qmtools Version: 1.2.0 Depends: R (>= 4.2.0), SummarizedExperiment Imports: rlang, ggplot2, patchwork, heatmaply, methods, MsCoreUtils, stats, igraph, VIM, scales, grDevices, graphics Suggests: limma, Rtsne, missForest, vsn, pcaMethods, pls, MsFeatures, impute, imputeLCMD, nlme, testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: c38ff3732c74bf258aaf7c8309419bd7 NeedsCompilation: no Title: Quantitative Metabolomics Data Processing Tools Description: The qmtools (quantitative metabolomics tools) package provides basic tools for processing quantitative metabolomics data with the standard SummarizedExperiment class. This includes functions for imputation, normalization, feature filtering, feature clustering, dimension-reduction, and visualization to help users prepare data for statistical analysis. Several functions in this package could also be used in other types of omics data. biocViews: Metabolomics, Preprocessing, Normalization, DimensionReduction, MassSpectrometry Author: Jaehyun Joo [aut, cre], Blanca Himes [aut] Maintainer: Jaehyun Joo URL: https://github.com/HimesGroup/qmtools VignetteBuilder: knitr BugReports: https://github.com/HimesGroup/qmtools/issues git_url: https://git.bioconductor.org/packages/qmtools git_branch: RELEASE_3_16 git_last_commit: e3660f9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qmtools_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qmtools_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qmtools_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qmtools_1.2.0.tgz vignettes: vignettes/qmtools/inst/doc/qmtools.html vignetteTitles: Quantitative metabolomics data processing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qmtools/inst/doc/qmtools.R dependencyCount: 162 Package: qpcrNorm Version: 1.56.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: cba6ab7638108ac878a217e8d313c453 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: RELEASE_3_16 git_last_commit: ba8802e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qpcrNorm_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qpcrNorm_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qpcrNorm_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qpcrNorm_1.56.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R dependencyCount: 13 Package: qpgraph Version: 2.32.2 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.5-0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) MD5sum: 4f4f38be02f23d4fd529e350a16fcb72 NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: RELEASE_3_16 git_last_commit: 813e677 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/qpgraph_2.32.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/qpgraph_2.32.2.zip mac.binary.ver: bin/macosx/contrib/4.2/qpgraph_2.32.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qpgraph_2.32.2.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper, simPATHy, topologyGSA dependencyCount: 102 Package: qPLEXanalyzer Version: 1.16.1 Depends: R (>= 4.0), Biobase, MSnbase Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0), ggdendro, ggplot2, graphics, grDevices, IRanges, limma, magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect, utils Suggests: gridExtra, knitr, qPLEXdata, rmarkdown, testthat, UniProt.ws, vdiffr License: GPL-2 Archs: x64 MD5sum: 8759c9727fe9a0c9fc645f692d057763 NeedsCompilation: no Title: Tools for quantitative proteomics data analysis Description: Tools for TMT based quantitative proteomics data analysis. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: RELEASE_3_16 git_last_commit: 0732f38 git_last_commit_date: 2023-03-14 Date/Publication: 2023-03-14 source.ver: src/contrib/qPLEXanalyzer_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/qPLEXanalyzer_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/qPLEXanalyzer_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qPLEXanalyzer_1.16.1.tgz vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html vignetteTitles: qPLEXanalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R dependsOnMe: qPLEXdata dependencyCount: 102 Package: qrqc Version: 1.52.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rhtslib (>= 1.15.3) License: GPL (>=2) MD5sum: 5160a5cd4b0aab743706c0683f3cc969 NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/qrqc git_branch: RELEASE_3_16 git_last_commit: a21e0d0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qrqc_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qrqc_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qrqc_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qrqc_1.52.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R dependencyCount: 163 Package: qsea Version: 1.24.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager, MASS License: GPL (>=2) Archs: x64 MD5sum: 0a604f35df80b9ed7eb25092382a51be NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard, Lukas Chavez, Ralf Herwig Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: RELEASE_3_16 git_last_commit: 662ea99 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qsea_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qsea_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qsea_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qsea_1.24.0.tgz vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R dependencyCount: 52 Package: qsmooth Version: 1.14.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics, Hmisc Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: CC BY 4.0 Archs: x64 MD5sum: fd62456b97bd33c2f43d44b6a7859b61 NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (), Kwame Okrah [aut], Koen Van den Berge [ctb], Hector Corrada Bravo [aut] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: RELEASE_3_16 git_last_commit: 9d3fa0b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qsmooth_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qsmooth_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qsmooth_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qsmooth_1.14.0.tgz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R dependencyCount: 126 Package: QSutils Version: 1.16.0 Depends: R (>= 3.5), Biostrings, BiocGenerics,methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: file LICENSE MD5sum: 209a73c83f1e1d14e3cb5a37ca8a1ff2 NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo [cre, aut] (), Josep Gregori i Font [aut] () Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: RELEASE_3_16 git_last_commit: 5eb0dc4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QSutils_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QSutils_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QSutils_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QSutils_1.16.0.tgz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R dependencyCount: 27 Package: qsvaR Version: 1.2.0 Depends: R (>= 4.2), SummarizedExperiment Imports: sva, stats, ggplot2, methods Suggests: BiocFileCache, BiocStyle, covr, knitr, limma, RefManageR, rmarkdown, sessioninfo, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 9602d179c2f581069f9a4b087970efa7 NeedsCompilation: no Title: Generate Quality Surrogate Variable Analysis for Degradation Correction Description: The qsvaR package contains functions for removing the effect of degration in rna-seq data from postmortem brain tissue. The package is equipped to help users generate principal components associated with degradation. The components can be used in differential expression analysis to remove the effects of degradation. biocViews: Software, WorkflowStep, Normalization, BiologicalQuestion, DifferentialExpression, Sequencing, Coverage Author: Joshua Stolz [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Joshua Stolz URL: https://github.com/LieberInstitute/qsvaR VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/qsvaR git_url: https://git.bioconductor.org/packages/qsvaR git_branch: RELEASE_3_16 git_last_commit: cb5aee3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qsvaR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qsvaR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qsvaR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qsvaR_1.2.0.tgz vignettes: vignettes/qsvaR/inst/doc/Intro_qsvaR.html vignetteTitles: Introduction to qsvaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsvaR/inst/doc/Intro_qsvaR.R dependencyCount: 93 Package: Qtlizer Version: 1.12.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 6d96f4069a4810bcd6075318c81f5859 NeedsCompilation: no Title: Comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre] (), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: RELEASE_3_16 git_last_commit: d158af6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Qtlizer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Qtlizer_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Qtlizer_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Qtlizer_1.12.0.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 25 Package: quantiseqr Version: 1.6.0 Depends: R (>= 4.1.0) Imports: Biobase, limSolve, MASS, methods, preprocessCore, stats, SummarizedExperiment, ggplot2, tidyr, rlang, utils Suggests: AnnotationDbi, BiocStyle, dplyr, ExperimentHub, GEOquery, knitr, macrophage, org.Hs.eg.db, reshape2, rmarkdown, testthat, tibble License: GPL-3 MD5sum: a206d54e9604ca8abf52262aaf2ace76 NeedsCompilation: no Title: Quantification of the Tumor Immune contexture from RNA-seq data Description: This package provides a streamlined workflow for the quanTIseq method, developed to perform the quantification of the Tumor Immune contexture from RNA-seq data. The quantification is performed against the TIL10 signature (dissecting the contributions of ten immune cell types), carefully crafted from a collection of human RNA-seq samples. The TIL10 signature has been extensively validated using simulated, flow cytometry, and immunohistochemistry data. biocViews: GeneExpression, Software, Transcription, Transcriptomics, Sequencing, Microarray, Visualization, Annotation, ImmunoOncology, FeatureExtraction, Classification, StatisticalMethod, ExperimentHubSoftware, FlowCytometry Author: Federico Marini [aut, cre] (), Francesca Finotello [aut] () Maintainer: Federico Marini VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantiseqr git_branch: RELEASE_3_16 git_last_commit: 5fe1351 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/quantiseqr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/quantiseqr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/quantiseqr_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/quantiseqr_1.6.0.tgz vignettes: vignettes/quantiseqr/inst/doc/using_quantiseqr.html vignetteTitles: Using quantiseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantiseqr/inst/doc/using_quantiseqr.R importsMe: easier dependencyCount: 64 Package: quantro Version: 1.32.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: rmarkdown, knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: 5f425a675de0aa7eb4fdee07957d45fb NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: RELEASE_3_16 git_last_commit: 0c70b78 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/quantro_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/quantro_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/quantro_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/quantro_1.32.0.tgz vignettes: vignettes/quantro/inst/doc/quantro.html vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro.R importsMe: yarn suggestsMe: qsmooth dependencyCount: 151 Package: quantsmooth Version: 1.64.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: a3b72d73ec34992c96358e6a40c1f23a NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: RELEASE_3_16 git_last_commit: 4a0a384 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/quantsmooth_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/quantsmooth_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/quantsmooth_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/quantsmooth_1.64.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 14 Package: QuartPAC Version: 1.30.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 458eee6216190923282938c4f7efe2ad NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures. Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: RELEASE_3_16 git_last_commit: 2606496 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QuartPAC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QuartPAC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QuartPAC_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QuartPAC_1.30.0.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R dependencyCount: 44 Package: QuasR Version: 1.38.0 Depends: R (>= 4.1), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, stats, tools, BiocGenerics, S4Vectors, IRanges, Biobase, Biostrings, BSgenome, Rsamtools (>= 2.13.1), GenomicFeatures, ShortRead, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, AnnotationDbi LinkingTo: Rhtslib (>= 1.99.1) Suggests: Gviz, BiocStyle, GenomicAlignments, Rhisat2, knitr, rmarkdown, covr, testthat License: GPL-2 MD5sum: cb3295e2142d0403ebed9b9dea91fb34 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch [aut], Charlotte Soneson [aut] (), Dimos Gaidatzis [aut], Michael Stadler [aut, cre] () Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuasR git_branch: RELEASE_3_16 git_last_commit: 0d658f7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QuasR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QuasR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QuasR_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QuasR_1.38.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R importsMe: SingleMoleculeFootprinting suggestsMe: eisaR dependencyCount: 110 Package: QuaternaryProd Version: 1.32.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) Archs: x64 MD5sum: d61d33fad8042df683fe0cd0ead521f4 NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: RELEASE_3_16 git_last_commit: 45f5b82 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QuaternaryProd_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QuaternaryProd_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QuaternaryProd_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QuaternaryProd_1.32.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 23 Package: QUBIC Version: 1.26.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE Archs: x64 MD5sum: 84ade7f60f97118609cdd8c9e5616c1b NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: RELEASE_3_16 git_last_commit: bf93aae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/QUBIC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/QUBIC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/QUBIC_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/QUBIC_1.26.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R importsMe: mosbi suggestsMe: runibic dependencyCount: 53 Package: qusage Version: 2.32.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) Archs: x64 MD5sum: 10d8ea7b83a72ec1698c272e9c689d7c NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: RELEASE_3_16 git_last_commit: 355ee8d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qusage_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qusage_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qusage_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qusage_2.32.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R importsMe: mExplorer suggestsMe: SigCheck dependencyCount: 16 Package: qvalue Version: 2.30.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: 8df957d24ededc50adfffe59a5cf39ed NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: RELEASE_3_16 git_last_commit: e8a4c22 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/qvalue_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/qvalue_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/qvalue_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/qvalue_2.30.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, DEGseq, DrugVsDisease, r3Cseq, webbioc, BonEV, cp4p, isva importsMe: Anaquin, anota, clusterProfiler, CTSV, derfinder, DOSE, edge, epihet, erccdashboard, EventPointer, FindIT2, fishpond, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RiboDiPA, RNAsense, RolDE, SDAMS, sights, signatureSearch, subSeq, synapter, trigger, vsclust, webbioc, IHWpaper, AEenrich, armada, cancerGI, fdrDiscreteNull, glmmSeq, groupedSurv, HDMT, jaccard, jackstraw, medScan, MOCHA, NBPSeq, SeqFeatR, ssizeRNA suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark, swfdr, RNAinteractMAPK, BootstrapQTL, CpGassoc, dartR, DGEobj.utils, easylabel, familiar, mutoss, Rediscover, seqgendiff, wrMisc dependencyCount: 41 Package: R3CPET Version: 1.30.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) MD5sum: 50c53785d011680d0b7bad11e7d46bfe NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: RELEASE_3_16 git_last_commit: 66ae293 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/R3CPET_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/R3CPET_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/R3CPET_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/R3CPET_1.30.0.tgz vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R dependencyCount: 161 Package: r3Cseq Version: 1.44.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 Archs: x64 MD5sum: 8270b483b87c8a464227fb7eecea6d29 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: RELEASE_3_16 git_last_commit: d21d569 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/r3Cseq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/r3Cseq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/r3Cseq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/r3Cseq_1.44.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 94 Package: R453Plus1Toolbox Version: 1.48.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 MD5sum: cebbe93dd4b6321fee66e4fd9dd68283 NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: RELEASE_3_16 git_last_commit: 93bf3f8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/R453Plus1Toolbox_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/R453Plus1Toolbox_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/R453Plus1Toolbox_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/R453Plus1Toolbox_1.48.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R dependencyCount: 111 Package: R4RNA Version: 1.26.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 Archs: x64 MD5sum: c92ae6b2df10b48aa1fd0d2e55d8c0a9 NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: RELEASE_3_16 git_last_commit: d1c764b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/R4RNA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/R4RNA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/R4RNA_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/R4RNA_1.26.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R importsMe: ggmsa suggestsMe: rfaRm dependencyCount: 18 Package: RadioGx Version: 2.2.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, data.table, S4Vectors, Biobase, parallel, BiocParallel, RColorBrewer, caTools, magicaxis, methods, reshape2, scales, grDevices, graphics, stats, utils, assertthat, matrixStats, downloader Suggests: rmarkdown, BiocStyle, knitr, pander, markdown License: GPL-3 MD5sum: 0a791da693d9633d29a2667c10bc11c3 NeedsCompilation: no Title: Analysis of Large-Scale Radio-Genomic Data Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) . biocViews: Software, Pharmacogenetics, QualityControl, Survival, Pharmacogenomics, Classification Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: RELEASE_3_16 git_last_commit: 5f34ff9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RadioGx_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RadioGx_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RadioGx_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RadioGx_2.2.0.tgz vignettes: vignettes/RadioGx/inst/doc/RadioGx.html vignetteTitles: RadioGx: An R Package for Analysis of Large Radiogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R dependencyCount: 146 Package: RaggedExperiment Version: 1.22.0 Depends: R (>= 4.2.0), GenomicRanges (>= 1.37.17) Imports: BiocGenerics, GenomeInfoDb, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: c1759ca69795e95282a7add9c41cb1ec NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. The RaggedExperiment class derives from a GRangesList representation and provides a semblance of a rectangular dataset. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut], Marcel Ramos [aut, cre] () Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: RELEASE_3_16 git_last_commit: 55a7c1b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RaggedExperiment_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RaggedExperiment_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RaggedExperiment_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RaggedExperiment_1.22.0.tgz vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, compartmap importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils, terraTCGAdata, MOCHA suggestsMe: maftools, MultiAssayExperiment, MultiDataSet, TENxIO, curatedTCGAData, SingleCellMultiModal dependencyCount: 25 Package: rain Version: 1.32.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 2727d6e2dcef610fb25cb16c01593b53 NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: RELEASE_3_16 git_last_commit: 82ee400 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rain_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rain_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rain_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rain_1.32.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 16 Package: rama Version: 1.72.0 Depends: R(>= 2.5.0) License: GPL (>= 2) MD5sum: f4fbe17e7330758a606a80ba412234ed NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/rama git_branch: RELEASE_3_16 git_last_commit: 112bac3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rama_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rama_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rama_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rama_1.72.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge dependencyCount: 0 Package: ramr Version: 1.6.0 Depends: R (>= 4.1), GenomicRanges, parallel, doParallel, foreach, doRNG, methods Imports: IRanges, BiocGenerics, ggplot2, reshape2, EnvStats, ExtDist, matrixStats, S4Vectors Suggests: RUnit, knitr, rmarkdown, gridExtra, annotatr, LOLA, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 Archs: x64 MD5sum: 29f6c32a7bfd70010dd7820e4cb6359d NeedsCompilation: no Title: Detection of Rare Aberrantly Methylated Regions in Array and NGS Data Description: ramr is an R package for detection of low-frequency aberrant methylation events in large data sets obtained by methylation profiling using array or high-throughput bisulfite sequencing. In addition, package provides functions to visualize found aberrantly methylated regions (AMRs), to generate sets of all possible regions to be used as reference sets for enrichment analysis, and to generate biologically relevant test data sets for performance evaluation of AMR/DMR search algorithms. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, MethylationArray, MethylSeq Author: Oleksii Nikolaienko [aut, cre] () Maintainer: Oleksii Nikolaienko URL: https://github.com/BBCG/ramr VignetteBuilder: knitr BugReports: https://github.com/BBCG/ramr/issues git_url: https://git.bioconductor.org/packages/ramr git_branch: RELEASE_3_16 git_last_commit: ad5c596 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ramr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ramr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ramr_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ramr_1.6.0.tgz vignettes: vignettes/ramr/inst/doc/ramr.html vignetteTitles: ramr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramr/inst/doc/ramr.R dependencyCount: 66 Package: ramwas Version: 1.22.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 MD5sum: 4b7c596c895c8537eaa99001d49b345a NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: RELEASE_3_16 git_last_commit: 012c3d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ramwas_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ramwas_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ramwas_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ramwas_1.22.0.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 100 Package: RandomWalkRestartMH Version: 1.18.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) Archs: x64 MD5sum: 548d626d295812b7373b87dc79bfd4db NeedsCompilation: no Title: Random walk with restart on multiplex and heterogeneous Networks Description: This package performs Random Walk with Restart on multiplex and heterogeneous networks. It is described in the following article: "Random Walk With Restart On Multiplex And Heterogeneous Biological Networks" . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas [cre, aut, ctb] () Maintainer: Alberto Valdeolivas URL: https://github.com/alberto-valdeolivas/RandomWalkRestartMH VignetteBuilder: knitr BugReports: https://github.com/alberto-valdeolivas/RandomWalkRestartMH/issues git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH git_branch: RELEASE_3_16 git_last_commit: 187c410 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RandomWalkRestartMH_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RandomWalkRestartMH_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RandomWalkRestartMH_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RandomWalkRestartMH_1.18.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH.html vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Network hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH.R importsMe: netOmics dependencyCount: 54 Package: randPack Version: 1.44.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: e6bea184510b9fd62603424a6cd36c20 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: RELEASE_3_16 git_last_commit: 21f1b2c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/randPack_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/randPack_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/randPack_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/randPack_1.44.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 6 Package: randRotation Version: 1.10.0 Imports: methods, graphics, utils, stats, Rdpack (>= 0.7) Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle, testthat (>= 2.1.0), limma, sva License: GPL-3 MD5sum: c5696fd75c67fdd5dbc56131ffb4bba3 NeedsCompilation: no Title: Random Rotation Methods for High Dimensional Data with Batch Structure Description: A collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods. biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics, RNASeq, Preprocessing, Microarray, DifferentialExpression, GeneExpression, Genetics, MicroRNAArray, Normalization, StatisticalMethod Author: Peter Hettegger [aut, cre] () Maintainer: Peter Hettegger URL: https://github.com/phettegger/randRotation VignetteBuilder: knitr BugReports: https://github.com/phettegger/randRotation/issues git_url: https://git.bioconductor.org/packages/randRotation git_branch: RELEASE_3_16 git_last_commit: d28e08c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/randRotation_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/randRotation_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/randRotation_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/randRotation_1.10.0.tgz vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf vignetteTitles: Random Rotation Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R dependencyCount: 7 Package: RankProd Version: 3.24.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: 10efc2575c19ca9cd5db3851fe203a2f NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: RELEASE_3_16 git_last_commit: de12266 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RankProd_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RankProd_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RankProd_3.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RankProd_3.24.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: tRanslatome importsMe: mslp, POMA, synlet, INCATome dependencyCount: 6 Package: RAREsim Version: 1.2.0 Depends: R (>= 4.1.0) Imports: nloptr Suggests: markdown, ggplot2, BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: 9043c5336cea1ec8608a7fcb3bfaf2b6 NeedsCompilation: no Title: Simulation of Rare Variant Genetic Data Description: Haplotype simulations of rare variant genetic data that emulates real data can be performed with RAREsim. RAREsim uses the expected number of variants in MAC bins - either as provided by default parameters or estimated from target data - and an abundance of rare variants as simulated HAPGEN2 to probabilistically prune variants. RAREsim produces haplotypes that emulate real sequencing data with respect to the total number of variants, allele frequency spectrum, haplotype structure, and variant annotation. biocViews: Genetics, Software, VariantAnnotation, Sequencing Author: Megan Null [aut], Ryan Barnard [cre] Maintainer: Ryan Barnard URL: https://github.com/meganmichelle/RAREsim VignetteBuilder: knitr BugReports: https://github.com/meganmichelle/RAREsim/issues git_url: https://git.bioconductor.org/packages/RAREsim git_branch: RELEASE_3_16 git_last_commit: a813136 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RAREsim_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RAREsim_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RAREsim_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RAREsim_1.2.0.tgz vignettes: vignettes/RAREsim/inst/doc/RAREsim_Vignette.html vignetteTitles: RAREsim Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RAREsim/inst/doc/RAREsim_Vignette.R dependencyCount: 38 Package: RareVariantVis Version: 2.26.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: 7e2d9cb40b5cbd8a45bad829bcbcfb9f NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: RELEASE_3_16 git_last_commit: bc98104 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RareVariantVis_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RareVariantVis_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RareVariantVis_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RareVariantVis_2.26.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R dependencyCount: 138 Package: rawrr Version: 1.6.1 Depends: R (>= 4.1) Imports: grDevices, graphics, stats, utils Suggests: BiocStyle (>= 2.5), ExperimentHub, knitr, protViz (>= 0.7), rmarkdown, tartare (>= 1.5), testthat License: GPL-3 MD5sum: eeb8c5036c1f60614bc5dccdd3f3998e NeedsCompilation: no Title: Direct Access to Orbitrap Data and Beyond Description: This package wraps the functionality of the RawFileReader .NET assembly. Within the R environment, spectra and chromatograms are represented by S3 objects (Kockmann T. et al. (2020) ). The package provides basic functions to download and install the required third-party libraries. The package is developed, tested, and used at the Functional Genomics Center Zurich, Switzerland . biocViews: MassSpectrometry, Proteomics, Metabolomics Author: Christian Panse [aut, cre] (), Tobias Kockmann [aut] () Maintainer: Christian Panse URL: https://github.com/fgcz/rawrr/ SystemRequirements: mono-runtime 4.x or higher (including System.Data library) on Linux/macOS, .Net Framework (>= 4.5.1) on Microsoft Windows. VignetteBuilder: knitr BugReports: https://github.com/fgcz/rawrr/issues git_url: https://git.bioconductor.org/packages/rawrr git_branch: RELEASE_3_16 git_last_commit: e359c1e git_last_commit_date: 2023-02-07 Date/Publication: 2023-02-07 source.ver: src/contrib/rawrr_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rawrr_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rawrr_1.6.1.tgz vignettes: vignettes/rawrr/inst/doc/rawrr.html vignetteTitles: Direct Access to Orbitrap Data and Beyond hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rawrr/inst/doc/rawrr.R importsMe: MsBackendRawFileReader dependencyCount: 4 Package: RbcBook1 Version: 1.66.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 80299eaecea3130ed5960e61f59ea155 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: RELEASE_3_16 git_last_commit: fb3fdd2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RbcBook1_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RbcBook1_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RbcBook1_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RbcBook1_1.66.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 10 Package: Rbec Version: 1.6.0 Imports: Rcpp (>= 1.0.6), dada2, ggplot2, readr, doParallel, foreach, grDevices, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 1532e84c27978606ff547a9b2103d763 NeedsCompilation: yes Title: Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities Description: Rbec is a adapted version of DADA2 for analyzing amplicon sequencing data from synthetic communities (SynComs), where the reference sequences for each strain exists. Rbec can not only accurately profile the microbial compositions in SynComs, but also predict the contaminants in SynCom samples. biocViews: Sequencing, MicrobialStrain, Microbiome Author: Pengfan Zhang [aut, cre] Maintainer: Pengfan Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbec git_branch: RELEASE_3_16 git_last_commit: 652172f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rbec_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rbec_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rbec_1.6.0.tgz vignettes: vignettes/Rbec/inst/doc/Rbec.html vignetteTitles: Rbec hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbec/inst/doc/Rbec.R dependencyCount: 95 Package: RBGL Version: 1.74.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: x64 MD5sum: 3ae6e552c4fb6de1d33c276fd5a1446f NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RBGL git_branch: RELEASE_3_16 git_last_commit: e698db8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RBGL_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RBGL_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RBGL_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RBGL_1.74.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, fgga, pkgDepTools, PerfMeas, SubpathwayLNCE importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GenomicInteractionNodes, GOSim, GOstats, MIGSA, NCIgraph, OrganismDbi, pkgDepTools, Streamer, VariantFiltering, BiDAG, bnClustOmics, eff2, gRbase, HEMDAG, micd, pcalg, rags2ridges, RANKS, SEMgraph, wiseR suggestsMe: DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, archeofrag, gRc, maGUI dependencyCount: 8 Package: RBioinf Version: 1.58.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: d7c872a979f365fcd98320bfac95bc99 NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/RBioinf git_branch: RELEASE_3_16 git_last_commit: 79052cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RBioinf_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RBioinf_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RBioinf_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RBioinf_1.58.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R dependencyCount: 7 Package: rBiopaxParser Version: 2.38.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) Archs: x64 MD5sum: a37589851c633e70f5171a9e2e703b21 NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: RELEASE_3_16 git_last_commit: fa987f5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rBiopaxParser_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rBiopaxParser_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rBiopaxParser_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rBiopaxParser_2.38.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R importsMe: pwOmics suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: RBM Version: 1.30.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: 7311210610044808c7f93135b551e13e NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: RELEASE_3_16 git_last_commit: 428ed83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RBM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RBM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RBM_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RBM_1.30.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 7 Package: Rbowtie Version: 1.38.0 Suggests: testthat, parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 15be4b5120e924c4544608b20ba83732 NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler URL: https://github.com/fmicompbio/Rbowtie SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rbowtie/issues git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: RELEASE_3_16 git_last_commit: ad76614 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rbowtie_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rbowtie_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rbowtie_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rbowtie_1.38.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: crisprBowtie, multicrispr suggestsMe: crisprDesign, eisaR dependencyCount: 0 Package: Rbowtie2 Version: 2.4.2 Depends: R (>= 4.1.0) Imports: magrittr, Rsamtools Suggests: knitr, testthat (>= 3.0.0), rmarkdown License: GPL (>= 3) Archs: x64 MD5sum: 4a881ab9807bc6b0afadd15e931f4e11 NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. The package contains wrapper functions that allow for genome indexing and alignment to those indexes. The package also allows for the creation of .bam files via Rsamtools. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei [aut, cre], Wei Zhang [aut] Maintainer: Zheng Wei SystemRequirements: C++11, GNU make, samtools VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: RELEASE_3_16 git_last_commit: e55c9b7 git_last_commit_date: 2023-02-24 Date/Publication: 2023-02-24 source.ver: src/contrib/Rbowtie2_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rbowtie2_2.4.2.zip mac.binary.ver: bin/macosx/contrib/4.2/Rbowtie2_2.4.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rbowtie2_2.4.2.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: CircSeqAlignTk, esATAC, UMI4Cats dependencyCount: 32 Package: rbsurv Version: 2.56.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) Archs: x64 MD5sum: 0e7812dcfcb1e0f6e8f5e09f3e2121f4 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: RELEASE_3_16 git_last_commit: 001b76a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rbsurv_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rbsurv_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rbsurv_2.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rbsurv_2.56.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 12 Package: Rbwa Version: 1.2.0 Depends: R (>= 4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE OS_type: unix MD5sum: 771ec285b2e0215069885fe866de9560 NeedsCompilation: yes Title: R wrapper for BWA-backtrack and BWA-MEM aligners Description: Provides an R wrapper for BWA alignment algorithms. Both BWA-backtrack and BWA-MEM are available. Convenience function to build a BWA index from a reference genome is also provided. Currently not supported for Windows machines. biocViews: Sequencing, Alignment Author: Jean-Philippe Fortin [aut, cre] Maintainer: Jean-Philippe Fortin URL: https://github.com/Jfortin1/Rbwa SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/Jfortin1/Rbwa/issues git_url: https://git.bioconductor.org/packages/Rbwa git_branch: RELEASE_3_16 git_last_commit: 079a06a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rbwa_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/Rbwa_1.2.0.tgz vignettes: vignettes/Rbwa/inst/doc/Rbwa.html vignetteTitles: An introduction to Rbwa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbwa/inst/doc/Rbwa.R importsMe: crisprBwa suggestsMe: crisprDesign dependencyCount: 0 Package: Rcade Version: 1.39.1 Depends: R (>= 3.5.0), methods, GenomicRanges, Rsamtools, baySeq Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors (>= 0.23.19), IRanges, GenomeInfoDb, GenomicAlignments Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: 6a82670b5ab4f2c346baec87fa26f865 NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/Rcade git_branch: master git_last_commit: e9c9120 git_last_commit_date: 2022-05-10 Date/Publication: 2022-08-24 source.ver: src/contrib/Rcade_1.39.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rcade_1.39.1.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rcade_1.40.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R dependencyCount: 80 Package: RCAS Version: 1.24.0 Depends: R (>= 3.5.0), plotly (>= 4.5.2), DT (>= 0.2), data.table Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, ggseqlogo, utils, ranger, gprofiler2 Suggests: testthat, covr License: Artistic-2.0 MD5sum: 778d06ea591932daa0efe1f7b5fae85b NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: RELEASE_3_16 git_last_commit: 385b33e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RCAS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCAS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCAS_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCAS_1.24.0.tgz vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R dependencyCount: 156 Package: RCASPAR Version: 1.44.0 License: GPL (>=3) MD5sum: b6bfd524f2ceb32562ec7957e6bab199 NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: RELEASE_3_16 git_last_commit: fddad3b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RCASPAR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCASPAR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCASPAR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCASPAR_1.44.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.20.0 Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0) Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW, parallel, rmarkdown License: LGPL-3 + file LICENSE MD5sum: bc8436f200a8904438e7d6a5acc455d0 NeedsCompilation: no Title: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: RELEASE_3_16 git_last_commit: a4ce655 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rcellminer_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rcellminer_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rcellminer_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rcellminer_2.20.0.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R suggestsMe: rcellminerData dependencyCount: 70 Package: rCGH Version: 1.28.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: f028e9f8929fbb3bff388d82dccd5745 NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: RELEASE_3_16 git_last_commit: f82df6d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rCGH_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rCGH_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rCGH_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rCGH_1.28.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R importsMe: preciseTAD dependencyCount: 142 Package: RcisTarget Version: 1.18.2 Depends: R (>= 3.5.0) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, graphics, GenomeInfoDb, GenomicRanges, arrow (>= 2.0.0), dplyr, tibble, GSEABase, methods, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, foreach, gplots, rtracklayer, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork Enhances: doMC, doRNG, zoo License: GPL-3 MD5sum: e0f644d5525f83c3d0984ee27c46d6d7 NeedsCompilation: no Title: RcisTarget Identify transcription factor binding motifs enriched on a list of genes or genomic regions Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: RELEASE_3_16 git_last_commit: 1ef21ab git_last_commit_date: 2022-12-01 Date/Publication: 2022-12-01 source.ver: src/contrib/RcisTarget_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/RcisTarget_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/RcisTarget_1.18.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RcisTarget_1.18.2.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.html, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment, RcisTarget - on regions, RcisTarget - with background hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget_MainTutorial.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisOfGenomicRegions.R, vignettes/RcisTarget/inst/doc/Tutorial_AnalysisWithBackground.R dependencyCount: 127 Package: RCM Version: 1.14.0 Depends: R (>= 4.0), DBI Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, stats, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8bafb94a18f5de1fc8faea7363a06aeb NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. In addition, diagnostic plot to detect lack of fit are available. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel URL: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: RELEASE_3_16 git_last_commit: 9b7c6fb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RCM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCM_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCM_1.14.0.tgz vignettes: vignettes/RCM/inst/doc/RCMvignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCM/inst/doc/RCMvignette.R dependencyCount: 94 Package: Rcpi Version: 1.34.0 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 | file LICENSE MD5sum: 9c328a53b58b69841594625def584f9f NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: A molecular informatics toolkit with an integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: RELEASE_3_16 git_last_commit: 84c1d63 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rcpi_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rcpi_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rcpi_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rcpi_1.34.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R importsMe: proteasy dependencyCount: 60 Package: RCSL Version: 1.6.0 Depends: R (>= 4.1) Imports: RcppAnnoy, igraph, NbClust, Rtsne, ggplot2, methods, pracma, umap, grDevices, graphics, stats Suggests: knitr, rmarkdown, mclust, RcppAnnoy License: GPL-3 MD5sum: 7005de8d320e579e13704af3af664242 NeedsCompilation: no Title: Rank Constrained Similarity Learning for single cell RNA sequencing data Description: A novel clustering algorithm and toolkit RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both lo-cal similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similar-ity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similar-ity of a cell to other cells is a linear combination of its global similarity and local similarity. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Sequencing Author: Qinglin Mei [cre, aut], Guojun Li [fnd], Zhengchang Su [fnd] Maintainer: Qinglin Mei URL: https://github.com/QinglinMei/RCSL VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCSL git_branch: RELEASE_3_16 git_last_commit: d639d77 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RCSL_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCSL_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCSL_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCSL_1.6.0.tgz vignettes: vignettes/RCSL/inst/doc/RCSL.html vignetteTitles: RCSL package manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCSL/inst/doc/RCSL.R dependencyCount: 55 Package: Rcwl Version: 1.14.0 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools, basilisk Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE MD5sum: fd40c03248d1040bdb2c0ef51869ecba NeedsCompilation: no Title: An R interface to the Common Workflow Language Description: The Common Workflow Language (CWL) is an open standard for development of data analysis workflows that is portable and scalable across different tools and working environments. Rcwl provides a simple way to wrap command line tools and build CWL data analysis pipelines programmatically within R. It increases the ease of usage, development, and maintenance of CWL pipelines. biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: RELEASE_3_16 git_last_commit: a0117ad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rcwl_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/Rcwl_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rcwl_1.14.0.tgz vignettes: vignettes/Rcwl/inst/doc/Rcwl.html vignetteTitles: Rcwl: An R interface to the Common Workflow Language hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R dependsOnMe: RcwlPipelines dependencyCount: 123 Package: RcwlPipelines Version: 1.14.0 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils, git2r, httr, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 78c64b2a8df420d4f71dfbf05efa3914 NeedsCompilation: no Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: RELEASE_3_16 git_last_commit: 3004adb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RcwlPipelines_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/RcwlPipelines_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RcwlPipelines_1.14.0.tgz vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html vignetteTitles: RcwlPipelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R dependencyCount: 136 Package: RCX Version: 1.2.2 Depends: R (>= 4.2.0) Imports: jsonlite, plyr, igraph, methods Suggests: BiocStyle, testthat, knitr, rmarkdown, base64enc, graph License: MIT + file LICENSE MD5sum: 94b6fe61c0bf12932df4d53eb20ef15d NeedsCompilation: no Title: R package implementing the Cytoscape Exchange (CX) format Description: Create, handle, validate, visualize and convert networks in the Cytoscape exchange (CX) format to standard data types and objects. The package also provides conversion to and from objects of iGraph and graphNEL. The CX format is also used by the NDEx platform, a online commons for biological networks, and the network visualization software Cytocape. biocViews: Pathways, DataImport, Network Author: Florian Auer [aut, cre] () Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/RCX VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/RCX/issues git_url: https://git.bioconductor.org/packages/RCX git_branch: RELEASE_3_16 git_last_commit: 85e1d89 git_last_commit_date: 2023-01-26 Date/Publication: 2023-01-26 source.ver: src/contrib/RCX_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCX_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/RCX_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCX_1.2.2.tgz vignettes: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.html, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.html, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.html, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.html vignetteTitles: Appendix: The RCX and CX Data Model, 02. Creating RCX from scratch, 03. Extending the RCX Data Model, 01. RCX - an R package implementing the Cytoscape Exchange (CX) format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCX/inst/doc/Appendix_The_RCX_and_CX_Data_Model.R, vignettes/RCX/inst/doc/Creating_RCX_from_scratch.R, vignettes/RCX/inst/doc/Extending_the_RCX_Data_Model.R, vignettes/RCX/inst/doc/RCX_an_R_package_implementing_the_Cytoscape_Exchange_format.R dependsOnMe: ndexr dependencyCount: 16 Package: RCy3 Version: 2.18.0 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, stats, graph, fs, uuid, uchardet, glue, RCurl, base64url, base64enc, IRkernel, IRdisplay, RColorBrewer, gplots Suggests: BiocStyle, knitr, rmarkdown, igraph, grDevices License: MIT + file LICENSE MD5sum: 1c5ce5d734cb43e0ebf1518afd03952e NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. Anything you can do using the graphical user interface of Cytoscape, you can now do with a single RCy3 function. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alex Pico [aut, cre] (), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb], Yihang Xin [ctb] Maintainer: Alex Pico URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: RELEASE_3_16 git_last_commit: b3e5f7b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RCy3_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCy3_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCy3_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCy3_2.18.0.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 14. Jupyter Bridge and RCy3 ~10 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Jupyter-bridge-rcy3.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, CeTF, fedup, MetaPhOR, MOGAMUN, NCIgraph, netZooR, regutools, TimiRGeN, transomics2cytoscape, lilikoi, netgsa, ScriptMapR suggestsMe: graphite, netDx, rScudo, sharp, sparsebnUtils dependencyCount: 50 Package: RCyjs Version: 2.20.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4870cabebdb8885c99c4f4658dbe9f4e NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: RELEASE_3_16 git_last_commit: 78b2eb0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RCyjs_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RCyjs_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RCyjs_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RCyjs_2.20.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 17 Package: rDGIdb Version: 1.24.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: ab8974b7e49b4eda6db71ed9502396eb NeedsCompilation: no Title: R Wrapper for DGIdb Description: The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (https://www.dgidb.org/). biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics, FunctionalGenomics,WorkflowStep,Annotation Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel Maintainer: Lars Bosshard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rDGIdb git_branch: RELEASE_3_16 git_last_commit: 96db5e2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rDGIdb_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rDGIdb_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rDGIdb_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rDGIdb_1.24.0.tgz vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf vignetteTitles: Query DGIdb using R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rDGIdb/inst/doc/vignette.R dependencyCount: 11 Package: Rdisop Version: 1.58.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 MD5sum: 1e641147bc1679a9bcb467e0344675fa NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: RELEASE_3_16 git_last_commit: 6de1516 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rdisop_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rdisop_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rdisop_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rdisop_1.58.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: CorMID, enviGCMS, InterpretMSSpectrum, MetabolomicsBasics suggestsMe: adductomicsR, MSnbase, RforProteomics dependencyCount: 3 Package: RDRToolbox Version: 1.48.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) Archs: x64 MD5sum: 06a9621b7cb5a07a65149f773fad09f3 NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: RELEASE_3_16 git_last_commit: 915aab9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RDRToolbox_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RDRToolbox_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RDRToolbox_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RDRToolbox_1.48.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R suggestsMe: loon dependencyCount: 40 Package: ReactomeContentService4R Version: 1.6.0 Imports: httr, jsonlite, utils, magick (>= 2.5.1), data.table, doParallel, foreach, parallel Suggests: pdftools, testthat, knitr, rmarkdown License: Apache License (>= 2.0) | file LICENSE MD5sum: 1bd9ccae307eac6f9d411110d9c37ccc NeedsCompilation: no Title: Interface for the Reactome Content Service Description: Reactome is a free, open-source, open access, curated and peer-reviewed knowledgebase of bio-molecular pathways. This package is to interact with the Reactome Content Service API. Pre-built functions would allow users to retrieve data and images that consist of proteins, pathways, and other molecules related to a specific gene or entity in Reactome. biocViews: DataImport, Pathways, Reactome Author: Chi-Lam Poon [aut, cre] (), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeContentService4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeContentService4R/issues git_url: https://git.bioconductor.org/packages/ReactomeContentService4R git_branch: RELEASE_3_16 git_last_commit: 5ea1928 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReactomeContentService4R_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomeContentService4R_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomeContentService4R_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ReactomeContentService4R_1.6.0.tgz vignettes: vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.html vignetteTitles: ReactomeContentService4R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeContentService4R/inst/doc/ReactomeContentService4R.R importsMe: ReactomeGraph4R dependencyCount: 20 Package: ReactomeGraph4R Version: 1.6.0 Depends: R (>= 4.1) Imports: neo4r, utils, getPass, jsonlite, purrr, magrittr, data.table, rlang, ReactomeContentService4R, doParallel, parallel, foreach Suggests: knitr, rmarkdown, testthat, stringr, networkD3, visNetwork, wesanderson License: Apache License (>= 2) MD5sum: 0709eab7151b9846f79000ca55834b0a NeedsCompilation: no Title: Interface for the Reactome Graph Database Description: Pathways, reactions, and biological entities in Reactome knowledge are systematically represented as an ordered network. Instances are represented as nodes and relationships between instances as edges; they are all stored in the Reactome Graph Database. This package serves as an interface to query the interconnected data from a local Neo4j database, with the aim of minimizing the usage of Neo4j Cypher queries. biocViews: DataImport, Pathways, Reactome, Network, GraphAndNetwork Author: Chi-Lam Poon [aut, cre] (), Reactome [cph] Maintainer: Chi-Lam Poon URL: https://github.com/reactome/ReactomeGraph4R VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGraph4R/issues git_url: https://git.bioconductor.org/packages/ReactomeGraph4R git_branch: RELEASE_3_16 git_last_commit: 87e3916 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReactomeGraph4R_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomeGraph4R_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomeGraph4R_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ReactomeGraph4R_1.6.0.tgz vignettes: vignettes/ReactomeGraph4R/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomeGraph4R/inst/doc/Introduction.R dependencyCount: 72 Package: ReactomeGSA Version: 1.12.0 Imports: jsonlite, httr, progress, ggplot2, methods, gplots, RColorBrewer, dplyr, tidyr Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase, devtools Enhances: limma, edgeR, Seurat (>= 3.0), scater License: MIT + file LICENSE MD5sum: 02790814f0f95a1c8add9fb8bdecdeee NeedsCompilation: no Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues git_url: https://git.bioconductor.org/packages/ReactomeGSA git_branch: RELEASE_3_16 git_last_commit: 8ff0616 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReactomeGSA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomeGSA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomeGSA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ReactomeGSA_1.12.0.tgz vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Analysing single-cell RNAseq data, Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependsOnMe: ReactomeGSA.data dependencyCount: 60 Package: ReactomePA Version: 1.42.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2 (>= 3.3.5), ggraph, reactome.db, igraph, graphite, gson Suggests: BiocStyle, clusterProfiler, knitr, rmarkdown, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: b790f73ac9a2525ef354ba60162f8d16 NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: RELEASE_3_16 git_last_commit: db48e25 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReactomePA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReactomePA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReactomePA_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ReactomePA_1.42.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R importsMe: bioCancer, epihet, miRspongeR, multiSight, Pigengene, scTensor, ExpHunterSuite suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, GRaNIE, scGPS dependencyCount: 131 Package: ReadqPCR Version: 1.44.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 298d2504fffbaf4bc062eb817b19d166 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: RELEASE_3_16 git_last_commit: b5adc83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReadqPCR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReadqPCR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReadqPCR_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ReadqPCR_1.44.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR dependencyCount: 6 Package: REBET Version: 1.16.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: b52758b2e523c69de1785c983247e217 NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: RELEASE_3_16 git_last_commit: efb54f9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/REBET_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/REBET_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/REBET_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/REBET_1.16.0.tgz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 16 Package: rebook Version: 1.8.0 Imports: utils, methods, knitr (>= 1.32), rmarkdown, CodeDepends, dir.expiry, filelock, BiocStyle Suggests: testthat, igraph, XML, BiocManager, RCurl, bookdown, rappdirs, yaml, BiocParallel, OSCA.intro, OSCA.workflows License: GPL-3 Archs: x64 MD5sum: 3ded885d23e56eff97ceb55783bb2404 NeedsCompilation: no Title: Re-using Content in Bioconductor Books Description: Provides utilities to re-use content across chapters of a Bioconductor book. This is mostly based on functionality developed while writing the OSCA book, but generalized for potential use in other large books with heavy compute. Also contains some functions to assist book deployment. biocViews: Software, Infrastructure, ReportWriting Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: RELEASE_3_16 git_last_commit: 0913c6e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rebook_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rebook_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rebook_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rebook_1.8.0.tgz vignettes: vignettes/rebook/inst/doc/userguide.html vignetteTitles: Reusing book content hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rebook/inst/doc/userguide.R dependsOnMe: csawBook, OSCA, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook dependencyCount: 48 Package: receptLoss Version: 1.10.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here License: GPL-3 + file LICENSE MD5sum: 7709521ab2945d212720bf1e95e87680 NeedsCompilation: no Title: Unsupervised Identification of Genes with Expression Loss in Subsets of Tumors Description: receptLoss identifies genes whose expression is lost in subsets of tumors relative to normal tissue. It is particularly well-suited in cases where the number of normal tissue samples is small, as the distribution of gene expression in normal tissue samples is approximated by a Gaussian. Originally designed for identifying nuclear hormone receptor expression loss but can be applied transcriptome wide as well. biocViews: GeneExpression, StatisticalMethod Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/receptLoss git_branch: RELEASE_3_16 git_last_commit: c849e35 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/receptLoss_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/receptLoss_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/receptLoss_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/receptLoss_1.10.0.tgz vignettes: vignettes/receptLoss/inst/doc/receptLoss.html vignetteTitles: receptLoss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/receptLoss/inst/doc/receptLoss.R dependencyCount: 60 Package: reconsi Version: 1.10.0 Imports: phyloseq, ks, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats, Matrix Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 6230782936e0da12933805c5b6581ddd NeedsCompilation: no Title: Resampling Collapsed Null Distributions for Simultaneous Inference Description: Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts. biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry Author: Stijn Hawinkel [cre, aut] () Maintainer: Stijn Hawinkel VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: RELEASE_3_16 git_last_commit: f38bc00 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/reconsi_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/reconsi_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/reconsi_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/reconsi_1.10.0.tgz vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R dependencyCount: 90 Package: recount Version: 1.24.1 Depends: R (>= 3.5.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap, DT, edgeR, ggplot2, RColorBrewer License: Artistic-2.0 MD5sum: 7b1e5f6baba505fb5f7d3d601c9a64aa NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (), Kasper Daniel Hansen [ctb] (), Ben Langmead [ctb] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_16 git_last_commit: e3cb1d7 git_last_commit_date: 2023-02-20 Date/Publication: 2023-02-21 source.ver: src/contrib/recount_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/recount_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.2/recount_1.24.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/recount_1.24.1.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: dasper, ODER, recount3 dependencyCount: 164 Package: recount3 Version: 1.8.0 Depends: SummarizedExperiment Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, RCurl, data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown, testthat, pryr, interactiveDisplayBase, recount License: Artistic-2.0 MD5sum: 285ed605ac98b391776dd70704e8157e NeedsCompilation: no Title: Explore and download data from the recount3 project Description: The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre] () Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recount3 VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/recount3/issues git_url: https://git.bioconductor.org/packages/recount3 git_branch: RELEASE_3_16 git_last_commit: f30787d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/recount3_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/recount3_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/recount3_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/recount3_1.8.0.tgz vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html vignetteTitles: recount3 quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R suggestsMe: RNAseqQC dependencyCount: 91 Package: recountmethylation Version: 1.8.6 Depends: R (>= 4.1) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache, basilisk, reticulate, DelayedMatrixStats Suggests: minfiData, knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 MD5sum: de137c76f378f5b187e5fca8f4bb1161 NeedsCompilation: no Title: Access and analyze public DNA methylation array data compilations Description: Resources for cross-study analyses of public DNAm array data from NCBI GEO repo, produced using Illumina's Infinium HumanMethylation450K (HM450K) and MethylationEPIC (EPIC) platforms. Provided functions enable download, summary, and filtering of large compilation files. Vignettes detail background about file formats, example analyses, and more. Note the disclaimer on package load and consult the main manuscripts for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (), Brian Walsh [aut] (), Kyle Ellrott [aut] (), Kasper D Hansen [aut] (), Reid F Thompson [aut] (), Abhinav Nellore [aut] () Maintainer: Sean K Maden URL: https://github.com/metamaden/recountmethylation VignetteBuilder: knitr BugReports: https://github.com/metamaden/recountmethylation/issues git_url: https://git.bioconductor.org/packages/recountmethylation git_branch: RELEASE_3_16 git_last_commit: 7c9f066 git_last_commit_date: 2023-03-26 Date/Publication: 2023-03-27 source.ver: src/contrib/recountmethylation_1.8.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/recountmethylation_1.8.6.zip mac.binary.ver: bin/macosx/contrib/4.2/recountmethylation_1.8.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/recountmethylation_1.8.6.tgz vignettes: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.html, vignettes/recountmethylation/inst/doc/exporting_saving_data.html, vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.html, vignettes/recountmethylation/inst/doc/recountmethylation_glint.html, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.html, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.html, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html vignetteTitles: Practical uses for CpG annotations, Working with DNAm data types, Data Analyses, Determine population ancestry from DNAm arrays, Power analysis for DNAm arrays, Nearest neighbors analysis for DNAm arrays, recountmethylation User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountmethylation/inst/doc/cpg_probe_annotations.R, vignettes/recountmethylation/inst/doc/exporting_saving_data.R, vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R, vignettes/recountmethylation/inst/doc/recountmethylation_glint.R, vignettes/recountmethylation/inst/doc/recountmethylation_pwrewas.R, vignettes/recountmethylation/inst/doc/recountmethylation_search_index.R, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R dependencyCount: 147 Package: recoup Version: 1.26.0 Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, Biostrings, circlize, GenomeInfoDb, GenomicFeatures, graphics, grDevices, httr, IRanges, methods, parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats, stringr, utils Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) MD5sum: 820478d6a78305a13e8c729a2469242e NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq, ChipOnChip, Alignment, DataImport Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: RELEASE_3_16 git_last_commit: c384eae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/recoup_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/recoup_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/recoup_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/recoup_1.26.0.tgz vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R dependencyCount: 120 Package: RedeR Version: 2.2.1 Depends: R (>= 4.0), methods Imports: igraph Suggests: BiocStyle, knitr, rmarkdown, markdown, TreeAndLeaf License: GPL (>= 3) MD5sum: 42e85cbfccbc537a1b7a793e9628eed1 NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of nested networks. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (Java>= 11) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_16 git_last_commit: 49897a9 git_last_commit_date: 2022-12-13 Date/Publication: 2022-12-14 source.ver: src/contrib/RedeR_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RedeR_2.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RedeR_2.2.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RedeR_2.2.1.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical networks" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf dependencyCount: 13 Package: RedisParam Version: 1.0.0 Depends: R (>= 4.2.0), BiocParallel (>= 1.29.12) Imports: methods, redux, withr, futile.logger Suggests: rmarkdown, knitr, testthat, BiocStyle License: Artistic-2.0 MD5sum: 9858ca0a5ecb9faabe3b9decf2df6987 NeedsCompilation: no Title: Provide a 'redis' back-end for BiocParallel Description: This package provides a Redis-based back-end for BiocParallel, enabling an alternative mechanism for distributed computation. The The 'manager' distributes tasks to a 'worker' pool through a central Redis server, rather than directly to workers as with other BiocParallel implementations. This means that the worker pool can change dynamically during job evaluation. All features of BiocParallel are supported, including reproducible random number streams, logging to the manager, and alternative 'load balancing' task distributions. biocViews: Infrastructure Author: Martin Morgan [aut, cre] (), Jiefei Wang [aut] Maintainer: Martin Morgan SystemRequirements: hiredis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedisParam git_branch: RELEASE_3_16 git_last_commit: 2d0585c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RedisParam_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RedisParam_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RedisParam_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RedisParam_1.0.0.tgz vignettes: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.html, vignettes/RedisParam/inst/doc/RedisParamUserGuide.html vignetteTitles: RedisParam for Developers, Using RedisParam hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RedisParam/inst/doc/RedisParamDeveloperGuide.R, vignettes/RedisParam/inst/doc/RedisParamUserGuide.R dependencyCount: 20 Package: REDseq Version: 1.44.0 Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils License: GPL (>=2) MD5sum: 4383a3e9c1f57e376cdeddcd377c547a NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu, Junhui Li and Thomas Fazzio Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/REDseq git_branch: RELEASE_3_16 git_last_commit: 647d21d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/REDseq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/REDseq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/REDseq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/REDseq_1.44.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R dependencyCount: 125 Package: RefPlus Version: 1.68.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 654073024eab6c9d742588c40d00bdf2 NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang git_url: https://git.bioconductor.org/packages/RefPlus git_branch: RELEASE_3_16 git_last_commit: 67b3f3a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RefPlus_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RefPlus_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RefPlus_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RefPlus_1.68.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R dependencyCount: 26 Package: RegEnrich Version: 1.8.0 Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet, SummarizedExperiment Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA, ggplot2 (>= 2.2.0), methods, reshape2, magrittr Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) MD5sum: d6b858adec1e59b2cf1c01e83ffafe25 NeedsCompilation: no Title: Gene regulator enrichment analysis Description: This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking. biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel, Transcription, GeneTarget, NetworkEnrichment, DifferentialExpression, Network, NetworkInference, GeneSetEnrichment, FunctionalPrediction Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut] Maintainer: Weiyang Tao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegEnrich git_branch: RELEASE_3_16 git_last_commit: 9f6e24a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RegEnrich_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RegEnrich_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RegEnrich_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RegEnrich_1.8.0.tgz vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html vignetteTitles: Gene regulator enrichment with RegEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R dependencyCount: 153 Package: regioneR Version: 1.30.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 058b9446d7ccc48a690df062dc59c6cc NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: RELEASE_3_16 git_last_commit: 52f238d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/regioneR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regioneR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regioneR_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/regioneR_1.30.0.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR, regioneReloaded importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, RgnTX, RIPAT, RLSeq, UMI4Cats suggestsMe: CNVRanger, EpiMix, MitoHEAR dependencyCount: 51 Package: regioneReloaded Version: 1.0.0 Depends: R (>= 4.2), regioneR Imports: stats, RColorBrewer, Rtsne, umap, ggplot2, ggrepel, reshape2, methods, scales, cluster, grid, grDevices Suggests: rmarkdown, BiocStyle, GenomeInfoDb, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 0ab6f8fe7ab85f591e22d2642e8fdf9c NeedsCompilation: no Title: RegioneReloaded: Multiple Association for Genomic Region Sets Description: RegioneReloaded is a package that allows simultaneous analysis of associations between genomic region sets, enabling clustering of data and the creation of ready-to-publish graphs. It takes over and expands on all the features of its predecessor regioneR. It also incorporates a strategy to improve p-value calculations and normalize z-scores coming from multiple analysis to allow for their direct comparison. RegioneReloaded builds upon regioneR by adding new plotting functions for obtaining publication-ready graphs. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation, Clustering, MultipleComparison Author: Roberto Malinverni [aut, cre] (), David Corujo [aut], Bernat Gel [aut] Maintainer: Roberto Malinverni URL: https://github.com/RMalinverni/regioneReload VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneReloaded git_branch: RELEASE_3_16 git_last_commit: ac84607 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/regioneReloaded_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regioneReloaded_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regioneReloaded_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/regioneReloaded_1.0.0.tgz vignettes: vignettes/regioneReloaded/inst/doc/regioneReloaded.html vignetteTitles: regioneReloaded hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneReloaded/inst/doc/regioneReloaded.R dependencyCount: 99 Package: regionReport Version: 1.32.0 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.25.3), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.29.1), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: b6092e9d7af84477fd0bb5ab73197eb8 NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology, QualityControl Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: RELEASE_3_16 git_last_commit: 608a5f1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/regionReport_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regionReport_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regionReport_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/regionReport_1.32.0.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/regionReport.R importsMe: recountWorkflow suggestsMe: recount dependencyCount: 171 Package: regsplice Version: 1.24.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 546b47268a1484299a1444b01178af01 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: RELEASE_3_16 git_last_commit: 72ba021 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/regsplice_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regsplice_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regsplice_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/regsplice_1.24.0.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 39 Package: regutools Version: 1.10.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges, Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils, BiocFileCache Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: 7a5a4dea46c3d14a8fae25f77fc8a3e1 NeedsCompilation: no Title: regutools: an R package for data extraction from RegulonDB Description: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. biocViews: GeneRegulation, GeneExpression, SystemsBiology, Network,NetworkInference,Visualization, Transcription Author: Joselyn Chavez [aut, cre] (), Carmina Barberena-Jonas [aut] (), Jesus E. Sotelo-Fonseca [aut] (), Jose Alquicira-Hernandez [ctb] (), Heladia Salgado [ctb] (), Leonardo Collado-Torres [aut] (), Alejandro Reyes [aut] () Maintainer: Joselyn Chavez URL: https://github.com/ComunidadBioInfo/regutools VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regutools git_url: https://git.bioconductor.org/packages/regutools git_branch: RELEASE_3_16 git_last_commit: 329ca37 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/regutools_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/regutools_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/regutools_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/regutools_1.10.0.tgz vignettes: vignettes/regutools/inst/doc/regutools.html vignetteTitles: regutools: an R package for data extraction from RegulonDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regutools/inst/doc/regutools.R dependencyCount: 180 Package: REMP Version: 1.22.0 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 MD5sum: c933c7b3feb7f5aa6daed4b1e71bb10c NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: RELEASE_3_16 git_last_commit: 1312141 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/REMP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/REMP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/REMP_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/REMP_1.22.0.tgz vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R dependencyCount: 216 Package: Repitools Version: 1.44.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) MD5sum: a39f510b1663ce55c1efaeddb3f4b98b NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson git_url: https://git.bioconductor.org/packages/Repitools git_branch: RELEASE_3_16 git_last_commit: 5f359d2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Repitools_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Repitools_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Repitools_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Repitools_1.44.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R dependencyCount: 116 Package: ReportingTools Version: 2.38.0 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db, rmarkdown, markdown License: Artistic-2.0 Archs: x64 MD5sum: 84577f6262b4ac78035b6363fe918e36 NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, knitr git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: RELEASE_3_16 git_last_commit: 5c4971e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReportingTools_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReportingTools_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReportingTools_2.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ReportingTools_2.38.0.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf, vignettes/ReportingTools/inst/doc/knitr.html vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny, Knitr and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R dependsOnMe: rnaseqGene importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 178 Package: RepViz Version: 1.14.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: rmarkdown, knitr, testthat License: GPL-3 MD5sum: 1b3d39fe1ea906228a5f586d40ce8950 NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: RELEASE_3_16 git_last_commit: a1e1628 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RepViz_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RepViz_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RepViz_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RepViz_1.14.0.tgz vignettes: vignettes/RepViz/inst/doc/RepViz.html vignetteTitles: RepViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RepViz/inst/doc/RepViz.R dependencyCount: 82 Package: ReQON Version: 1.44.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: 40f412c9035e8a24cfbb944a5fd55fe2 NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, HighThroughputSequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 git_url: https://git.bioconductor.org/packages/ReQON git_branch: RELEASE_3_16 git_last_commit: 1505281 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ReQON_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ReQON_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ReQON_1.44.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R dependencyCount: 33 Package: ResidualMatrix Version: 1.8.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 68e68610b5928669376b8e3b17f2bfc8 NeedsCompilation: no Title: Creating a DelayedMatrix of Regression Residuals Description: Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. biocViews: Software, DataRepresentation, Regression, BatchEffect, ExperimentalDesign Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ResidualMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ResidualMatrix/issues git_url: https://git.bioconductor.org/packages/ResidualMatrix git_branch: RELEASE_3_16 git_last_commit: 888a93e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ResidualMatrix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ResidualMatrix_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ResidualMatrix_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ResidualMatrix_1.8.0.tgz vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html vignetteTitles: Using the ResidualMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R importsMe: batchelor suggestsMe: BiocSingular, scran dependencyCount: 15 Package: RESOLVE Version: 1.0.0 Depends: R (>= 3.5.0), NMF Imports: Biostrings, BSgenome, BSgenome.Hsapiens.1000genomes.hs37d5, cluster, data.table, GenomeInfoDb, GenomicRanges, glmnet, ggplot2, gridExtra, IRanges, lsa, nnls, parallel, reshape2 Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: c3143f8a49a31bab8b10b594921fc2cd NeedsCompilation: no Title: RESOLVE: An R package for the efficient analysis of mutational signatures from cancer genomes Description: Cancer is a genetic disease caused by somatic mutations in genes controlling key biological functions such as cellular growth and division. Such mutations may arise both through cell-intrinsic and exogenous processes, generating characteristic mutational patterns over the genome named mutational signatures. The study of mutational signatures have become a standard component of modern genomics studies, since it can reveal which (environmental and endogenous) mutagenic processes are active in a tumor, and may highlight markers for therapeutic response. Mutational signatures computational analysis presents many pitfalls. First, the task of determining the number of signatures is very complex and depends on heuristics. Second, several signatures have no clear etiology, casting doubt on them being computational artifacts rather than due to mutagenic processes. Last, approaches for signatures assignment are greatly influenced by the set of signatures used for the analysis. To overcome these limitations, we developed RESOLVE (Robust EStimation Of mutationaL signatures Via rEgularization), a framework that allows the efficient extraction and assignment of mutational signatures. RESOLVE implements a novel algorithm that enables (i) the efficient extraction, (ii) exposure estimation, and (iii) confidence assessment during the computational inference of mutational signatures. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [aut] (), Luca De Sano [cre, aut] () Maintainer: Luca De Sano URL: https://github.com/danro9685/RESOLVE VignetteBuilder: knitr BugReports: https://github.com/danro9685/RESOLVE/issues git_url: https://git.bioconductor.org/packages/RESOLVE git_branch: RELEASE_3_16 git_last_commit: 5223f8c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RESOLVE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RESOLVE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RESOLVE_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RESOLVE_1.0.0.tgz vignettes: vignettes/RESOLVE/inst/doc/RESOLVE.html vignetteTitles: RESOLVE.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RESOLVE/inst/doc/RESOLVE.R dependencyCount: 99 Package: restfulSE Version: 1.20.0 Depends: R (>= 3.6), SummarizedExperiment,DelayedArray Imports: utils, stats, methods, S4Vectors, Biobase,reshape2, AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1), magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db, BiocStyle, restfulSEData, rmarkdown License: Artistic-2.0 MD5sum: f8668cef27e54b75af55768530c119ba NeedsCompilation: no Title: Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface Description: This package provides functions and classes to interface with remote data stores by operating on SummarizedExperiment-like objects. biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing, Coverage Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/restfulSE git_branch: RELEASE_3_16 git_last_commit: 0d059b0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/restfulSE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/restfulSE_1.19.0.zip mac.binary.ver: bin/macosx/contrib/4.2/restfulSE_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/restfulSE_1.20.0.tgz vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf vignetteTitles: restfulSE -- experiments with SE interface to remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R dependsOnMe: tenXplore suggestsMe: BiocOncoTK, BiocSklearn dependencyCount: 117 Package: rexposome Version: 1.20.1 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: f0885765411130c947d08ff7f300c993 NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: RELEASE_3_16 git_last_commit: c02ba64 git_last_commit_date: 2023-01-26 Date/Publication: 2023-01-26 source.ver: src/contrib/rexposome_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rexposome_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rexposome_1.20.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rexposome_1.20.1.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome suggestsMe: brgedata dependencyCount: 157 Package: rfaRm Version: 1.10.2 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics License: GPL-3 MD5sum: 99d651dd83546eefddc2d199e6b8653b NeedsCompilation: no Title: An R interface to the Rfam database Description: rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, MultipleSequenceAlignment Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo Ledesma-Amaro Maintainer: Lara Selles Vidal , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rfaRm git_branch: RELEASE_3_16 git_last_commit: 100d347 git_last_commit_date: 2022-12-12 Date/Publication: 2022-12-12 source.ver: src/contrib/rfaRm_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/rfaRm_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/rfaRm_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rfaRm_1.10.2.tgz vignettes: vignettes/rfaRm/inst/doc/rfaRm.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R dependencyCount: 47 Package: Rfastp Version: 1.8.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 635fd2494d9427bb7d15e3d24eb46c2e NeedsCompilation: yes Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality Control, Adapter, low quality and polyX trimming) and UMI Sequence Parsing). Description: Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. biocViews: QualityControl, Sequencing, Preprocessing, Software Author: Wei Wang [aut] (), Ji-Dung Luo [ctb] (), Thomas Carroll [cre, aut] () Maintainer: Thomas Carroll SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rfastp git_branch: RELEASE_3_16 git_last_commit: d959817 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rfastp_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rfastp_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rfastp_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rfastp_1.8.0.tgz vignettes: vignettes/Rfastp/inst/doc/Rfastp.html vignetteTitles: Rfastp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R dependencyCount: 44 Package: rfPred Version: 1.36.0 Depends: R (>= 3.5.0), methods Imports: utils, GenomeInfoDb, data.table, IRanges, GenomicRanges, parallel, Rsamtools Suggests: BiocStyle License: GPL (>=2 ) Archs: x64 MD5sum: f7bfb56b1482c97ea84264c7d225b9da NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_16 git_last_commit: 3ea3c6f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rfPred_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rfPred_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rfPred_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rfPred_1.36.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 32 Package: rGADEM Version: 2.46.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer License: Artistic-2.0 Archs: x64 MD5sum: 3bd011a34a31b945502400d3f88663f0 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit git_url: https://git.bioconductor.org/packages/rGADEM git_branch: RELEASE_3_16 git_last_commit: eced781 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rGADEM_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rGADEM_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rGADEM_2.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rGADEM_2.46.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R dependencyCount: 48 Package: rGenomeTracks Version: 1.4.0 Depends: R (>= 4.1.0), Imports: imager, reticulate, methods, rGenomeTracksData Suggests: rmarkdown, knitr, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: cef5b377820ebf33e618194cc43d0a10 NeedsCompilation: no Title: Integerated visualization of epigenomic data Description: rGenomeTracks package leverages the power of pyGenomeTracks software with the interactivity of R. pyGenomeTracks is a python software that offers robust method for visualizing epigenetic data files like narrowPeak, Hic matrix, TADs and arcs, however though, here is no way currently to use it within R interactive session. rGenomeTracks wrapped the whole functionality of pyGenomeTracks with additional utilites to make to more pleasant for R users. biocViews: Software, HiC, Visualization Author: Omar Elashkar [aut, cre] () Maintainer: Omar Elashkar SystemRequirements: pyGenomeTracks (prefered to use install_pyGenomeTracks()) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGenomeTracks git_branch: RELEASE_3_16 git_last_commit: d1db997 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rGenomeTracks_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rGenomeTracks_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rGenomeTracks_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rGenomeTracks_1.4.0.tgz vignettes: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.html vignetteTitles: rGenomeTracks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGenomeTracks/inst/doc/rGenomeTracks.R dependencyCount: 110 Package: RGMQL Version: 1.18.0 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 5753849d023a2bc0672ae2a338e720a3 NeedsCompilation: no Title: GenoMetric Query Language for R/Bioconductor Description: This package brings the GenoMetric Query Language (GMQL) functionalities into the R environment. GMQL is a high-level, declarative language to manage heterogeneous genomic datasets for biomedical purposes, using simple queries to process genomic regions and their metadata and properties. GMQL adopts algorithms efficiently designed for big data using cloud-computing technologies (like Apache Hadoop and Spark) allowing GMQL to run on modern infrastructures, in order to achieve scalability and high performance. It allows to create, manipulate and extract genomic data from different data sources both locally and remotely. Our RGMQL functions allow complex queries and processing leveraging on the R idiomatic paradigm. The RGMQL package also provides a rich set of ancillary classes that allow sophisticated input/output management and sorting, such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1, Q2, Q3 (and many others). Note that many RGMQL functions are not directly executed in R environment, but are deferred until real execution is issued. biocViews: Software, Infrastructure, DataImport, Network, ImmunoOncology, SingleCell Author: Simone Pallotta [aut, cre], Marco Masseroli [aut] Maintainer: Simone Pallotta URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGMQL git_branch: RELEASE_3_16 git_last_commit: b687d58 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RGMQL_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RGMQL_1.17.0.zip vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.html vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R dependencyCount: 75 Package: RgnTX Version: 1.0.0 Depends: R (>= 4.2.0) Imports: GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, graphics, IRanges, methods, regioneR, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: 66e23f6176f741f9df34c82c34749803 NeedsCompilation: no Title: Colocalization analysis of transcriptome elements in the presence of isoform heterogeneity and ambiguity Description: RgnTX allows the integration of transcriptome annotations so as to model the complex alternative splicing patterns. It supports the testing of transcriptome elements without clear isoform association, which is often the real scenario due to technical limitations. It involves functions that do permutaion test for evaluating association between features and transcriptome regions. biocViews: AlternativeSplicing, Sequencing, RNASeq, MethylSeq, Transcription, SplicedAlignment Author: Yue Wang [aut, cre], Jia Meng [aut] Maintainer: Yue Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RgnTX git_branch: RELEASE_3_16 git_last_commit: d4c5c82 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RgnTX_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RgnTX_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RgnTX_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RgnTX_1.0.0.tgz vignettes: vignettes/RgnTX/inst/doc/RgnTX.html vignetteTitles: RgnTX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RgnTX/inst/doc/RgnTX.R dependencyCount: 113 Package: rgoslin Version: 1.2.0 Imports: Rcpp (>= 1.0.3), dplyr LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), BiocStyle, knitr, rmarkdown, kableExtra, BiocManager, stringr, ggplot2, tibble, lipidr License: MIT + file LICENSE MD5sum: 4711132a8acf576f30e8a5b757b28a16 NeedsCompilation: yes Title: Lipid Shorthand Name Parsing and Normalization Description: The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable. biocViews: Software, Lipidomics, Metabolomics, Preprocessing, Normalization, MassSpectrometry Author: Nils Hoffmann [aut, cre] (), Dominik Kopczynski [aut] () Maintainer: Nils Hoffmann URL: https://github.com/lifs-tools/rgoslin VignetteBuilder: knitr BugReports: https://github.com/lifs-tools/rgoslin/issues git_url: https://git.bioconductor.org/packages/rgoslin git_branch: RELEASE_3_16 git_last_commit: 6d51b88 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rgoslin_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rgoslin_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rgoslin_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rgoslin_1.2.0.tgz vignettes: vignettes/rgoslin/inst/doc/introduction.html vignetteTitles: Using R Goslin to parse and normalize lipid nomenclature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rgoslin/inst/doc/introduction.R dependencyCount: 22 Package: RGraph2js Version: 1.26.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 Archs: x64 MD5sum: 8a2ad6a9c701b812bec6ff8c6307db18 NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: RELEASE_3_16 git_last_commit: 1b1fabd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RGraph2js_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RGraph2js_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RGraph2js_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RGraph2js_1.26.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 10 Package: Rgraphviz Version: 2.42.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL MD5sum: 53f3f39cce41cc63cf99893bff0b0e10 NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: RELEASE_3_16 git_last_commit: f687744 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rgraphviz_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rgraphviz_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rgraphviz_2.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rgraphviz_2.42.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, dlsem, gridGraphviz, GUIProfiler, hasseDiagram importsMe: apComplex, biocGraph, BiocOncoTK, bnem, chimeraviz, CytoML, dce, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, KEGGgraph, MIGSA, mirIntegrator, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, SplicingGraphs, trackViewer, TRONCO, abn, BiDAG, bnpa, ceg, CePa, classGraph, cogmapr, dnet, gRain, gRbase, gRim, hpoPlot, ontologyPlot, SEMgraph, stablespec, wiseR suggestsMe: a4, altcdfenvs, annotate, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, MLP, NCIgraph, OmnipathR, pkgDepTools, RBGL, RBioinf, rBiopaxParser, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bnclassify, bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet, gRc, HEMDAG, iTOP, kpcalg, kst, lava, loon, maGUI, MCDA, micd, migraph, multiplex, ParallelPC, pcalg, pchc, psych, relations, rEMM, rPref, rSpectral, SCCI, sisal, SourceSet, textplot, tm, topologyGSA, tpc, unifDAG, zenplots dependencyCount: 9 Package: rGREAT Version: 2.0.2 Depends: R (>= 3.6.0), GenomicRanges, IRanges, methods Imports: graphics, rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats, GlobalOptions, shiny, DT, GenomicFeatures, digest, GO.db, progress, circlize, AnnotationDbi, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, RColorBrewer, S4Vectors, GenomeInfoDb, foreach, doParallel, Rcpp LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, rmarkdown, BiocManager, org.Mm.eg.db, msigdbr, KEGGREST, reactome.db Enhances: BioMartGOGeneSets, UniProtKeywords License: MIT + file LICENSE Archs: x64 MD5sum: dc311aa9d4fe0ace42cd1b745b57f2b1 NeedsCompilation: yes Title: GREAT Analysis - Functional Enrichment on Genomic Regions Description: GREAT (Genomic Regions Enrichment of Annotations Tool) is a type of functional enrichment analysis directly performed on genomic regions. This package implements the GREAT algorithm (the local GREAT analysis), also it supports directly interacting with the GREAT web service (the online GREAT analysis). Both analysis can be viewed by a Shiny application. rGREAT by default supports more than 600 organisms and a large number of gene set collections, as well as self-provided gene sets and organisms from users. Additionally, it implements a general method for dealing with background regions. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: RELEASE_3_16 git_last_commit: a8049fc git_last_commit_date: 2022-11-21 Date/Publication: 2022-11-21 source.ver: src/contrib/rGREAT_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/rGREAT_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/rGREAT_2.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rGREAT_2.0.2.tgz vignettes: vignettes/rGREAT/inst/doc/local-GREAT.html, vignettes/rGREAT/inst/doc/online-GREAT.html, vignettes/rGREAT/inst/doc/other-docs.html, vignettes/rGREAT/inst/doc/other-geneset-databases.html, vignettes/rGREAT/inst/doc/other-organisms.html vignetteTitles: 2. Analyze with local GREAT, 1. Analyze with online GREAT, 5. Other documents, 4. Work with other geneset databases, 3. Work with other organisms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rGREAT/inst/doc/local-GREAT.R, vignettes/rGREAT/inst/doc/online-GREAT.R, vignettes/rGREAT/inst/doc/other-geneset-databases.R, vignettes/rGREAT/inst/doc/other-organisms.R importsMe: ATACCoGAPS suggestsMe: TADCompare dependencyCount: 135 Package: RGSEA Version: 1.32.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) Archs: x64 MD5sum: 793958b5ea6408a64a55838f698cb546 NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: RELEASE_3_16 git_last_commit: c0aa6a2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RGSEA_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RGSEA_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RGSEA_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RGSEA_1.32.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 5 Package: rgsepd Version: 1.30.0 Depends: R (>= 4.2.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 1957660c4375ac94ed69d359c1e6ec3e NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: RELEASE_3_16 git_last_commit: d59ea56 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rgsepd_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rgsepd_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rgsepd_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rgsepd_1.30.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 126 Package: rhdf5 Version: 2.42.1 Depends: R (>= 4.0.0), methods Imports: Rhdf5lib (>= 1.13.4), rhdf5filters LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark, dplyr, ggplot2, mockery License: Artistic-2.0 MD5sum: 3f1738ccdee045f205c6309d40ad2754 NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Mike Smith [aut, cre] (), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: RELEASE_3_16 git_last_commit: 8df5fc7 git_last_commit_date: 2023-04-07 Date/Publication: 2023-04-07 source.ver: src/contrib/rhdf5_2.42.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rhdf5_2.42.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rhdf5_2.42.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rhdf5_2.42.1.tgz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud, rhdf5 - HDF5 interface for R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GSCA, HDF5Array, HiCBricks, LoomExperiment, MuData, octad importsMe: BayesSpace, BgeeCall, biomformat, bnbc, bsseq, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, cytomapper, diffHic, DropletUtils, epigraHMM, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HiCcompare, HiCDOC, HiContacts, IONiseR, MOFA2, NxtIRFcore, phantasus, ptairMS, PureCN, recountmethylation, ribor, scCB2, scone, signatureSearch, SpliceWiz, SpotClean, trackViewer, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, DmelSGI, MethylSeqData, ptairData, signatureSearchData, bioRad, ebvcube, file2meco, NEONiso, ondisc, rDataPipeline, smapr suggestsMe: edgeR, rhdf5filters, SCArray, slalom, Spectra, SummarizedExperiment, TENxIO, tximport, zellkonverter, antaresProcessing, antaresRead, antaresViz, conos, CRMetrics, digitalDLSorteR, io, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX dependencyCount: 3 Package: rhdf5client Version: 1.20.0 Depends: R (>= 3.6), methods, DelayedArray Imports: S4Vectors, httr, R6, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: 976fb4075570891731b3343caa40341f NeedsCompilation: yes Title: Access HDF5 content from h5serv Description: Provides functionality for reading data from h5serv server from within R. biocViews: DataImport, Software Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], BJ Stubbs [aut], Vincent Carey [cre, aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_16 git_last_commit: 1484405 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rhdf5client_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rhdf5client_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rhdf5client_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rhdf5client_1.20.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: restfulSE suggestsMe: BiocOncoTK, HumanTranscriptomeCompendium dependencyCount: 25 Package: rhdf5filters Version: 1.10.1 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), rhdf5 (>= 2.34.0) License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: c9ef1a9376a1a18798c7d21415ae44b5 NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of additional compression filters for HDF5 datasets. The package is intended to provide seemless integration with rhdf5, however the compiled filters can also be used with external applications. biocViews: Infrastructure, DataImport Author: Mike Smith [aut, cre] () Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5filters SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5filters git_url: https://git.bioconductor.org/packages/rhdf5filters git_branch: RELEASE_3_16 git_last_commit: ccf950c git_last_commit_date: 2023-03-24 Date/Publication: 2023-03-24 source.ver: src/contrib/rhdf5filters_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/rhdf5filters_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/rhdf5filters_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rhdf5filters_1.10.1.tgz vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html vignetteTitles: HDF5 Compression Filters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R importsMe: HDF5Array, rhdf5 dependencyCount: 1 Package: Rhdf5lib Version: 1.20.0 Depends: R (>= 4.0.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest, mockery License: Artistic-2.0 Archs: x64 MD5sum: 94c1bdfe7799bf8db8402e8a42746129 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith [ctb, cre] (), The HDF Group [cph] Maintainer: Mike Smith URL: https://github.com/grimbough/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: RELEASE_3_16 git_last_commit: 7606799 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rhdf5lib_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rhdf5lib_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rhdf5lib_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rhdf5lib_1.20.0.tgz vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: epigraHMM, rhdf5 suggestsMe: mbkmeans linksToMe: CytoML, DropletUtils, epigraHMM, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, ondisc dependencyCount: 0 Package: Rhisat2 Version: 1.14.0 Depends: R (>= 4.2) Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: be7b01b092f63b6edc3f7489faf7724d NeedsCompilation: yes Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues git_url: https://git.bioconductor.org/packages/Rhisat2 git_branch: RELEASE_3_16 git_last_commit: 7b73d3d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rhisat2_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rhisat2_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rhisat2_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rhisat2_1.14.0.tgz vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html vignetteTitles: Rhisat2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R importsMe: CircSeqAlignTk suggestsMe: eisaR, QuasR dependencyCount: 99 Package: Rhtslib Version: 2.0.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: knitr, rmarkdown, BiocStyle License: LGPL (>= 2) Archs: x64 MD5sum: 69bbda8208069ab4542f23cd6249bac8 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.15.1 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib/issues git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: RELEASE_3_16 git_last_commit: 1757333 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rhtslib_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rhtslib_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rhtslib_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rhtslib_2.0.0.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R importsMe: deepSNV, diffHic, maftools, mitoClone2, scPipe linksToMe: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV, DiffBind, diffHic, epialleleR, FLAMES, h5vc, maftools, methylKit, mitoClone2, podkat, qrqc, QuasR, Rfastp, Rsamtools, scPipe, seqbias, ShortRead, TransView, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboCrypt Version: 1.4.0 Depends: R (>= 3.6.0), ORFik (>= 1.13.12) Imports: BiocGenerics, BiocParallel, Biostrings, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, plotly, rlang Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE Archs: x64 MD5sum: 0d3d1f4e4f485395cf02e0774042f1ed NeedsCompilation: no Title: Interactive visualization in genomics Description: R Package for interactive visualization and browsing NGS data. It contains a browser for both transcript and genomic coordinate view. In addition a QC and general metaplots are included, among others differential translation plots and gene expression plots. The package is still under development. biocViews: Software, Sequencing, RiboSeq, RNASeq, Author: Michal Swirski [aut, cre], Haakon Tjeldnes [ctb] Maintainer: Michal Swirski URL: https://github.com/m-swirski/RiboCrypt VignetteBuilder: knitr BugReports: https://github.com/m-swirski/RiboCrypt/issues git_url: https://git.bioconductor.org/packages/RiboCrypt git_branch: RELEASE_3_16 git_last_commit: 0c32d79 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RiboCrypt_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RiboCrypt_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RiboCrypt_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RiboCrypt_1.4.0.tgz vignettes: vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.html vignetteTitles: RiboCrypt_overview.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RiboCrypt/inst/doc/RiboCrypt_overview.R dependencyCount: 156 Package: RiboDiPA Version: 1.6.0 Depends: R (>= 4.1), Rsamtools, GenomicFeatures, GenomicAlignments Imports: Rcpp (>= 1.0.2), graphics, stats, data.table, elitism, methods, S4Vectors, IRanges, GenomicRanges, matrixStats, reldist, doParallel, foreach, parallel, qvalue, DESeq2, ggplot2, BiocFileCache,BiocGenerics LinkingTo: Rcpp Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: e8256bc14cbb0443dcd9ded275652dae NeedsCompilation: yes Title: Differential pattern analysis for Ribo-seq data Description: This package performs differential pattern analysis for Ribo-seq data. It identifies genes with significantly different patterns in the ribosome footprint between two conditions. RiboDiPA contains five major components including bam file processing, P-site mapping, data binning, differential pattern analysis and footprint visualization. biocViews: RiboSeq, GeneExpression, GeneRegulation, DifferentialExpression, Sequencing, Coverage, Alignment, RNASeq, ImmunoOncology, QualityControl, DataImport, Software, Normalization Author: Keren Li [aut], Matt Hope [aut], Xiaozhong Wang [aut], Ji-Ping Wang [aut, cre] Maintainer: Ji-Ping Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboDiPA git_branch: RELEASE_3_16 git_last_commit: eeafa07 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RiboDiPA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RiboDiPA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RiboDiPA_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RiboDiPA_1.6.0.tgz vignettes: vignettes/RiboDiPA/inst/doc/RiboDiPA.html vignetteTitles: RiboDiPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboDiPA/inst/doc/RiboDiPA.R dependencyCount: 143 Package: RiboProfiling Version: 1.28.0 Depends: R (>= 3.5.0), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 Archs: x64 MD5sum: 69ebcf07adea387df2f6da033c6767cd NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: RELEASE_3_16 git_last_commit: 0553477 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RiboProfiling_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RiboProfiling_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RiboProfiling_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RiboProfiling_1.28.0.tgz vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R dependencyCount: 161 Package: ribor Version: 1.10.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 1d15ba1fb642daf684ac0402b4a7a041 NeedsCompilation: no Title: An R Interface for Ribo Files Description: The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents. biocViews: Software, Infrastructure Author: Michael Geng [cre, aut], Hakan Ozadam [aut], Can Cenik [aut] Maintainer: Michael Geng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: RELEASE_3_16 git_last_commit: a090395 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ribor_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ribor_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ribor_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ribor_1.10.0.tgz vignettes: vignettes/ribor/inst/doc/ribor.html vignetteTitles: A Walkthrough of RiboR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ribor/inst/doc/ribor.R dependencyCount: 52 Package: riboSeqR Version: 1.32.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 07d6999d956a9cdb78814ed0458593c4 NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: RELEASE_3_16 git_last_commit: b37a6c6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/riboSeqR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/riboSeqR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/riboSeqR_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/riboSeqR_1.32.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 40 Package: ribosomeProfilingQC Version: 1.10.0 Depends: R (>= 4.0), GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq, GenomicAlignments, GenomicFeatures, GenomeInfoDb, IRanges, methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread, S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils, cluster, stats, graphics, grid Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, edgeR, limma, testthat, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: b75d67094e8de9b6d1ce0fbf2482a7f7 NeedsCompilation: no Title: Ribosome Profiling Quality Control Description: Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis. biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl, Visualization, Coverage Author: Jianhong Ou [aut, cre] (), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC git_branch: RELEASE_3_16 git_last_commit: 1fb3d93 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ribosomeProfilingQC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ribosomeProfilingQC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ribosomeProfilingQC_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ribosomeProfilingQC_1.10.0.tgz vignettes: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html vignetteTitles: ribosomeProfilingQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R dependencyCount: 175 Package: rifi Version: 1.2.2 Depends: R (>= 4.2) Imports: car, cowplot, doMC, parallel, dplyr, egg, foreach, ggplot2, graphics, grDevices, grid, methods, nls2, nnet, rlang, S4Vectors, scales, stats, stringr, SummarizedExperiment, tibble, rtracklayer, reshape2, utils Suggests: DescTools, knitr, rmarkdown, BiocStyle License: GPL-3 + file LICENSE MD5sum: c592a86b788be8c0b3e2dccaf59e91a2 NeedsCompilation: no Title: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq Description: 'rifi' analyses data from rifampicin time series created by microarray or RNAseq. 'rifi' is a transcriptome data analysis tool for the holistic identification of transcription and decay associated processes. The decay constants and the delay of the onset of decay is fitted for each probe/bin. Subsequently, probes/bins of equal properties are combined into segments by dynamic programming, independent of a existing genome annotation. This allows to detect transcript segments of different stability or transcriptional events within one annotated gene. In addition to the classic decay constant/half-life analysis, 'rifi' detects processing sites, transcription pausing sites, internal transcription start sites in operons, sites of partial transcription termination in operons, identifies areas of likely transcriptional interference by the collision mechanism and gives an estimate of the transcription velocity. All data are integrated to give an estimate of continous transcriptional units, i.e. operons. Comprehensive output tables and visualizations of the full genome result and the individual fits for all probes/bins are produced. biocViews: RNASeq, DifferentialExpression, GeneRegulation, Transcriptomics, Regression, Microarray, Software Author: Loubna Youssar [aut, ctb], Walja Wanney [aut, ctb], Jens Georg [aut, cre] Maintainer: Jens Georg VignetteBuilder: knitr BugReports: https://github.com/CyanolabFreiburg/rifi git_url: https://git.bioconductor.org/packages/rifi git_branch: RELEASE_3_16 git_last_commit: 6aeba7e git_last_commit_date: 2023-03-03 Date/Publication: 2023-03-03 source.ver: src/contrib/rifi_1.2.2.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/rifi_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rifi_1.2.2.tgz vignettes: vignettes/rifi/inst/doc/vignette.html vignetteTitles: Rifi for decay estimation,, based on high resolution microarray or RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rifi/inst/doc/vignette.R dependencyCount: 125 Package: RImmPort Version: 1.26.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: d0f17c5ab1c9ea60aa96050ac97a43e1 NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: RELEASE_3_16 git_last_commit: a16bd83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RImmPort_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RImmPort_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RImmPort_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RImmPort_1.26.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 42 Package: Ringo Version: 1.62.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 MD5sum: 2e4a7a0c02ca130b32ff6913bd26b98f NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/Ringo git_branch: RELEASE_3_16 git_last_commit: 9070ce3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Ringo_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Ringo_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Ringo_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Ringo_1.62.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, ccTutorial importsMe: Repitools dependencyCount: 80 Package: RIPAT Version: 1.8.0 Depends: R (>= 4.0) Imports: biomaRt (>= 2.38.0), GenomicRanges (>= 1.34.0), ggplot2 (>= 3.1.0), grDevices (>= 3.5.3), IRanges (>= 2.16.0), karyoploteR (>= 1.6.3), openxlsx (>= 4.1.4), plyr (>= 1.8.4), regioneR (>= 1.12.0), rtracklayer (>= 1.42.2), stats (>= 3.5.3), stringr (>= 1.3.1), utils (>= 3.5.3) Suggests: knitr (>= 1.28) License: Artistic-2.0 Archs: x64 MD5sum: af75f0db8c39ffdd47c4bc2ff540dfdd NeedsCompilation: no Title: Retroviral Integration Pattern Analysis Tool (RIPAT) Description: RIPAT is developed as an R package for retroviral integration sites annotation and distribution analysis. RIPAT needs local alignment results from BLAST and BLAT. Specific input format is depicted in RIPAT manual. RIPAT provides RV integration pattern analysis result as forms of R objects, excel file with multiple sheets and plots. biocViews: Annotation Author: Min-Jeong Baek [aut, cre] Maintainer: Min-Jeong Baek URL: https://github.com/bioinfo16/RIPAT/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RIPAT git_branch: RELEASE_3_16 git_last_commit: 1bd9739 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RIPAT_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RIPAT_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RIPAT_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RIPAT_1.8.0.tgz vignettes: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.html vignetteTitles: RIPAT : Retroviral Integration Pattern Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.R dependencyCount: 154 Package: Risa Version: 1.40.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL MD5sum: f3a143d3d3d6227b470cb7250ad85b1d NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran URL: http://www.isa-tools.org/ BugReports: https://github.com/ISA-tools/Risa/issues git_url: https://git.bioconductor.org/packages/Risa git_branch: RELEASE_3_16 git_last_commit: 7449c90 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Risa_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Risa_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Risa_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Risa_1.40.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R suggestsMe: mtbls2 dependencyCount: 97 Package: RITAN Version: 1.22.0 Depends: R (>= 4.0), Imports: graphics, methods, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata, GenomicFeatures, ensembldb, AnnotationFilter, EnsDb.Hsapiens.v86 Suggests: rmarkdown, BgeeDB License: file LICENSE MD5sum: a8dcfeeb948815e71512c416e2ffde4d NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics, GraphAndNetwork Author: Michael Zimmermann [aut, cre] Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_16 git_last_commit: 17d6547 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RITAN_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RITAN_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RITAN_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RITAN_1.22.0.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R dependencyCount: 149 Package: RIVER Version: 1.22.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: d7592fc63617d3953faf25ee6e914898 NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: RELEASE_3_16 git_last_commit: d5090dd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RIVER_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RIVER_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RIVER_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RIVER_1.22.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 47 Package: RJMCMCNucleosomes Version: 1.22.0 Depends: R (>= 3.5.0), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 MD5sum: 13f0bef565919e760daeb34ac8c1ed77 NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: RELEASE_3_16 git_last_commit: afaad5b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RJMCMCNucleosomes_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RJMCMCNucleosomes_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RJMCMCNucleosomes_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RJMCMCNucleosomes_1.22.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 56 Package: RLassoCox Version: 1.6.0 Depends: R (>= 4.1), glmnet Imports: Matrix, igraph, survival, stats Suggests: knitr License: Artistic-2.0 Archs: x64 MD5sum: fdad9f6cddc138b8e40affbb5d1ab282 NeedsCompilation: no Title: A reweighted Lasso-Cox by integrating gene interaction information Description: RLassoCox is a package that implements the RLasso-Cox model proposed by Wei Liu. The RLasso-Cox model integrates gene interaction information into the Lasso-Cox model for accurate survival prediction and survival biomarker discovery. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. The RLasso-Cox model uses random walk to evaluate the topological weight of genes, and then highlights topologically important genes to improve the generalization ability of the Lasso-Cox model. The RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. biocViews: Survival, Regression, GeneExpression, GenePrediction, Network Author: Wei Liu [cre, aut] () Maintainer: Wei Liu VignetteBuilder: knitr BugReports: https://github.com/weiliu123/RLassoCox/issues git_url: https://git.bioconductor.org/packages/RLassoCox git_branch: RELEASE_3_16 git_last_commit: 12a1703 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RLassoCox_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RLassoCox_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RLassoCox_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RLassoCox_1.6.0.tgz vignettes: vignettes/RLassoCox/inst/doc/RLassoCox.pdf vignetteTitles: RLassoCox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLassoCox/inst/doc/RLassoCox.R dependencyCount: 22 Package: RLMM Version: 1.60.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: ba8d5ca06b8a03930c3adad02cb17d46 NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: RELEASE_3_16 git_last_commit: 7a39ffc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RLMM_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RLMM_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RLMM_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RLMM_1.60.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: RLSeq Version: 1.4.1 Depends: R (>= 4.2.0) Imports: dplyr, ggplot2, RColorBrewer, grid, regioneR, valr, caretEnsemble, GenomicFeatures, rtracklayer, GenomicRanges, GenomeInfoDb, ComplexHeatmap, AnnotationHub, VennDiagram, callr, circlize, ggplotify, ggprism, methods, stats, RLHub, aws.s3, pheatmap Suggests: AnnotationDbi, BiocStyle, covr, lintr, rcmdcheck, DT, httr, jsonlite, kableExtra, kernlab, knitr, magick, MASS, org.Hs.eg.db, R.utils, randomForest, readr, rmarkdown, rpart, testthat (>= 3.0.0), tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, futile.logger License: MIT + file LICENSE MD5sum: 574465cbee2a9a0f273cf22cf2144415 NeedsCompilation: no Title: RLSeq: An analysis package for R-loop mapping data Description: RLSeq is a toolkit for analyzing and evaluating R-loop mapping datasets. RLSeq serves two primary purposes: (1) to facilitate the evaluation of dataset quality, and (2) to enable R-loop analysis in the context of publicly-available data sets from RLBase. The package is intended to provide a simple pipeline, called with the `RLSeq()` function, which performs all main analyses. Individual functions are also accessible and provide custom analysis capabilities. Finally an HTML report is generated with `report()`. biocViews: Sequencing, Coverage, Epigenetics, Transcriptomics, Classification Author: Henry Miller [aut, cre, cph] (), Daniel Montemayor [ctb] (), Simon Levy [ctb] (), Anna Vines [ctb] (), Alexander Bishop [ths, cph] () Maintainer: Henry Miller URL: https://github.com/Bishop-Laboratory/RLSeq, https://bishop-laboratory.github.io/RLSeq/ VignetteBuilder: knitr BugReports: https://github.com/Bishop-Laboratory/RLSeq/issues git_url: https://git.bioconductor.org/packages/RLSeq git_branch: RELEASE_3_16 git_last_commit: 03ca877 git_last_commit_date: 2022-11-07 Date/Publication: 2022-11-07 source.ver: src/contrib/RLSeq_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RLSeq_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RLSeq_1.4.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RLSeq_1.4.1.tgz vignettes: vignettes/RLSeq/inst/doc/RLSeq.html vignetteTitles: Analyzing R-loop Data with RLSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RLSeq/inst/doc/RLSeq.R dependencyCount: 207 Package: Rmagpie Version: 1.54.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 2e46ad876812d2e0b10e5dd387948efb NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: RELEASE_3_16 git_last_commit: 1568237 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rmagpie_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rmagpie_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rmagpie_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rmagpie_1.54.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 19 Package: RMassBank Version: 3.8.0 Depends: Rcpp Imports: XML,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase,httr, enviPat,assertthat,logger,RCurl,readJDX,webchem, ChemmineR,ChemmineOB,R.utils,data.table Suggests: BiocStyle,gplots,RMassBankData (>= 1.33.1), xcms (>= 1.37.1), CAMERA, RUnit, knitr, rmarkdown License: Artistic-2.0 MD5sum: fb5e0c2bc7113a7cf707e01cb72bf83a NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: RELEASE_3_16 git_last_commit: 919937f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RMassBank_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RMassBank_3.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RMassBank_3.8.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.html, vignettes/RMassBank/inst/doc/RMassBankNonstandard.html, vignettes/RMassBank/inst/doc/RMassBankXCMS.html vignetteTitles: RMassBank: The workflow by example, RMassBank: Non-standard usage, RMassBank for XCMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R suggestsMe: RMassBankData dependencyCount: 142 Package: rmelting Version: 1.14.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.9-8) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: 61776c6d6e654ff8a8a430ea4e376135 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program (https://www.ebi.ac.uk/biomodels/tools/melting/) to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/rmelting/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: RELEASE_3_16 git_last_commit: 2d44b6e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rmelting_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rmelting_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rmelting_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rmelting_1.14.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: Rmmquant Version: 1.16.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 10a7f4e42f1fbfa2653ab72f748a4a97 NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: RELEASE_3_16 git_last_commit: ec5342d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rmmquant_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rmmquant_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rmmquant_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rmmquant_1.16.2.tgz vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 184 Package: rmspc Version: 1.4.0 Imports: processx, BiocManager, rtracklayer, stats, tools, methods, GenomicRanges, stringr Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 Archs: x64 MD5sum: 2ceb1a7afc515c44eda59089a935f064 NeedsCompilation: no Title: Multiple Sample Peak Calling Description: The rmspc package runs MSPC (Multiple Sample Peak Calling) software using R. The analysis of ChIP-seq samples outputs a number of enriched regions (commonly known as "peaks"), each indicating a protein-DNA interaction or a specific chromatin modification. When replicate samples are analyzed, overlapping peaks are expected. This repeated evidence can therefore be used to locally lower the minimum significance required to accept a peak. MSPC uses combined evidence from replicated experiments to evaluate peak calling output, rescuing peaks, and reduce false positives. It takes any number of replicates as input and improves sensitivity and specificity of peak calling on each, and identifies consensus regions between the input samples. biocViews: ChIPSeq, Sequencing, ChipOnChip, DataImport, RNASeq Author: Vahid Jalili [aut], Marzia Angela Cremona [aut], Fernando Palluzzi [aut], Meriem Bahda [aut, cre] Maintainer: Meriem Bahda URL: https://genometric.github.io/MSPC/ SystemRequirements: .NET 5.0 VignetteBuilder: knitr BugReports: https://github.com/Genometric/MSPC/issues git_url: https://git.bioconductor.org/packages/rmspc git_branch: RELEASE_3_16 git_last_commit: 81b6483 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rmspc_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rmspc_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rmspc_1.4.0.tgz vignettes: vignettes/rmspc/inst/doc/rmpsc.html vignetteTitles: User guide to the rmspc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rmspc/inst/doc/rmpsc.R dependencyCount: 58 Package: RNAAgeCalc Version: 1.10.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 36c3488fd5f1e60e51396f846f3e9ee9 NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: RELEASE_3_16 git_last_commit: 3d06c66 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAAgeCalc_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAAgeCalc_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAAgeCalc_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAAgeCalc_1.10.0.tgz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 167 Package: RNAdecay Version: 1.18.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: 6fc34db27bfa3adbeddc37f13b385431 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: RELEASE_3_16 git_last_commit: 3619ad8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAdecay_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAdecay_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAdecay_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAdecay_1.18.0.tgz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R dependencyCount: 63 Package: rnaEditr Version: 1.8.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: d1236593240da671fec255aebcebc538 NeedsCompilation: no Title: Statistical analysis of RNA editing sites and hyper-editing regions Description: RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models. biocViews: GeneTarget, Epigenetics, DimensionReduction, FeatureExtraction, Regression, Survival, RNASeq Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut] Maintainer: Lanyu Zhang URL: https://github.com/TransBioInfoLab/rnaEditr VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues git_url: https://git.bioconductor.org/packages/rnaEditr git_branch: RELEASE_3_16 git_last_commit: b3f5c8e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rnaEditr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rnaEditr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rnaEditr_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rnaEditr_1.8.0.tgz vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html vignetteTitles: Introduction to rnaEditr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R dependencyCount: 117 Package: RNAinteract Version: 1.46.0 Depends: R (>= 2.12.0), Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12), abind, locfit, Biobase License: Artistic-2.0 MD5sum: 8bb5a4719e28588198ef2d949444e15c NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer [aut], Wolfgang Huber [ctb], Mike Smith [cre] Maintainer: Mike Smith git_url: https://git.bioconductor.org/packages/RNAinteract git_branch: RELEASE_3_16 git_last_commit: 3aca0aa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAinteract_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAinteract_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAinteract_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAinteract_1.46.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R dependsOnMe: RNAinteractMAPK dependencyCount: 108 Package: RNAmodR Version: 1.12.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomicRanges, Modstrings Imports: methods, stats, grDevices, matrixStats, BiocGenerics, Biostrings (>= 2.57.2), BiocParallel, GenomicFeatures, GenomicAlignments, GenomeInfoDb, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0), reshape2, graphics, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 MD5sum: ffc590bdc29767d0f7e5b5214c4d51a6 NeedsCompilation: no Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues git_url: https://git.bioconductor.org/packages/RNAmodR git_branch: RELEASE_3_16 git_last_commit: 6cf3f83 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAmodR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAmodR_1.12.0.tgz vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html, vignettes/RNAmodR/inst/doc/RNAmodR.html vignetteTitles: RNAmodR - creating new classes for a new detection strategy, RNAmodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R, vignettes/RNAmodR/inst/doc/RNAmodR.R dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq dependencyCount: 162 Package: RNAmodR.AlkAnilineSeq Version: 1.12.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, RNAmodR.Data License: Artistic-2.0 MD5sum: c34aa61fbe750cf796b0efe49f7c4c96 NeedsCompilation: no Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq git_branch: RELEASE_3_16 git_last_commit: cf71467 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR.AlkAnilineSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR.AlkAnilineSeq_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAmodR.AlkAnilineSeq_1.12.0.tgz vignettes: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html vignetteTitles: RNAmodR.AlkAnilineSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R suggestsMe: RNAmodR.ML dependencyCount: 163 Package: RNAmodR.ML Version: 1.12.0 Depends: R (>= 3.6), RNAmodR Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 MD5sum: 328af6dc93c4fc57c1a9634bffc9644d NeedsCompilation: no Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues git_url: https://git.bioconductor.org/packages/RNAmodR.ML git_branch: RELEASE_3_16 git_last_commit: 3147377 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAmodR.ML_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR.ML_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR.ML_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAmodR.ML_1.12.0.tgz vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html vignetteTitles: RNAmodR.ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R dependencyCount: 164 Package: RNAmodR.RiboMethSeq Version: 1.12.0 Depends: R (>= 4.0), RNAmodR (>= 1.5.3) Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, RNAmodR.Data License: Artistic-2.0 MD5sum: 9330b608c5821c0e5adc5aa6f6ead134 NeedsCompilation: no Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq git_branch: RELEASE_3_16 git_last_commit: 7613515 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAmodR.RiboMethSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAmodR.RiboMethSeq_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAmodR.RiboMethSeq_1.12.0.tgz vignettes: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html vignetteTitles: RNAmodR.RiboMethSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R dependencyCount: 163 Package: RNAsense Version: 1.12.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f39a13936ffc9fd834f13ef73501ccc6 NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: RELEASE_3_16 git_last_commit: 296c1de git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNAsense_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNAsense_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNAsense_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNAsense_1.12.0.tgz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 60 Package: rnaseqcomp Version: 1.28.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 611658a8e336b5d26aff7894cb6139a1 NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: RELEASE_3_16 git_last_commit: 9fbc5f8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rnaseqcomp_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rnaseqcomp_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rnaseqcomp_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rnaseqcomp_1.28.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R suggestsMe: SummarizedBenchmark dependencyCount: 2 Package: RNASeqPower Version: 1.38.0 License: LGPL (>=2) MD5sum: fbfe16cd2fa94de1cf5e8697dc4b6641 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: RELEASE_3_16 git_last_commit: 505c98b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RNASeqPower_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RNASeqPower_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RNASeqPower_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNASeqPower_1.38.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R suggestsMe: DGEobj.utils dependencyCount: 0 Package: RNASeqR Version: 1.16.0 Depends: R(>= 3.5.0), ggplot2, pathview, edgeR, methods Imports: Rsamtools, tools, reticulate, ballgown, gridExtra, rafalib, FactoMineR, factoextra, corrplot, PerformanceAnalytics, reshape2, DESeq2, systemPipeR, systemPipeRdata, clusterProfiler, org.Hs.eg.db, org.Sc.sgd.db, stringr, pheatmap, grDevices, graphics, stats, utils, DOSE, Biostrings, parallel Suggests: knitr, rmarkdown, png, grid, RNASeqRData License: Artistic-2.0 MD5sum: 957128c7b72e128e679f361bb3a752ac NeedsCompilation: no Title: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow Description: This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: "RNASeqRParam S4 Object Creation", "Environment Setup", "Quality Assessment", "Reads Alignment & Quantification", "Gene-level Differential Analyses" and "Functional Analyses". Each step corresponds to a function in this package. After running functions in order, a basic RNASeq analysis would be done easily. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, GeneExpression, GeneSetEnrichment, Alignment, QualityControl, DifferentialExpression, FunctionalPrediction, ExperimentalDesign, GO, KEGG, Visualization, Normalization, Pathways, Clustering, ImmunoOncology Author: Kuan-Hao Chao Maintainer: Kuan-Hao Chao URL: https://github.com/HowardChao/RNASeqR SystemRequirements: RNASeqR only support Linux and macOS. Window is not supported. Python2 is highly recommended. If your machine is Python3, make sure '2to3' command is available. VignetteBuilder: knitr BugReports: https://github.com/HowardChao/RNASeqR/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/RNASeqR git_branch: RELEASE_3_16 git_last_commit: 0f6d741 git_last_commit_date: 2022-11-01 Date/Publication: 2023-03-20 source.ver: src/contrib/RNASeqR_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/RNASeqR_1.15.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RNASeqR_1.16.0.tgz vignettes: vignettes/RNASeqR/inst/doc/RNASeqR.html vignetteTitles: RNA-Seq analysis based on one independent variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqR/inst/doc/RNASeqR.R dependencyCount: 244 Package: RnaSeqSampleSize Version: 2.8.0 Depends: R (>= 4.0.0), ggplot2, RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, Rcpp (>= 0.11.2),recount,ggpubr,SummarizedExperiment,tidyr,dplyr,tidyselect,utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 258116bb27bdb8883257301a8be3a720 NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li [aut], Yan Guo [aut], Quanhu Sheng [aut], Yu Shyr [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: RELEASE_3_16 git_last_commit: 01c7c52 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RnaSeqSampleSize_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RnaSeqSampleSize_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RnaSeqSampleSize_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RnaSeqSampleSize_2.8.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 205 Package: RnBeads Version: 2.16.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, grid, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RnBeads.hg19, RnBeads.mm9, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, GLAD, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR, sesame License: GPL-3 Archs: x64 MD5sum: a589182da5a77954c6846027f88ae829 NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Christoph Bock [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller git_url: https://git.bioconductor.org/packages/RnBeads git_branch: RELEASE_3_16 git_last_commit: 84bc2eb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RnBeads_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RnBeads_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RnBeads_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RnBeads_2.16.0.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R dependsOnMe: MAGAR suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5 dependencyCount: 167 Package: roar Version: 1.34.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: ec309b2755416acedf1b81eb850ab866 NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: RELEASE_3_16 git_last_commit: 6a464d0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/roar_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/roar_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/roar_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/roar_1.34.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R dependencyCount: 46 Package: ROC Version: 1.74.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: rmarkdown, Biobase License: Artistic-2.0 MD5sum: 37c04dc7d0ac3571ff0da51098afb706 NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: RELEASE_3_16 git_last_commit: 982ad4d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ROC_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROC_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROC_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ROC_1.74.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst, rMisbeta suggestsMe: genefilter dependencyCount: 9 Package: ROCpAI Version: 1.10.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 351a488f137ff202e00c2bdba9838d54 NeedsCompilation: no Title: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers Description: The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. biocViews: Software, StatisticalMethod, Classification Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] Maintainer: Juan-Pedro Garcia VignetteBuilder: knitr BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues git_url: https://git.bioconductor.org/packages/ROCpAI git_branch: RELEASE_3_16 git_last_commit: 6db2cf3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ROCpAI_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROCpAI_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROCpAI_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ROCpAI_1.10.0.tgz vignettes: vignettes/ROCpAI/inst/doc/vignettes.html vignetteTitles: ROC Partial Area Indexes for evaluating classifiers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R dependencyCount: 32 Package: RolDE Version: 1.2.0 Depends: R (>= 4.2.0) Imports: stats, methods, ROTS, matrixStats, foreach, parallel, doParallel, doRNG, rngtools, SummarizedExperiment, nlme, qvalue, grDevices, graphics, utils Suggests: knitr, printr, rmarkdown, testthat License: GPL-3 MD5sum: 6def7800fe092da81bf592423207c43e NeedsCompilation: no Title: RolDE: Robust longitudinal Differential Expression Description: RolDE detects longitudinal differential expression between two conditions in noisy high-troughput data. Suitable even for data with a moderate amount of missing values.RolDE is a composite method, consisting of three independent modules with different approaches to detecting longitudinal differential expression. The combination of these diverse modules allows RolDE to robustly detect varying differences in longitudinal trends and expression levels in diverse data types and experimental settings. biocViews: StatisticalMethod, Software, TimeCourse, Regression, Proteomics, DifferentialExpression Author: Tommi Valikangas [cre, aut] Maintainer: Tommi Valikangas URL: https://github.com/elolab/RolDE VignetteBuilder: knitr BugReports: https://github.com/elolab/RolDE/issues git_url: https://git.bioconductor.org/packages/RolDE git_branch: RELEASE_3_16 git_last_commit: 0a1c12c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RolDE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RolDE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RolDE_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RolDE_1.2.0.tgz vignettes: vignettes/RolDE/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RolDE/inst/doc/Introduction.R dependencyCount: 67 Package: rols Version: 2.26.0 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: 89852d10678aa99632afb99b46fffefd NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto [aut, cre], Tiage Chedraoui Silva [ctb], Andrew Clugston [ctb] Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: RELEASE_3_16 git_last_commit: d67143d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rols_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rols_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rols_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rols_2.26.0.tgz vignettes: vignettes/rols/inst/doc/rols.html vignetteTitles: An R interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R importsMe: struct suggestsMe: MSnbase, spatialHeatmap, RforProteomics dependencyCount: 26 Package: ROntoTools Version: 2.26.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 9de3f2e7ac9c008025e214bd844038d3 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Calin Voichita git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: RELEASE_3_16 git_last_commit: e5a9ba3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ROntoTools_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROntoTools_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROntoTools_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ROntoTools_2.26.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA dependencyCount: 34 Package: ropls Version: 1.30.0 Depends: R (>= 3.5.0) Imports: Biobase, ggplot2, graphics, grDevices, methods, plotly, stats, MultiAssayExperiment, MultiDataSet, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL MD5sum: 99baf6dc9b10be1df94ebe46e2acde21 NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis (PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot [aut, cre] () Maintainer: Etienne A. Thevenot URL: https://doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_16 git_last_commit: 877d8bb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ropls_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ropls_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ropls_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ropls_1.30.0.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: ropls-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R importsMe: ASICS, biosigner, lipidr, MultiBaC, phenomis, proFIA suggestsMe: autonomics, ptairMS, structToolbox, MetabolomicsBasics dependencyCount: 102 Package: ROSeq Version: 1.10.0 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: fd91e8d895782c853968336bf6374d44 NeedsCompilation: no Title: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data Description: ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used. biocViews: GeneExpression, DifferentialExpression, SingleCell Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut] Maintainer: Krishan Gupta URL: https://github.com/krishan57gupta/ROSeq VignetteBuilder: knitr BugReports: https://github.com/krishan57gupta/ROSeq/issues git_url: https://git.bioconductor.org/packages/ROSeq git_branch: RELEASE_3_16 git_last_commit: a68c870 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ROSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROSeq_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ROSeq_1.10.0.tgz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 1.26.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) MD5sum: 7ef71e23c0d919a79729224717aa1617 NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: RELEASE_3_16 git_last_commit: 8bb45fe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ROTS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ROTS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ROTS_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ROTS_1.26.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA, RolDE suggestsMe: wrProteo dependencyCount: 7 Package: RPA Version: 1.54.0 Depends: R (>= 3.1.1), affy, BiocGenerics, BiocStyle, methods, rmarkdown Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE Archs: x64 MD5sum: 5e144f840d9841d93187a7ec787cf2f9 NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] () Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: RELEASE_3_16 git_last_commit: d1c8f29 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RPA_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RPA_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RPA_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RPA_1.54.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 104 Package: rprimer Version: 1.2.0 Depends: R (>= 4.1) Imports: Biostrings, bslib, DT, ggplot2, IRanges, mathjaxr, methods, patchwork, reshape2, S4Vectors, shiny, shinycssloaders, shinyFeedback Suggests: BiocStyle, covr, kableExtra, knitr, rmarkdown, styler, testthat (>= 3.0.0) License: GPL-3 MD5sum: d52e6257578aaac63532f55e367d4696 NeedsCompilation: no Title: Design Degenerate Oligos from a Multiple DNA Sequence Alignment Description: Functions, workflow, and a Shiny application for visualizing sequence conservation and designing degenerate primers, probes, and (RT)-(q/d)PCR assays from a multiple DNA sequence alignment. The results can be presented in data frame format and visualized as dashboard-like plots. For more information, please see the package vignette. biocViews: Alignment, ddPCR, Coverage, MultipleSequenceAlignment, SequenceMatching, qPCR Author: Sofia Persson [aut, cre] () Maintainer: Sofia Persson URL: https://github.com/sofpn/rprimer VignetteBuilder: knitr BugReports: https://github.com/sofpn/rprimer/issues git_url: https://git.bioconductor.org/packages/rprimer git_branch: RELEASE_3_16 git_last_commit: a6e365c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rprimer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rprimer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rprimer_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rprimer_1.2.0.tgz vignettes: vignettes/rprimer/inst/doc/getting-started-with-rprimer.html vignetteTitles: Instructions for use hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rprimer/inst/doc/getting-started-with-rprimer.R dependencyCount: 90 Package: RProtoBufLib Version: 2.10.0 Suggests: knitr, rmarkdown License: BSD_3_clause MD5sum: 260cc9ba12583b3113df8ee9b5b06958 NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: RELEASE_3_16 git_last_commit: 5fe7496 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RProtoBufLib_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RProtoBufLib_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RProtoBufLib_2.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RProtoBufLib_2.10.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: rpx Version: 2.6.3 Depends: methods Imports: BiocFileCache, jsonlite, xml2, RCurl, curl, utils Suggests: Biostrings, BiocStyle, testthat, knitr, rmarkdown License: GPL-2 Archs: x64 MD5sum: 09b83697e4bdeed8a629368fbadd6819 NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: RELEASE_3_16 git_last_commit: 7931689 git_last_commit_date: 2023-03-13 Date/Publication: 2023-03-13 source.ver: src/contrib/rpx_2.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/rpx_2.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/rpx_2.6.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rpx_2.6.3.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: MsExperiment, MSnbase, PSMatch, RforProteomics dependencyCount: 50 Package: Rqc Version: 1.32.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 4986a8d601200417d208aaba1b398eb7 NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: RELEASE_3_16 git_last_commit: 1691219 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rqc_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rqc_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rqc_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rqc_1.32.0.tgz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 168 Package: rqt Version: 1.24.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: x64 MD5sum: 6218b8c085842047b75de769eae67b14 NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer: Ilya Y. Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues git_url: https://git.bioconductor.org/packages/rqt git_branch: RELEASE_3_16 git_last_commit: 94d45f3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rqt_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rqt_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rqt_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rqt_1.24.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 160 Package: rqubic Version: 1.44.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 MD5sum: 6aef29ecd4392a4fb4fa992ce9873c35 NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Clustering Author: Jitao David Zhang [aut, cre, ctb] () Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: RELEASE_3_16 git_last_commit: 40affc2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rqubic_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rqubic_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rqubic_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rqubic_1.44.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R importsMe: miRSM suggestsMe: RcmdrPlugin.BiclustGUI dependencyCount: 53 Package: rRDP Version: 1.32.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE Archs: x64 MD5sum: 1ecfab12fa08878c4717417a6faa8a92 NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java git_url: https://git.bioconductor.org/packages/rRDP git_branch: RELEASE_3_16 git_last_commit: 9080407 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rRDP_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rRDP_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rRDP_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rRDP_1.32.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependsOnMe: rRDPData dependencyCount: 18 Package: RRHO Version: 1.38.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 8242c4e6e0a3584a5c89df78198d42a5 NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: RELEASE_3_16 git_last_commit: 7b7d2eb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RRHO_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RRHO_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RRHO_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RRHO_1.38.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 8 Package: rrvgo Version: 1.10.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods, umap Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), shinydashboard, DT, plotly, heatmaply, magrittr, utils, clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: 05c1018dae1e3a96cfb76a7d87c1a281 NeedsCompilation: no Title: Reduce + Visualize GO Description: Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. biocViews: Annotation, Clustering, GO, Network, Pathways, Software Author: Sergi Sayols [aut, cre] Maintainer: Sergi Sayols URL: https://www.bioconductor.org/packages/rrvgo, https://ssayols.github.io/rrvgo/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rrvgo git_branch: RELEASE_3_16 git_last_commit: 4b4bf52 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rrvgo_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rrvgo_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rrvgo_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rrvgo_1.10.0.tgz vignettes: vignettes/rrvgo/inst/doc/rrvgo.html vignetteTitles: Using rrvgo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R suggestsMe: genekitr dependencyCount: 109 Package: Rsamtools Version: 2.14.0 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Biostrings (>= 2.47.6), R (>= 3.5.0) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), zlibbioc, bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 1.99.3), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE MD5sum: 36dc928eb8aebb85ad4f2d0098c1daf6 NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan, Hervé Pagès, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q BugReports: https://github.com/Bioconductor/Rsamtools/issues git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: RELEASE_3_16 git_last_commit: 8302eb7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rsamtools_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rsamtools_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rsamtools_2.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rsamtools_2.14.0.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf vignetteTitles: An introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: ArrayExpressHTS, BitSeq, CODEX, contiBAIT, CoverageView, esATAC, exomeCopy, FRASER, GenomicAlignments, GenomicFiles, girafe, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, Rcade, RepViz, ReQON, RiboDiPA, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, spiky, ssviz, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, csawBook, Brundle importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, APAlyzer, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, ATACseqQC, ATACseqTFEA, atena, BadRegionFinder, bambu, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BRGenomics, BSgenome, CAGEr, casper, cellbaseR, CexoR, cfDNAPro, ChIC, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChromSCape, chromstaR, chromVAR, CircSeqAlignTk, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEXSeq, DiffBind, diffHic, DNAfusion, easyRNASeq, EDASeq, ensembldb, epigenomix, epigraHMM, eudysbiome, extraChIPs, FilterFFPE, FLAMES, FunChIP, gcapc, GeneGeneInteR, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, HTSeqGenie, icetea, IMAS, INSPEcT, karyoploteR, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylKit, MMAPPR2, mosaics, motifmatchr, msgbsR, NADfinder, NanoMethViz, nearBynding, nucleR, ORFik, panelcn.mops, PICS, plyranges, pram, profileplyr, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, ramwas, Rbowtie2, recoup, Repitools, rfPred, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, RNASeqR, Rqc, rtracklayer, scDblFinder, scPipe, scruff, segmentSeq, seqsetvis, SimFFPE, single, sitadela, soGGi, SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils, tracktables, trackViewer, transcriptR, TRESS, tRNAscanImport, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, VaSP, VCFArray, VplotR, chipseqDBData, LungCancerLines, MMAPPR2data, ExomeDepth, ggcoverage, hoardeR, kibior, MAAPER, NIPTeR, noisyr, PlasmaMutationDetector, PlasmaMutationDetector2, pulseTD, scPloidy, Signac, umiAnalyzer, VALERIE suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gwascat, IRanges, ldblock, MungeSumstats, omicsPrint, RNAmodR.ML, SeqArray, seqbias, SigFuge, similaRpeak, Streamer, TENxIO, GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE, systemPipeRdata, chipseqDB, polyRAD, seqmagick dependencyCount: 30 Package: rsbml Version: 2.56.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: x64 MD5sum: d79970f3e2ff24a8b86ade8814a3547b NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: RELEASE_3_16 git_last_commit: a084b19 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rsbml_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rsbml_2.56.0.zip vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR, seeds dependencyCount: 7 Package: rScudo Version: 1.14.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 Archs: x64 MD5sum: e64a20fc14cfdd102698fd0c29152efc NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: RELEASE_3_16 git_last_commit: 72303c6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rScudo_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rScudo_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rScudo_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rScudo_1.14.0.tgz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 36 Package: rsemmed Version: 1.8.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: b673de6bbcfac1b1d4fbf7b02f1d1967 NeedsCompilation: no Title: An interface to the Semantic MEDLINE database Description: A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths. biocViews: Software, Annotation, Pathways, SystemsBiology Author: Leslie Myint [aut, cre] () Maintainer: Leslie Myint URL: https://github.com/lmyint/rsemmed VignetteBuilder: knitr BugReports: https://github.com/lmyint/rsemmed/issues git_url: https://git.bioconductor.org/packages/rsemmed git_branch: RELEASE_3_16 git_last_commit: ac2c21b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rsemmed_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rsemmed_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rsemmed_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rsemmed_1.8.0.tgz vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html vignetteTitles: rsemmed User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R dependencyCount: 29 Package: RSeqAn Version: 1.18.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 83ebe7d9e05c064e3e89d03b6bcdeb87 NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: RELEASE_3_16 git_last_commit: d814f3f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RSeqAn_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RSeqAn_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RSeqAn_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RSeqAn_1.18.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R importsMe: qckitfastq linksToMe: qckitfastq dependencyCount: 3 Package: Rsubread Version: 2.12.3 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) MD5sum: 28dc31adea9d1f1d20f749d0a794fe9a NeedsCompilation: yes Title: Mapping, quantification and variant analysis of sequencing data Description: Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: RELEASE_3_16 git_last_commit: fef44d1 git_last_commit_date: 2023-03-01 Date/Publication: 2023-03-01 source.ver: src/contrib/Rsubread_2.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rsubread_2.12.3.zip mac.binary.ver: bin/macosx/contrib/4.2/Rsubread_2.12.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rsubread_2.12.3.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: APAlyzer, diffUTR, dupRadar, FRASER, ribosomeProfilingQC, scPipe, scruff suggestsMe: autonomics, icetea, NxtIRFcore, singleCellTK, SpliceWiz, tidybulk dependencyCount: 8 Package: RSVSim Version: 1.38.0 Depends: R (>= 3.5.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 Archs: x64 MD5sum: 32fdb23588f4acb8b31801a11eeb64fb NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: RELEASE_3_16 git_last_commit: 6fe2d5d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RSVSim_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RSVSim_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RSVSim_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RSVSim_1.38.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 51 Package: rSWeeP Version: 1.10.0 Depends: R (>= 4.0) Imports: pracma, stats Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 98fc57049c6c2bc16b69e85e51edb408 NeedsCompilation: no Title: Functions to creation of low dimensional comparative matrices of Amino Acid Sequence occurrences Description: The SWeeP method was developed to favor the analizes between amino acids sequences and to assist alignment free phylogenetic studies. This method is based on the concept of sparse words, which is applied in the scan of biological sequences and its the conversion in a matrix of ocurrences. Aiming the generation of low dimensional matrices of Amino Acid Sequence occurrences. biocViews: Software,StatisticalMethod,SupportVectorMachine,Technology,Sequencing,Genetics, Alignment Author: Danrley R. Fernandes [com, cre, aut] Maintainer: Danrley R. Fernandes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: RELEASE_3_16 git_last_commit: 53e35ff git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rSWeeP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rSWeeP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rSWeeP_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rSWeeP_1.10.0.tgz vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html vignetteTitles: rSWeeP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R dependencyCount: 5 Package: RTCA Version: 1.50.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 51dab9fdb0edf9e8ca093fe383231cd9 NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: RELEASE_3_16 git_last_commit: ee04c09 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RTCA_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTCA_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTCA_1.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RTCA_1.50.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 8 Package: RTCGA Version: 1.28.0 Depends: R (>= 3.3.0) Imports: XML, RCurl, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales, rmarkdown, htmltools Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, magrittr, tidyr License: GPL-2 MD5sum: 9982fa3d1f97e661060f8d10a05ec943 NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: ImmunoOncology, Software, DataImport, DataRepresentation, Preprocessing, RNASeq, Survival, DNAMethylation, PrincipalComponent, Visualization Author: Marcin Kosinski [aut, cre], Przemyslaw Biecek [ctb], Witold Chodor [ctb] Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues git_url: https://git.bioconductor.org/packages/RTCGA git_branch: RELEASE_3_16 git_last_commit: fbb6974 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RTCGA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTCGA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTCGA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RTCGA_1.28.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCGA/inst/doc/RTCGA_Workflow.R dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation, RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12, RTCGA.rnaseq, RTCGA.RPPA dependencyCount: 137 Package: RTCGAToolbox Version: 2.28.4 Depends: R (>= 3.5.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, grDevices, httr, limma, methods, RaggedExperiment, RCircos, RCurl, RJSONIO, rvest, S4Vectors (>= 0.23.10), stats, stringr, SummarizedExperiment, survival, TCGAutils (>= 1.9.4), utils, XML Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: GPL-2 MD5sum: 05395e523033c09ea86c6b375d56e228 NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre] (), Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues git_url: https://git.bioconductor.org/packages/RTCGAToolbox git_branch: RELEASE_3_16 git_last_commit: 64f8f78 git_last_commit_date: 2023-03-29 Date/Publication: 2023-03-29 source.ver: src/contrib/RTCGAToolbox_2.28.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTCGAToolbox_2.28.4.zip mac.binary.ver: bin/macosx/contrib/4.2/RTCGAToolbox_2.28.4.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: cBioPortalData suggestsMe: TCGAutils dependencyCount: 113 Package: RTN Version: 2.22.1 Depends: R (>= 3.6.3), methods, Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr, pheatmap, grDevices, graphics, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 1c964d15d6ab6de410e135b4565b8c84 NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: RELEASE_3_16 git_last_commit: 88ac54f git_last_commit_date: 2022-11-27 Date/Publication: 2022-11-28 source.ver: src/contrib/RTN_2.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTN_2.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RTN_2.22.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RTN_2.22.1.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b suggestsMe: geneplast dependencyCount: 142 Package: RTNduals Version: 1.22.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 757ed534ffd05606d306b8f97d3f0868 NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: RELEASE_3_16 git_last_commit: 83b4c06 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RTNduals_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTNduals_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTNduals_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RTNduals_1.22.0.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 143 Package: RTNsurvival Version: 1.22.0 Depends: R(>= 3.6.3), RTN(>= 2.14.1), RTNduals(>= 1.14.1), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 07e60983480d0e6ca9c4cd034e0b34d9 NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: RELEASE_3_16 git_last_commit: 7056888 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RTNsurvival_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTNsurvival_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RTNsurvival_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RTNsurvival_1.22.0.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 147 Package: RTopper Version: 1.44.1 Depends: R (>= 2.12.0), Biobase Imports: limma, multtest Suggests: org.Hs.eg.db, KEGGREST, GO.db License: GPL (>= 3) + file LICENSE MD5sum: 6bf77abed8eb267c2e7c0ad7d6d5f3d8 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: RELEASE_3_16 git_last_commit: 55eb68a git_last_commit_date: 2022-11-04 Date/Publication: 2022-11-06 source.ver: src/contrib/RTopper_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/RTopper_1.44.1.zip mac.binary.ver: bin/macosx/contrib/4.2/RTopper_1.44.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RTopper_1.44.1.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 16 Package: Rtpca Version: 1.8.0 Depends: R (>= 4.0.0), stats, dplyr, tidyr Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils, tibble Suggests: knitr, BiocStyle, TPP, testthat, rmarkdown License: GPL-3 MD5sum: c3428c44c312406599a5c76a589e78dc NeedsCompilation: no Title: Thermal proximity co-aggregation with R Description: R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rtpca git_branch: RELEASE_3_16 git_last_commit: 32664ea git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rtpca_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rtpca_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rtpca_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rtpca_1.8.0.tgz vignettes: vignettes/Rtpca/inst/doc/Rtpca.html vignetteTitles: Introduction to Rtpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R dependencyCount: 50 Package: rtracklayer Version: 1.58.0 Depends: R (>= 3.5.0), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), BiocIO, tools, restfulr (>= 0.0.13) LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE MD5sum: cc7175c537ed4a3007d2b34aa4b94078 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: RELEASE_3_16 git_last_commit: 54a7497 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rtracklayer_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rtracklayer_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rtracklayer_1.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rtracklayer_1.58.0.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BRGenomics, BSgenome, CAGEfightR, CoverageView, CSSQ, cummeRbund, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, IdeoViz, MethylSeekR, ORFhunteR, r3Cseq, StructuralVariantAnnotation, svaNUMT, svaRetro, EatonEtAlChIPseq, liftOver, sequencing, csawBook, OSCA.intro, HiCfeat importsMe: AnnotationHubData, annotatr, APAlyzer, ASpediaFI, ATACseqQC, ballgown, BgeeCall, BindingSiteFinder, biscuiteer, BiSeq, branchpointer, BSgenome, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, chromswitch, circRNAprofiler, cliProfiler, CNEr, coMET, compartmap, consensusSeekeR, contiBAIT, conumee, crisprDesign, customProDB, dasper, DeepBlueR, derfinder, DEScan2, diffHic, diffUTR, DMCFB, DMCHMM, dmrseq, ELMER, enhancerHomologSearch, enrichTF, ensembldb, EpiCompare, epidecodeR, epigraHMM, epimutacions, erma, esATAC, extraChIPs, factR, fcScan, FindIT2, FLAMES, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, gmapR, gmoviz, GOTHiC, GreyListChIP, Gviz, hiAnnotator, HiTC, HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, m6Aboost, MADSEQ, maser, MEDIPS, metagene, metagene2, metaseqR2, methrix, methylKit, motifbreakR, MotifDb, multicrispr, MungeSumstats, NADfinder, nearBynding, normr, NxtIRFcore, ODER, OGRE, OMICsPCA, ORFik, PAST, periodicDNA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, RCAS, recount, recount3, recoup, regioneR, REMP, Repitools, RGMQL, RiboProfiling, ribosomeProfilingQC, rifi, RIPAT, RLSeq, rmspc, RNAmodR, roar, scanMiRApp, scDblFinder, scPipe, scruff, seqCAT, seqsetvis, sevenC, SGSeq, shinyepico, signeR, SigsPack, sitadela, soGGi, SpliceWiz, srnadiff, syntenet, TFBSTools, trackViewer, transcriptR, TRESS, tRNAscanImport, txcutr, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, NxtIRFdata, spatialLIBD, SingscoreAMLMutations, crispRdesignR, GALLO, geneHapR, ggcoverage, kibior, PlasmaMutationDetector, PlasmaMutationDetector2, utr.annotation, valr suggestsMe: alpine, AnnotationHub, autonomics, BiocFileCache, biovizBase, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, crisprViz, DAMEfinder, eisaR, epistack, epivizrChart, epivizrData, geneXtendeR, GenomicAlignments, GenomicDistributions, GenomicInteractionNodes, GenomicRanges, goseq, gwascat, InPAS, interactiveDisplay, megadepth, methylumi, miRBaseConverter, MutationalPatterns, NanoMethViz, OrganismDbi, Pi, PICS, PING, pipeFrame, plotgardener, pqsfinder, ProteoDisco, R453Plus1Toolbox, RcisTarget, rGADEM, Ringo, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, similaRpeak, systemPipeR, TAPseq, TCGAutils, triplex, tRNAdbImport, TVTB, xcore, EpiTxDb.Hs.hg38, EpiTxDb.Sc.sacCer3, excluderanges, FDb.FANTOM4.promoters.hg19, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, systemPipeRdata, chipseqDB, gkmSVM, Rgff, RTIGER, Seurat, Signac, xQTLbiolinks dependencyCount: 45 Package: Rtreemix Version: 1.60.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: x64 MD5sum: 104d5e970842c5c547b00e5562760ecd NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska git_url: https://git.bioconductor.org/packages/Rtreemix git_branch: RELEASE_3_16 git_last_commit: dd0253e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Rtreemix_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Rtreemix_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Rtreemix_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Rtreemix_1.60.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R dependencyCount: 78 Package: rTRM Version: 1.36.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: methods, AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 34d53db5bbe458f222a39ce78d2196ed NeedsCompilation: no Title: Identification of Transcriptional Regulatory Modules from Protein-Protein Interaction Networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: RELEASE_3_16 git_last_commit: 76bbcd6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rTRM_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rTRM_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rTRM_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rTRM_1.36.0.tgz vignettes: vignettes/rTRM/inst/doc/Introduction.html vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/Introduction.R importsMe: rTRMui dependencyCount: 51 Package: rTRMui Version: 1.36.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: f262362f2b2ad7bdba8dcaf5f92341f3 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: RELEASE_3_16 git_last_commit: 8f4ce3c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/rTRMui_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/rTRMui_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/rTRMui_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rTRMui_1.36.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 99 Package: runibic Version: 1.20.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE MD5sum: 33bd9363ccd2927a388dd1ac9e7fe555 NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: RELEASE_3_16 git_last_commit: da49cd9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/runibic_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/runibic_1.20.0.zip vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: mosbi dependencyCount: 85 Package: RUVcorr Version: 1.30.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db, rmarkdown License: GPL-2 Archs: x64 MD5sum: 595e8b22a044dae0760130c9be28e474 NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: RELEASE_3_16 git_last_commit: 8d11567 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RUVcorr_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RUVcorr_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RUVcorr_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RUVcorr_1.30.0.tgz vignettes: vignettes/RUVcorr/inst/doc/Vignette.html vignetteTitles: Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R dependencyCount: 40 Package: RUVnormalize Version: 1.32.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 61e02f5e710c572ddaa8c252fb08f46b NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: RELEASE_3_16 git_last_commit: 90d765e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RUVnormalize_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RUVnormalize_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RUVnormalize_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RUVnormalize_1.32.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 7 Package: RUVSeq Version: 1.32.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 932b00f30b3650bc5d84d56bc4a228f3 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: RELEASE_3_16 git_last_commit: 800ee8e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RUVSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RUVSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RUVSeq_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RUVSeq_1.32.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R dependsOnMe: octad, rnaseqGene importsMe: consensusDE, ribosomeProfilingQC, scone, standR suggestsMe: DEScan2, NanoTube dependencyCount: 115 Package: RVS Version: 1.20.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 8593935963ecb3883df338330027ee8e NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Thomas Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_16 git_last_commit: c06c325 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/RVS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/RVS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/RVS_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/RVS_1.20.0.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 64 Package: rWikiPathways Version: 1.18.2 Imports: httr, utils, XML, rjson, data.table, tidyr, RCurl Suggests: testthat, BiocStyle, knitr, rmarkdown, BridgeDbR License: MIT + file LICENSE MD5sum: d130a072bfe0977e6ba0c72b78678cfe NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. It provides programmatic access to WikiPathways content in multiple data and image formats, including official monthly release files and convenient GMT read/write functions. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (), Alex Pico [aut] () Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rwikipathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rwikipathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: RELEASE_3_16 git_last_commit: 0dba45c git_last_commit_date: 2023-04-03 Date/Publication: 2023-04-03 source.ver: src/contrib/rWikiPathways_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/rWikiPathways_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/rWikiPathways_1.18.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/rWikiPathways_1.18.2.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R importsMe: famat, multiSight, TimiRGeN, RVA suggestsMe: TRONCO dependencyCount: 39 Package: S4Vectors Version: 0.36.2 Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>= 0.37.0) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle License: Artistic-2.0 MD5sum: 8404b1ea162990d9cccf7c7006805ad5 NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. 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proActiv, procoil, proDA, profileplyr, ProteoDisco, PureCN, PWMEnrich, qcmetrics, QFeatures, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RadioGx, RaggedExperiment, ramr, RareVariantVis, Rcade, RCAS, RcisTarget, RcwlPipelines, recount, recount3, recountmethylation, recoup, regioneR, regionReport, regsplice, regutools, REMP, Repitools, ResidualMatrix, restfulSE, rexposome, rfaRm, RGMQL, RgnTX, rGREAT, rhdf5client, RiboDiPA, RiboProfiling, ribor, ribosomeProfilingQC, rifi, RJMCMCNucleosomes, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, roar, rprimer, Rqc, Rsamtools, rScudo, RTCGAToolbox, RTN, rtracklayer, SC3, ScaledMatrix, scanMiR, scanMiRApp, SCArray, scater, scClassify, scDblFinder, scDD, scds, scHOT, scmap, scMerge, scMET, SCnorm, SCOPE, scp, scPipe, scran, scruff, scTensor, scTGIF, scTreeViz, scuttle, sechm, segmenter, SeqArray, seqCAT, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenbridges, sevenC, SGSeq, ShortRead, simpleSeg, SingleCellExperiment, singleCellTK, SingleR, singscore, sitadela, skewr, slingshot, SMITE, SNPhood, soGGi, SomaticSignatures, Spaniel, spatialDE, SpatialExperiment, SpatialFeatureExperiment, spatialHeatmap, spatzie, spicyR, spiky, splatter, SpliceWiz, SplicingGraphs, SPLINTER, SpotClean, sRACIPE, srnadiff, STAN, standR, strandCheckR, struct, StructuralVariantAnnotation, SummarizedExperiment, svaNUMT, svaRetro, SynExtend, systemPipeR, TAPseq, TarSeqQC, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TENxIO, terraTCGAdata, TFBSTools, TFHAZ, tidybulk, tidySingleCellExperiment, tidySummarizedExperiment, TileDBArray, TnT, ToxicoGx, trackViewer, tradeSeq, TrajectoryUtils, transcriptR, TransView, Trendy, tricycle, tRNA, tRNAdbImport, tRNAscanImport, TSCAN, tscR, TVTB, twoddpcr, txcutr, tximeta, Ularcirc, UMI4Cats, universalmotif, VanillaICE, VariantAnnotation, VariantFiltering, VaSP, VCFArray, VDJdive, velociraptor, Voyager, VplotR, wavClusteR, weitrix, wiggleplotr, xcms, xcore, XNAString, XVector, yamss, zellkonverter, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.v3.1.1.GRCh38, MafH5.gnomAD.v3.1.2.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP155.GRCh37, SNPlocs.Hsapiens.dbSNP155.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, celldex, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DropletTestFiles, FlowSorted.Blood.EPIC, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, imcdatasets, leeBamViews, MerfishData, MetaGxPancreas, MethylSeqData, MicrobiomeBenchmarkData, MouseGastrulationData, MouseThymusAgeing, pd.atdschip.tiling, scpdata, scRNAseq, sesameData, SimBenchData, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, tuberculosis, GeoMxWorkflows, ActiveDriverWGS, crispRdesignR, digitalDLSorteR, DR.SC, driveR, genBaRcode, geno2proteo, ggcoverage, hoardeR, IFAA, imcExperiment, LoopRig, microbial, MOCHA, NIPTeR, oncoPredict, PlasmaMutationDetector, PlasmaMutationDetector2, pulseTD, restfulr, rsolr, SC.MEB, SCRIP, scROSHI, Signac, toxpiR suggestsMe: AlpsNMR, BiocGenerics, conclus, dearseq, epivizrChart, globalSeq, GWASTools, GWENA, hca, maftools, martini, MicrobiotaProcess, MungeSumstats, RTCGA, SPOTlight, TFEA.ChIP, TFutils, traviz, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, BioPlex, curatedAdipoChIP, curatedAdipoRNA, ObMiTi, xcoredata, cancerTiming, gkmSVM, grandR, MARVEL, polyRAD, Rgff, rliger, Seurat, SNPassoc, updog, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 6 Package: safe Version: 3.38.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) Archs: x64 MD5sum: 89af07f9b5bf46d97acf85ce113a8843 NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/safe git_branch: RELEASE_3_16 git_last_commit: 03b8dee git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/safe_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/safe_3.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/safe_3.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/safe_3.38.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R dependsOnMe: PCGSE importsMe: EGSEA, EnrichmentBrowser dependencyCount: 47 Package: sagenhaft Version: 1.68.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: ea8d4058b38bc8388a60bd17dfbb9cb8 NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: RELEASE_3_16 git_last_commit: 7d26510 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sagenhaft_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sagenhaft_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sagenhaft_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sagenhaft_1.68.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAIGEgds Version: 1.12.5 Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.31.8), Rcpp Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics, SNPRelate, ggmanh License: GPL-3 MD5sum: 8b05a27488d6dbf35b8c6546fdcea65e NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the original SAIGE R package (v0.29.4.4 for single variant tests, Zhou et al. 2018). SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint approximation. Benchmarks show that SAIGEgds is 5 to 6 times faster than the original SAIGE R package. biocViews: Software, Genetics, StatisticalMethod, GenomeWideAssociation Author: Xiuwen Zheng [aut, cre] (), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: RELEASE_3_16 git_last_commit: af178c2 git_last_commit_date: 2023-03-23 Date/Publication: 2023-03-24 source.ver: src/contrib/SAIGEgds_1.12.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/SAIGEgds_1.12.5.zip mac.binary.ver: bin/macosx/contrib/4.2/SAIGEgds_1.12.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SAIGEgds_1.12.5.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 26 Package: sampleClassifier Version: 1.22.0 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 Archs: x64 MD5sum: d02fde62b8e98212b364660f574f98d3 NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify microarray RNA-seq gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El Amrani [aut, cre] Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: RELEASE_3_16 git_last_commit: 8322cf8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sampleClassifier_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sampleClassifier_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sampleClassifier_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sampleClassifier_1.22.0.tgz vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf vignetteTitles: sampleClassifier Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R dependencyCount: 94 Package: SamSPECTRAL Version: 1.52.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) MD5sum: 8273c067cf19811dc6df104dc9381072 NeedsCompilation: yes Title: Identifies cell population in flow cytometry data Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: RELEASE_3_16 git_last_commit: 8bd88a1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SamSPECTRAL_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SamSPECTRAL_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SamSPECTRAL_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SamSPECTRAL_1.52.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangeranalyseR Version: 1.8.0 Depends: R (>= 4.0.0), stringr, ape, Biostrings, DECIPHER, parallel, reshape2, phangorn, sangerseqR, gridExtra, shiny, shinydashboard, shinyjs, data.table, plotly, DT, zeallot, excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx, tools, rmarkdown (>= 2.9), knitr (>= 1.33), seqinr, BiocStyle, logger Suggests: testthat (>= 2.1.0) License: GPL-2 Archs: x64 MD5sum: 28357bef24c268e3fb785a0cf5892949 NeedsCompilation: no Title: sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R Description: This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms. biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing, QualityControl, Visualization, GUI Author: Rob Lanfear , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: RELEASE_3_16 git_last_commit: b7ae102 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sangeranalyseR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sangeranalyseR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sangeranalyseR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sangeranalyseR_1.8.0.tgz vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html vignetteTitles: sangeranalyseR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R dependencyCount: 135 Package: sangerseqR Version: 1.34.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: bf4800712ffbeef61f60ae2df0b71533 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: RELEASE_3_16 git_last_commit: 76df034 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sangerseqR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sangerseqR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sangerseqR_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sangerseqR_1.34.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R dependsOnMe: sangeranalyseR importsMe: scifer suggestsMe: CrispRVariants, bold dependencyCount: 48 Package: SANTA Version: 2.34.0 Depends: R (>= 4.1), igraph Imports: graphics, Matrix, methods, stats Suggests: BiocGenerics, BioNet, DLBCL, formatR, knitr, msm, org.Sc.sgd.db, markdown, rmarkdown, RUnit License: GPL (>= 2) MD5sum: 06e328d9fc0df2335639f7513b0e0a92 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex Cornish [cre, aut] Maintainer: Alex Cornish VignetteBuilder: knitr BugReports: https://github.com/alexjcornish/SANTA git_url: https://git.bioconductor.org/packages/SANTA git_branch: RELEASE_3_16 git_last_commit: 827739a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SANTA_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SANTA_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SANTA_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SANTA_2.34.0.tgz vignettes: vignettes/SANTA/inst/doc/SANTA-vignette.html vignetteTitles: Introduction to SANTA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R dependencyCount: 13 Package: sarks Version: 1.10.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE Archs: x64 MD5sum: 0f9c54fbc3b40146633f981a1ead23d9 NeedsCompilation: no Title: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains Description: Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. biocViews: MotifDiscovery, GeneRegulation, GeneExpression, Transcriptomics, RNASeq, DifferentialExpression, FeatureExtraction Author: Dennis Wylie [aut, cre] () Maintainer: Dennis Wylie URL: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797, https://github.com/denniscwylie/sarks SystemRequirements: Java (>= 1.8) BugReports: https://github.com/denniscwylie/sarks/issues git_url: https://git.bioconductor.org/packages/sarks git_branch: RELEASE_3_16 git_last_commit: 7fb1fdc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sarks_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sarks_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sarks_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sarks_1.10.0.tgz vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf vignetteTitles: sarks-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R dependencyCount: 21 Package: satuRn Version: 1.6.0 Depends: R (>= 4.1) Imports: locfdr, SummarizedExperiment, BiocParallel, limma, pbapply, ggplot2, boot, Matrix, stats, methods, graphics Suggests: knitr, rmarkdown, testthat, covr, BiocStyle, AnnotationHub, ensembldb, edgeR, DEXSeq, stageR, DelayedArray License: Artistic-2.0 MD5sum: e17ff9c2780acf0e150510c519185bd9 NeedsCompilation: no Title: Scalable Analysis of Differential Transcript Usage for Bulk and Single-Cell RNA-sequencing Applications Description: satuRn provides a higly performant and scalable framework for performing differential transcript usage analyses. The package consists of three main functions. The first function, fitDTU, fits quasi-binomial generalized linear models that model transcript usage in different groups of interest. The second function, testDTU, tests for differential usage of transcripts between groups of interest. Finally, plotDTU visualizes the usage profiles of transcripts in groups of interest. biocViews: Regression, ExperimentalDesign, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Jeroen Gilis [aut, cre], Kristoffer Vitting-Seerup [ctb], Koen Van den Berge [ctb], Lieven Clement [ctb] Maintainer: Jeroen Gilis URL: https://github.com/statOmics/satuRn VignetteBuilder: knitr BugReports: https://github.com/statOmics/satuRn/issues git_url: https://git.bioconductor.org/packages/satuRn git_branch: RELEASE_3_16 git_last_commit: 5970d80 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/satuRn_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/satuRn_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/satuRn_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/satuRn_1.6.0.tgz vignettes: vignettes/satuRn/inst/doc/Vignette.html vignetteTitles: satuRn - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/satuRn/inst/doc/Vignette.R suggestsMe: SpliceWiz dependencyCount: 66 Package: savR Version: 1.36.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: 628e369b6055c8d7993a6a2ca4580b1f NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues git_url: https://git.bioconductor.org/packages/savR git_branch: RELEASE_3_16 git_last_commit: c156273 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/savR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/savR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/savR_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/savR_1.36.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R dependencyCount: 43 Package: SBGNview Version: 1.12.0 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 MD5sum: 2a2721414eb58d11e81d0330ca17cae5 NeedsCompilation: no Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways" Description: SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: RELEASE_3_16 git_last_commit: 688ec84 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SBGNview_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SBGNview_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SBGNview_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SBGNview_1.12.0.tgz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.quick.start.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, Quick start SBGNview, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.quick.start.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 87 Package: SBMLR Version: 1.94.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: ffac41d6dd902f2c065f1abac94d2671 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: RELEASE_3_16 git_last_commit: ccb65ab git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SBMLR_1.94.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SBMLR_1.94.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SBMLR_1.94.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SBMLR_1.94.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.26.2 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater License: GPL-3 MD5sum: 493d9d7064adf3fbe66f1eb266cf7c71 NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: RELEASE_3_16 git_last_commit: 7d40f24 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/SC3_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/SC3_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.2/SC3_1.26.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SC3_1.26.2.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SC3/inst/doc/SC3.R importsMe: FEAST suggestsMe: InteractiveComplexHeatmap, scTreeViz, VAExprs dependencyCount: 101 Package: Scale4C Version: 1.20.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 Archs: x64 MD5sum: 9fe42c4d99dc78c9c4d45e64717e53c9 NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: RELEASE_3_16 git_last_commit: 605e85f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Scale4C_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Scale4C_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Scale4C_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Scale4C_1.20.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 26 Package: ScaledMatrix Version: 1.6.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 9c1086352d5749b4c27da04ca7530c08 NeedsCompilation: no Title: Creating a DelayedMatrix of Scaled and Centered Values Description: Provides delayed computation of a matrix of scaled and centered values. The result is equivalent to using the scale() function but avoids explicit realization of a dense matrix during block processing. This permits greater efficiency in common operations, most notably matrix multiplication. biocViews: Software, DataRepresentation Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ScaledMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ScaledMatrix/issues git_url: https://git.bioconductor.org/packages/ScaledMatrix git_branch: RELEASE_3_16 git_last_commit: 45a29d3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ScaledMatrix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ScaledMatrix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ScaledMatrix_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ScaledMatrix_1.6.0.tgz vignettes: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.html vignetteTitles: Using the ScaledMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScaledMatrix/inst/doc/ScaledMatrix.R importsMe: batchelor, BiocSingular, mumosa, scPCA suggestsMe: scran dependencyCount: 15 Package: scAlign Version: 1.12.0 Depends: R (>= 3.6), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4), tensorflow, purrr, irlba, Rtsne, ggplot2, methods, utils, FNN Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 262b37c6fe85922c8770a2f87bfd10c4 NeedsCompilation: no Title: An alignment and integration method for single cell genomics Description: An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) for more details. biocViews: SingleCell, Transcriptomics, DimensionReduction, NeuralNetwork Author: Nelson Johansen [aut, cre], Gerald Quon [aut] Maintainer: Nelson Johansen URL: https://github.com/quon-titative-biology/scAlign SystemRequirements: python (< 3.7), tensorflow VignetteBuilder: knitr BugReports: https://github.com/quon-titative-biology/scAlign/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/scAlign git_branch: RELEASE_3_16 git_last_commit: b875098 git_last_commit_date: 2022-11-01 Date/Publication: 2023-03-20 source.ver: src/contrib/scAlign_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scAlign_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scAlign_1.11.0.tgz vignettes: vignettes/scAlign/inst/doc/scAlign.pdf vignetteTitles: alignment_tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAlign/inst/doc/scAlign.R dependencyCount: 174 Package: SCAN.UPC Version: 2.40.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: 11dce837faafc3d6488941f01647b95f NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: RELEASE_3_16 git_last_commit: 56e8db8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCAN.UPC_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCAN.UPC_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCAN.UPC_2.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCAN.UPC_2.40.0.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 113 Package: scanMiR Version: 1.4.0 Depends: R (>= 4.0) Imports: Biostrings, GenomicRanges, IRanges, data.table, BiocParallel, methods, GenomeInfoDb, S4Vectors, ggplot2, stats, stringi, utils, graphics, grid, ggseqlogo, cowplot Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0) License: GPL-3 MD5sum: 10c7b2b46c461f673dfb8fc51e8f1b86 NeedsCompilation: no Title: scanMiR Description: A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment). biocViews: miRNA, SequenceMatching, Alignment Author: Pierre-Luc Germain [aut] (), Michael Soutschek [aut], Fridolin Gross [cre, aut] Maintainer: Fridolin Gross VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiR git_branch: RELEASE_3_16 git_last_commit: 71782bb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scanMiR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scanMiR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scanMiR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scanMiR_1.4.0.tgz vignettes: vignettes/scanMiR/inst/doc/Kdmodels.html, vignettes/scanMiR/inst/doc/scanning.html vignetteTitles: 2_Kdmodels, 1_scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scanMiR/inst/doc/Kdmodels.R, vignettes/scanMiR/inst/doc/scanning.R importsMe: scanMiRApp, scanMiRData dependencyCount: 63 Package: scanMiRApp Version: 1.4.0 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationFilter, AnnotationHub, BiocParallel, Biostrings, data.table, digest, DT, ensembldb, fst, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, htmlwidgets, IRanges, Matrix, methods, plotly, rintrojs, rtracklayer, S4Vectors, scanMiR, scanMiRData, shiny, shinycssloaders, shinydashboard, shinyjqui, stats, utils, waiter Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 3.0.0), shinytest, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-3 MD5sum: a25f3f901dc236e49ff66f10d81cc6ad NeedsCompilation: no Title: scanMiR shiny application Description: A shiny interface to the scanMiR package. The application enables the scanning of transcripts and custom sequences for miRNA binding sites, the visualization of KdModels and binding results, as well as browsing predicted repression data. In addition contains the IndexedFst class for fast indexed reading of large GenomicRanges or data.frames, and some utilities for facilitating scans and identifying enriched miRNA-target pairs. biocViews: miRNA, SequenceMatching, GUI, ShinyApps Author: Pierre-Luc Germain [cre, aut] (), Michael Soutschek [aut], Fridolin Gross [ctb] Maintainer: Pierre-Luc Germain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scanMiRApp git_branch: RELEASE_3_16 git_last_commit: e15546d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scanMiRApp_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scanMiRApp_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scanMiRApp_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scanMiRApp_1.4.0.tgz vignettes: vignettes/scanMiRApp/inst/doc/IndexedFST.html, vignettes/scanMiRApp/inst/doc/scanMiRApp.html vignetteTitles: IndexedFst, scanMiRApp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scanMiRApp/inst/doc/IndexedFST.R, vignettes/scanMiRApp/inst/doc/scanMiRApp.R dependencyCount: 156 Package: scAnnotatR Version: 1.4.0 Depends: R (>= 4.1), Seurat, SingleCellExperiment, SummarizedExperiment Imports: dplyr, ggplot2, caret, ROCR, pROC, data.tree, methods, stats, e1071, ape, kernlab, AnnotationHub, utils Suggests: knitr, rmarkdown, scRNAseq, testthat License: MIT + file LICENSE MD5sum: d9eb50d9e2d6ca4390e2524749a05073 NeedsCompilation: no Title: Pretrained learning models for cell type prediction on single cell RNA-sequencing data Description: The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs. biocViews: SingleCell, Transcriptomics, GeneExpression, SupportVectorMachine, Classification, Software Author: Vy Nguyen [aut] (), Johannes Griss [cre] () Maintainer: Johannes Griss URL: https://github.com/grisslab/scAnnotatR VignetteBuilder: knitr BugReports: https://github.com/grisslab/scAnnotatR/issues/new git_url: https://git.bioconductor.org/packages/scAnnotatR git_branch: RELEASE_3_16 git_last_commit: 90e7357 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scAnnotatR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scAnnotatR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scAnnotatR_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scAnnotatR_1.4.0.tgz vignettes: vignettes/scAnnotatR/inst/doc/classifying-cells.html, vignettes/scAnnotatR/inst/doc/training-basic-model.html, vignettes/scAnnotatR/inst/doc/training-child-model.html vignetteTitles: 1. Introduction to scAnnotatR, 2. Training basic model, 3. Training child model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAnnotatR/inst/doc/classifying-cells.R, vignettes/scAnnotatR/inst/doc/training-basic-model.R, vignettes/scAnnotatR/inst/doc/training-child-model.R suggestsMe: scAnnotatR.models dependencyCount: 210 Package: SCANVIS Version: 1.12.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: c078206af574aa9ea18fb511118029a1 NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: RELEASE_3_16 git_last_commit: 73e6171 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCANVIS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCANVIS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCANVIS_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCANVIS_1.12.0.tgz vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 47 Package: SCArray Version: 1.6.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.27.4), methods, DelayedArray (>= 0.16.0) Imports: BiocGenerics, S4Vectors, IRanges, utils, SummarizedExperiment, SingleCellExperiment, DelayedMatrixStats Suggests: Matrix, scater, uwot, RUnit, knitr, markdown, rmarkdown, rhdf5, HDF5Array License: GPL-3 MD5sum: bb0483dd6621792829b6f738f88cee74 NeedsCompilation: yes Title: Large-scale single-cell RNA-seq data manipulation with GDS files Description: Provides large-scale single-cell RNA-seq data manipulation using Genomic Data Structure (GDS) files. It combines dense and sparse matrices stored in GDS files and the Bioconductor infrastructure framework (SingleCellExperiment and DelayedArray) to provide out-of-memory data storage and large-scale manipulation using the R programming language. biocViews: Infrastructure, DataRepresentation, DataImport, SingleCell, RNASeq Author: Xiuwen Zheng [aut, cre] () Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SCArray VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCArray git_branch: RELEASE_3_16 git_last_commit: 56c7925 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCArray_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCArray_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCArray_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCArray_1.6.0.tgz vignettes: vignettes/SCArray/inst/doc/Overview.html, vignettes/SCArray/inst/doc/SCArray.html vignetteTitles: Overview, Single-cell RNA-seq data manipulation using GDS files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCArray/inst/doc/SCArray.R dependencyCount: 30 Package: SCATE Version: 1.8.0 Depends: parallel, preprocessCore, splines, splines2, xgboost, SCATEData, Rtsne, mclust Imports: utils, stats, GenomicAlignments, GenomicRanges Suggests: rmarkdown, ggplot2, knitr License: MIT + file LICENSE Archs: x64 MD5sum: 29e007e8c886e37e62af26fc3267e20f NeedsCompilation: no Title: SCATE: Single-cell ATAC-seq Signal Extraction and Enhancement Description: SCATE is a software tool for extracting and enhancing the sparse and discrete Single-cell ATAC-seq Signal. Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. SCATE was developed to adaptively integrate information from co-activated CREs, similar cells, and publicly available regulome data and substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample. biocViews: ExperimentHub, ExperimentData, Genome, SequencingData, SingleCellData, SNPData Author: Zhicheng Ji [aut], Weiqiang Zhou [aut], Wenpin Hou [cre, aut] (), Hongkai Ji [aut] Maintainer: Wenpin Hou VignetteBuilder: knitr BugReports: https://github.com/Winnie09/SCATE/issues git_url: https://git.bioconductor.org/packages/SCATE git_branch: RELEASE_3_16 git_last_commit: 6864cc6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCATE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCATE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCATE_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCATE_1.8.0.tgz vignettes: vignettes/SCATE/inst/doc/SCATE.html vignetteTitles: 1. SCATE package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCATE/inst/doc/SCATE.R dependencyCount: 126 Package: scater Version: 1.26.1 Depends: SingleCellExperiment, scuttle, ggplot2 Imports: stats, utils, methods, grid, gridExtra, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat, BiocNeighbors, BiocSingular, BiocParallel, rlang, ggbeeswarm, viridis, Rtsne, RColorBrewer, RcppML, uwot, pheatmap, ggrastr, ggrepel Suggests: BiocStyle, snifter, cowplot, biomaRt, knitr, scRNAseq, robustbase, rmarkdown, testthat, Biobase License: GPL-3 MD5sum: 49a00d8296149e57f67e5d12856c1289 NeedsCompilation: no Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Felix G.M. Ernst [ctb], Alan O'Callaghan [ctb, cre] Maintainer: Alan O'Callaghan URL: http://bioconductor.org/packages/scater/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: RELEASE_3_16 git_last_commit: d73b6c0 git_last_commit_date: 2022-11-13 Date/Publication: 2022-11-13 source.ver: src/contrib/scater_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/scater_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/scater_1.26.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scater_1.26.1.tgz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: netSmooth, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: airpart, BayesSpace, CATALYST, celda, CelliD, CellMixS, ChromSCape, conclus, distinct, FLAMES, IRISFGM, mia, miaViz, muscat, peco, pipeComp, scDblFinder, scTreeViz, singleCellTK, Spaniel, spatialHeatmap, splatter, tricycle, VAExprs, spatialLIBD, PRECAST, SC.MEB suggestsMe: APL, batchelor, bluster, ccImpute, CellaRepertorium, CellTrails, Cepo, CiteFuse, corral, dittoSeq, ExperimentSubset, fcoex, ggspavis, Glimma, InteractiveComplexHeatmap, iSEE, iSEEhex, iSEEu, M3Drop, MAST, mbkmeans, miloR, miQC, monocle, MuData, mumosa, Nebulosa, netDx, SC3, SCArray, scds, schex, scHOT, scMerge, scone, scp, scPipe, scran, scRepertoire, SingleR, slalom, SPOTlight, standR, SummarizedBenchmark, tidySingleCellExperiment, traviz, UCell, velociraptor, Voyager, waddR, curatedMetagenomicData, DuoClustering2018, HCAData, muscData, SingleCellMultiModal, TabulaMurisData, tuberculosis, simpleSingleCell, scellpam dependencyCount: 96 Package: scatterHatch Version: 1.4.0 Depends: R (>= 4.1) Imports: grid, ggplot2, plyr, spatstat.geom, stats, grDevices Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 4850280493256b81c291984068018c24 NeedsCompilation: no Title: Creates hatched patterns for scatterplots Description: The objective of this package is to efficiently create scatterplots where groups can be distinguished by color and texture. Visualizations in computational biology tend to have many groups making it difficult to distinguish between groups solely on color. Thus, this package is useful for increasing the accessibility of scatterplot visualizations to those with visual impairments such as color blindness. biocViews: Visualization, SingleCell, CellBiology, Software, Spatial Author: Atul Deshpande [aut, cre] () Maintainer: Atul Deshpande URL: https://github.com/FertigLab/scatterHatch VignetteBuilder: knitr BugReports: https://github.com/FertigLab/scatterHatch/issues git_url: https://git.bioconductor.org/packages/scatterHatch git_branch: RELEASE_3_16 git_last_commit: dc90e5d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scatterHatch_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scatterHatch_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scatterHatch_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scatterHatch_1.4.0.tgz vignettes: vignettes/scatterHatch/inst/doc/vignette.html vignetteTitles: Creating a Scatterplot with Texture hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scatterHatch/inst/doc/vignette.R dependencyCount: 42 Package: scBFA Version: 1.12.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods, Matrix Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 + file LICENSE MD5sum: 25e6092c94496ee9fac7f79cfd649202 NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: RELEASE_3_16 git_last_commit: 432b484 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scBFA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scBFA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scBFA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scBFA_1.12.0.tgz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 198 Package: SCBN Version: 1.16.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown License: GPL-2 MD5sum: ed522c3ece872f6319cde92a9f972689 NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: RELEASE_3_16 git_last_commit: ed68a6c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCBN_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCBN_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCBN_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCBN_1.16.0.tgz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R importsMe: TEKRABber dependencyCount: 1 Package: scBubbletree Version: 1.0.0 Depends: R (>= 4.2.0) Imports: reshape2, future, future.apply, ape, scales, Seurat, ggplot2, ggtree, patchwork, methods, stats, base, utils Suggests: BiocStyle, knitr, testthat, cluster, SingleCellExperiment License: GPL-3 + file LICENSE MD5sum: 7a2c3b3070f5d5b4badf7544f155f087 NeedsCompilation: no Title: Quantitative visual exploration of scRNA-seq data Description: scBubbletree is a quantitative method for visual exploration of scRNA-seq data. It preserves biologically meaningful properties of scRNA-seq data, such as local and global cell distances, as well as the density distribution of cells across the sample. scBubbletree is scalable and avoids the overplotting problem, and is able to visualize diverse cell attributes derived from multiomic single-cell experiments. Importantly, Importantly, scBubbletree is easy to use and to integrate with popular approaches for scRNA-seq data analysis. biocViews: Visualization,Clustering, SingleCell,Transcriptomics,RNASeq Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski URL: https://github.com/snaketron/scBubbletree SystemRequirements: Python (>= 3.6), leidenalg (>= 0.8.2) VignetteBuilder: knitr BugReports: https://github.com/snaketron/scBubbletree/issues git_url: https://git.bioconductor.org/packages/scBubbletree git_branch: RELEASE_3_16 git_last_commit: eef9db9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scBubbletree_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scBubbletree_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scBubbletree_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scBubbletree_1.0.0.tgz vignettes: vignettes/scBubbletree/inst/doc/User_manual.html vignetteTitles: User Manual: scBubbletree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBubbletree/inst/doc/User_manual.R dependencyCount: 160 Package: scCB2 Version: 1.8.0 Depends: R (>= 3.6.0) Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods, utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel, iterators, foreach, Seurat Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 7512414d449023981782f36e41b20cca NeedsCompilation: yes Title: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data Description: scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled. biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Preprocessing, Clustering Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/scCB2 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/zijianni/scCB2/issues git_url: https://git.bioconductor.org/packages/scCB2 git_branch: RELEASE_3_16 git_last_commit: e5f084d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scCB2_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scCB2_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scCB2_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scCB2_1.8.0.tgz vignettes: vignettes/scCB2/inst/doc/scCB2.html vignetteTitles: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scCB2/inst/doc/scCB2.R dependencyCount: 189 Package: scClassify Version: 1.10.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod, Cepo Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: 011aa5058d6ebafd4f6e63a5deb383be NeedsCompilation: no Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues git_url: https://git.bioconductor.org/packages/scClassify git_branch: RELEASE_3_16 git_last_commit: 4f45f5a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scClassify_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scClassify_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scClassify_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scClassify_1.10.0.tgz vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html, vignettes/scClassify/inst/doc/scClassify.html vignetteTitles: pretrainedModel, scClassify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R, vignettes/scClassify/inst/doc/scClassify.R dependencyCount: 157 Package: sccomp Version: 1.2.1 Depends: R (>= 4.2.0) Imports: methods, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rstantools (>= 2.1.1), rstan (>= 2.18.1), SeuratObject, SingleCellExperiment, parallel, dplyr, tidyr, purrr, magrittr, rlang, tibble, boot, lifecycle, stats, tidyselect, utils, ggplot2, ggrepel, patchwork, forcats, readr, scales, stringr, glue LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) Suggests: BiocStyle, testthat (>= 3.0.0), markdown, knitr, tidyseurat, tidySingleCellExperiment, loo Enhances: furrr, extraDistr License: GPL-3 MD5sum: f862c79ca0c15cb775b6bda2f7920691 NeedsCompilation: yes Title: Robust Outlier-aware Estimation of Composition and Heterogeneity for Single-cell Data Description: A robust and outlier-aware method for testing differential tissue composition from single-cell data. This model can infer changes in tissue composition and heterogeneity, and can produce realistic data simulations based on any existing dataset. This model can also transfer knowledge from a large set of integrated datasets to increase accuracy further. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/sccomp SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/stemangiola/sccomp/issues git_url: https://git.bioconductor.org/packages/sccomp git_branch: RELEASE_3_16 git_last_commit: 42f3729 git_last_commit_date: 2022-12-20 Date/Publication: 2022-12-21 source.ver: src/contrib/sccomp_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/sccomp_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.2/sccomp_1.2.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sccomp_1.2.1.tgz vignettes: vignettes/sccomp/inst/doc/introduction.html vignetteTitles: Overview of the sccomp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sccomp/inst/doc/introduction.R dependencyCount: 104 Package: scDataviz Version: 1.8.0 Depends: R (>= 4.0), S4Vectors, SingleCellExperiment, Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods Suggests: PCAtools, cowplot, BiocGenerics, RUnit, knitr, kableExtra, rmarkdown License: GPL-3 Archs: x64 MD5sum: dbbddacb255aafef43492da777322d9f NeedsCompilation: no Title: scDataviz: single cell dataviz and downstream analyses Description: In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease. biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression, Transcription, FlowCytometry, MassSpectrometry, DataImport Author: Kevin Blighe [aut, cre] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/scDataviz VignetteBuilder: knitr BugReports: https://github.com/kevinblighe/scDataviz/issues git_url: https://git.bioconductor.org/packages/scDataviz git_branch: RELEASE_3_16 git_last_commit: 1df199e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scDataviz_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDataviz_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDataviz_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scDataviz_1.8.0.tgz vignettes: vignettes/scDataviz/inst/doc/scDataviz.html vignetteTitles: scDataviz: single cell dataviz and downstream analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R dependencyCount: 172 Package: scDblFinder Version: 1.12.0 Depends: R (>= 4.0), SingleCellExperiment Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils, MASS, IRanges, GenomicRanges, GenomeInfoDb, Rsamtools, rtracklayer Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, circlize, ComplexHeatmap, ggplot2, dplyr, viridisLite, mbkmeans License: GPL-3 + file LICENSE MD5sum: b1131cba5b79264700f26ca27be0292b NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, the new fast and comprehensive scDblFinder method, and a reimplementation of the Amulet detection method for single-cell ATAC-seq. biocViews: Preprocessing, SingleCell, RNASeq, ATACSeq Author: Pierre-Luc Germain [cre, aut] (), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: RELEASE_3_16 git_last_commit: 043e175 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scDblFinder_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDblFinder_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDblFinder_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scDblFinder_1.12.0.tgz vignettes: vignettes/scDblFinder/inst/doc/computeDoubletDensity.html, vignettes/scDblFinder/inst/doc/findDoubletClusters.html, vignettes/scDblFinder/inst/doc/introduction.html, vignettes/scDblFinder/inst/doc/recoverDoublets.html, vignettes/scDblFinder/inst/doc/scATAC.html, vignettes/scDblFinder/inst/doc/scDblFinder.html vignetteTitles: 4_computeDoubletDensity, 3_findDoubletClusters, 1_introduction, 5_recoverDoublets, 6_scATAC, 2_scDblFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDblFinder/inst/doc/computeDoubletDensity.R, vignettes/scDblFinder/inst/doc/findDoubletClusters.R, vignettes/scDblFinder/inst/doc/introduction.R, vignettes/scDblFinder/inst/doc/recoverDoublets.R, vignettes/scDblFinder/inst/doc/scATAC.R, vignettes/scDblFinder/inst/doc/scDblFinder.R dependsOnMe: OSCA.advanced importsMe: singleCellTK dependencyCount: 120 Package: scDD Version: 1.22.0 Depends: R (>= 3.5.0) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: 43ddf42535a11a193b5d9b23bf2318d2 NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] () Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues git_url: https://git.bioconductor.org/packages/scDD git_branch: RELEASE_3_16 git_last_commit: ea9fbf1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scDD_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDD_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDD_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scDD_1.22.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter dependencyCount: 125 Package: scDDboost Version: 1.0.0 Depends: R (>= 4.2), ggplot2 Imports: Rcpp (>= 0.12.11), RcppEigen (>= 0.3.2.9.0),EBSeq, BiocParallel, mclust, SingleCellExperiment, cluster, Oscope, SummarizedExperiment, stats, methods LinkingTo: Rcpp, RcppEigen, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) MD5sum: f45ffa21932575f85e7eabdb5ac8a408 NeedsCompilation: yes Title: A compositional model to assess expression changes from single-cell rna-seq data Description: scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions. biocViews: SingleCell, Software, Clustering, Sequencing, GeneExpression, DifferentialExpression, Bayesian Author: Xiuyu Ma [cre, aut], Michael A. Newton [ctb] Maintainer: Xiuyu Ma URL: https://github.com/wiscstatman/scDDboost SystemRequirements: c++11 VignetteBuilder: knitr BugReports: https://github.com/wiscstatman/scDDboost/issues git_url: https://git.bioconductor.org/packages/scDDboost git_branch: RELEASE_3_16 git_last_commit: c7bccff git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scDDboost_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scDDboost_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scDDboost_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scDDboost_1.0.0.tgz vignettes: vignettes/scDDboost/inst/doc/scDDboost.html vignetteTitles: scDDboost Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDDboost/inst/doc/scDDboost.R dependencyCount: 92 Package: scde Version: 2.26.2 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 Archs: x64 MD5sum: 08f7ffee4de2317b30823c07ade97315 NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut] Maintainer: Jean Fan URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: RELEASE_3_16 git_last_commit: 42cdc08 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/scde_2.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/scde_2.26.2.zip mac.binary.ver: bin/macosx/contrib/4.2/scde_2.26.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scde_2.26.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 48 Package: scds Version: 1.14.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: a03841ee30fc0e770e5b00ae5ab0ca48 NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: RELEASE_3_16 git_last_commit: 32f6932 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scds_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scds_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scds_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scds_1.14.0.tgz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R importsMe: singleCellTK suggestsMe: ExperimentSubset, muscData dependencyCount: 48 Package: SCFA Version: 1.8.1 Depends: R (>= 4.0) Imports: matrixStats, BiocParallel, torch (>= 0.3.0), coro, igraph, Matrix, cluster, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr, rmarkdown License: LGPL MD5sum: f3fef0cc4f91c417d83bb6f8919f54a0 NeedsCompilation: no Title: SCFA: Subtyping via Consensus Factor Analysis Description: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients. biocViews: Survival, Clustering, Classification Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] Maintainer: Duc Tran URL: https://github.com/duct317/SCFA VignetteBuilder: knitr BugReports: https://github.com/duct317/SCFA/issues git_url: https://git.bioconductor.org/packages/SCFA git_branch: RELEASE_3_16 git_last_commit: 9e87474 git_last_commit_date: 2023-03-27 Date/Publication: 2023-03-27 source.ver: src/contrib/SCFA_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCFA_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SCFA_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCFA_1.8.1.tgz vignettes: vignettes/SCFA/inst/doc/Example.html vignetteTitles: SCFA package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCFA/inst/doc/Example.R dependencyCount: 51 Package: scFeatureFilter Version: 1.18.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: f5324716544954dae085d0d673a9812a NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: RELEASE_3_16 git_last_commit: f691a58 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scFeatureFilter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scFeatureFilter_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scFeatureFilter_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scFeatureFilter_1.18.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 38 Package: scGPS Version: 1.12.2 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq2, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 MD5sum: 2c7a83f471ed12aca1874d626d3f8d71 NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: RELEASE_3_16 git_last_commit: 42d6072 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/scGPS_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/scGPS_1.12.2.zip mac.binary.ver: bin/macosx/contrib/4.2/scGPS_1.12.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scGPS_1.12.2.tgz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 142 Package: schex Version: 1.12.0 Depends: SingleCellExperiment (>= 1.7.4), Seurat, ggplot2 (>= 3.2.1), shiny Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, scales, grid, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, shinydashboard, iSEE, igraph, scran License: GPL-3 Archs: x64 MD5sum: cf21f9642b8d321c946f98defb64cf94 NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment and SeuratObject. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization Author: Saskia Freytag Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: RELEASE_3_16 git_last_commit: 28fe8ce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/schex_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/schex_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/schex_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/schex_1.12.0.tgz vignettes: vignettes/schex/inst/doc/multi_modal_schex.html, vignettes/schex/inst/doc/picking_the_right_resolution.html, vignettes/schex/inst/doc/Seurat_schex.html, vignettes/schex/inst/doc/shiny_schex.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: multi_modal_schex, picking_the_right_resolution, Seurat_schex, shiny_schhex, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/multi_modal_schex.R, vignettes/schex/inst/doc/picking_the_right_resolution.R, vignettes/schex/inst/doc/Seurat_schex.R, vignettes/schex/inst/doc/shiny_schex.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF suggestsMe: fcoex dependencyCount: 181 Package: scHOT Version: 1.10.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, markdown, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 MD5sum: 90d5dc43bcaf662e97857e14ccd3ab32 NeedsCompilation: no Title: single-cell higher order testing Description: Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data. biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut] Maintainer: Shila Ghazanfar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: RELEASE_3_16 git_last_commit: 5082c3d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scHOT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scHOT_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scHOT_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scHOT_1.10.0.tgz vignettes: vignettes/scHOT/inst/doc/scHOT.html vignetteTitles: Getting started: scHOT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scHOT/inst/doc/scHOT.R dependencyCount: 73 Package: scifer Version: 1.0.0 Imports: dplyr, rmarkdown, data.table, Biostrings, parallel, stats, plyr, knitr, ggplot2, gridExtra, DECIPHER, stringr, sangerseqR, kableExtra, tibble, scales, rlang, flowCore, methods Suggests: fs, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: bad38f6d75de43e08a89a6e2236ab9e0 NeedsCompilation: no Title: Scifer: Single-Cell Immunoglobulin Filtering of Sanger Sequences Description: Have you ever index sorted cells in a 96 or 384-well plate and then sequenced using Sanger sequencing? If so, you probably had some struggles to either check the electropherogram of each cell sequenced manually, or when you tried to identify which cell was sorted where after sequencing the plate. Scifer was developed to solve this issue by performing basic quality control of Sanger sequences and merging flow cytometry data from probed single-cell sorted B cells with sequencing data. scifer can export summary tables, 'fasta' files, electropherograms for visual inspection, and generate reports. biocViews: Preprocessing, QualityControl, SangerSeq, Sequencing, Software, FlowCytometry, SingleCell Author: Rodrigo Arcoverde Cerveira [aut, cre, cph] (), Sebastian Ols [aut, dtc] (), Karin Loré [dtc, ths] () Maintainer: Rodrigo Arcoverde Cerveira URL: https://github.com/rodrigarc/scifer VignetteBuilder: knitr BugReports: https://github.com/rodrigarc/scifer/issues git_url: https://git.bioconductor.org/packages/scifer git_branch: RELEASE_3_16 git_last_commit: 3899308 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scifer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scifer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scifer_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scifer_1.0.0.tgz vignettes: vignettes/scifer/inst/doc/scifer_walkthrough.html vignetteTitles: Using scifer to filter single-cell sorted B cell receptor (BCR) sanger sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scifer/inst/doc/scifer_walkthrough.R dependencyCount: 119 Package: scMAGeCK Version: 1.9.1 Imports: Seurat, ggplot2, stats, utils Suggests: knitr, rmarkdown License: BSD_2_clause MD5sum: 7b21647e40a871924025b9a0276c4055 NeedsCompilation: yes Title: Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data Description: scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq) biocViews: CRISPR, SingleCell, RNASeq, PooledScreens, Transcriptomics, GeneExpression, Regression Author: Wei Li, Xiaolong Cheng, Lin Yang Maintainer: Xiaolong Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scMAGeCK git_branch: master git_last_commit: bd6d5e5 git_last_commit_date: 2022-04-29 Date/Publication: 2022-04-29 source.ver: src/contrib/scMAGeCK_1.9.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/scMAGeCK_1.9.1.zip vignettes: vignettes/scMAGeCK/inst/doc/scMAGeCK.html vignetteTitles: scMAGeCK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMAGeCK/inst/doc/scMAGeCK.R dependencyCount: 150 Package: scmap Version: 1.20.2 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 0a19b50a0a8a65ca5595d35f948e7068 NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: RELEASE_3_16 git_last_commit: 347230e git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/scmap_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/scmap_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/scmap_1.20.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scmap_1.20.2.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 68 Package: scMerge Version: 1.14.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), parallel, pdist, proxy, ruv, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, scater, testthat, badger License: GPL-3 Archs: x64 MD5sum: 9c5d99f5cf8e4a48806ef4b7a08ffa42 NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell RNA-seq (scRNA-Seq) data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple scRNA-Seq data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of scRNA-Seq data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Yingxin Lin URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: RELEASE_3_16 git_last_commit: afedc69 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scMerge_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scMerge_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scMerge_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scMerge_1.14.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html vignetteTitles: scMerge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R importsMe: singleCellTK suggestsMe: Cepo dependencyCount: 144 Package: scMET Version: 1.0.0 Depends: R (>= 4.2.0) Imports: methods, Rcpp (>= 1.0.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), rstantools (>= 2.1.0), VGAM, data.table, MASS, logitnorm, ggplot2, matrixStats, assertthat, viridis, coda, BiocStyle, cowplot, stats, SummarizedExperiment, SingleCellExperiment, Matrix, dplyr, S4Vectors LinkingTo: BH (>= 1.66.0), Rcpp (>= 1.0.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.21.3), StanHeaders (>= 2.21.0.7) Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 2624cfeecb7e2d55bae3b55dc0dda8db NeedsCompilation: yes Title: Bayesian modelling of cell-to-cell DNA methylation heterogeneity Description: High-throughput single-cell measurements of DNA methylomes can quantify methylation heterogeneity and uncover its role in gene regulation. However, technical limitations and sparse coverage can preclude this task. scMET is a hierarchical Bayesian model which overcomes sparsity, sharing information across cells and genomic features to robustly quantify genuine biological heterogeneity. scMET can identify highly variable features that drive epigenetic heterogeneity, and perform differential methylation and variability analyses. We illustrate how scMET facilitates the characterization of epigenetically distinct cell populations and how it enables the formulation of novel hypotheses on the epigenetic regulation of gene expression. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, DifferentialExpression, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, Bayesian, Sequencing, Coverage, SingleCell Author: Andreas C. Kapourani [aut, cre] (), John Riddell [ctb] Maintainer: Andreas C. Kapourani SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/andreaskapou/scMET/issues git_url: https://git.bioconductor.org/packages/scMET git_branch: RELEASE_3_16 git_last_commit: 3030683 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scMET_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scMET_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scMET_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scMET_1.0.0.tgz vignettes: vignettes/scMET/inst/doc/scMET_vignette.html vignetteTitles: scMET analysis using synthetic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMET/inst/doc/scMET_vignette.R dependencyCount: 111 Package: scmeth Version: 1.18.0 Depends: R (>= 3.5.0) Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, BiocGenerics, ggplot2, ggthemes License: GPL-2 Archs: x64 MD5sum: eb0171295587fe6dc4bc25af138dffd9 NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: RELEASE_3_16 git_last_commit: fffdd11 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scmeth_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scmeth_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scmeth_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scmeth_1.18.0.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R suggestsMe: biscuiteer dependencyCount: 165 Package: SCnorm Version: 1.20.0 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: d9cf4f2fbf010dd0a173afefb500f2ab NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: RELEASE_3_16 git_last_commit: df65f61 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCnorm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCnorm_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCnorm_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCnorm_1.20.0.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 71 Package: scone Version: 1.22.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK, MatrixGenerics, SingleCellExperiment Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork, doParallel, BatchJobs, splatter, scater, kableExtra, mclust, TENxPBMCData License: Artistic-2.0 MD5sum: 1a27594985a8cb4d3ee3fbab10314d7c NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph], Matteo Borella [ctb], Chiara Romualdi [ctb] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: RELEASE_3_16 git_last_commit: ce782b3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scone_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scone_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scone_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scone_1.22.0.tgz vignettes: vignettes/scone/inst/doc/PsiNorm.html, vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: PsiNorm normalization, Introduction to SCONE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/PsiNorm.R, vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 180 Package: Sconify Version: 1.18.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 19d32efc62e19da1cf84ac13e09601ac NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: RELEASE_3_16 git_last_commit: cd1e7fc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Sconify_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Sconify_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Sconify_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Sconify_1.18.0.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 62 Package: SCOPE Version: 1.10.0 Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0) License: GPL-2 MD5sum: 30a18e783f11ea81a32b7932eb73a6ee NeedsCompilation: no Title: A normalization and copy number estimation method for single-cell DNA sequencing Description: Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing, WholeGenome, Coverage, Alignment, QualityControl, DataImport, DNASeq Author: Rujin Wang, Danyu Lin, Yuchao Jiang Maintainer: Rujin Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: RELEASE_3_16 git_last_commit: cd8f6c9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SCOPE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SCOPE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SCOPE_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SCOPE_1.10.0.tgz vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html vignetteTitles: SCOPE: Single-cell Copy Number Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R dependencyCount: 98 Package: scoreInvHap Version: 1.20.0 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: 7b61266950c4d1412f7be40bd7b8a458 NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut], Dolors Pelegrí [aut], Juan R. Gonzalez [aut, cre] Maintainer: Dolors Pelegri-Siso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: RELEASE_3_16 git_last_commit: c46fd46 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scoreInvHap_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scoreInvHap_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scoreInvHap_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scoreInvHap_1.20.0.tgz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Inversion genotyping with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 101 Package: scp Version: 1.8.0 Depends: R (>= 4.2.0), QFeatures (>= 1.3.5) Imports: methods, stats, utils, SingleCellExperiment, SummarizedExperiment, MultiAssayExperiment, MsCoreUtils, matrixStats, S4Vectors, dplyr, magrittr Suggests: testthat, knitr, BiocStyle, rmarkdown, patchwork, ggplot2, impute, scater, sva, preprocessCore, vsn, uwot License: Artistic-2.0 MD5sum: 185713450361774c0a087d040c56697a NeedsCompilation: no Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis Description: Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics data. The package is an extension to the 'QFeatures' package and relies on 'SingleCellExpirement' to enable single-cell proteomics analyses. The package offers the user the functionality to process quantitative table (as generated by MaxQuant, Proteome Discoverer, and more) into data tables ready for downstream analysis and data visualization. biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry, Preprocessing, CellBasedAssays Author: Christophe Vanderaa [aut, cre] (), Laurent Gatto [aut] () Maintainer: Christophe Vanderaa URL: https://UCLouvain-CBIO.github.io/scp VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/scp/issues git_url: https://git.bioconductor.org/packages/scp git_branch: RELEASE_3_16 git_last_commit: 8fd0fdb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scp_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scp_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scp_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scp_1.8.0.tgz vignettes: vignettes/scp/inst/doc/advanced.html, vignettes/scp/inst/doc/QFeatures_nutshell.html, vignettes/scp/inst/doc/read_scp.html, vignettes/scp/inst/doc/scp.html vignetteTitles: Advanced usage of `scp`, QFeatures in a nutshell, Load data using readSCP, Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/advanced.R, vignettes/scp/inst/doc/QFeatures_nutshell.R, vignettes/scp/inst/doc/read_scp.R, vignettes/scp/inst/doc/scp.R suggestsMe: scpdata dependencyCount: 105 Package: scPCA Version: 1.12.0 Depends: R (>= 4.0.0) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray, ScaledMatrix, MatrixGenerics Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark License: MIT + file LICENSE MD5sum: f1fc3d6e40ac424f090787892359ed1c NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (), Nima Hejazi [aut] (), Sandrine Dudoit [ctb, ths] () Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: RELEASE_3_16 git_last_commit: 8e92c23 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scPCA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scPCA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scPCA_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scPCA_1.12.0.tgz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependsOnMe: OSCA.advanced, OSCA.workflows dependencyCount: 68 Package: scPipe Version: 1.20.6 Depends: R (>= 4.2.0), SingleCellExperiment Imports: AnnotationDbi, basilisk, BiocGenerics, biomaRt, Biostrings, data.table, dplyr, DropletUtils, flexmix, GenomicRanges, GenomicAlignments, GGally, ggplot2, glue (>= 1.3.0), grDevices, graphics, hash, IRanges, magrittr, MASS, Matrix (>= 1.5.0), mclust, methods, MultiAssayExperiment, org.Hs.eg.db, org.Mm.eg.db, purrr, Rcpp (>= 0.11.3), reshape, reticulate, Rhtslib, rlang, robustbase, Rsamtools, Rsubread, rtracklayer, SummarizedExperiment, S4Vectors, scales, stats, stringr, tibble, tidyr, tools, utils, LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc, testthat Suggests: BiocStyle, DT, GenomicFeatures, grid, igraph, kableExtra, knitr, locStra, plotly, rmarkdown, RColorBrewer, readr, reshape2, RANN, shiny, scater (>= 1.11.0), testthat, xml2, umap License: GPL (>= 2) MD5sum: 18ab2ad1a40f3b2653b6f63f1a41c38e NeedsCompilation: yes Title: Pipeline for single cell multi-omic data pre-processing Description: A preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation, DataImport Author: Luyi Tian [aut], Shian Su [aut, cre], Shalin Naik [ctb], Shani Amarasinghe [aut], Oliver Voogd [aut], Phil Yang [aut], Matthew Ritchie [ctb] Maintainer: Shian Su URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe git_url: https://git.bioconductor.org/packages/scPipe git_branch: RELEASE_3_16 git_last_commit: defa6ff git_last_commit_date: 2023-03-06 Date/Publication: 2023-03-07 source.ver: src/contrib/scPipe_1.20.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/scPipe_1.20.6.zip mac.binary.ver: bin/macosx/contrib/4.2/scPipe_1.20.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scPipe_1.20.6.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.html, vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: a flexible data preprocessing pipeline for single-cell data, scPipe: a flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_atac_tutorial.R, vignettes/scPipe/inst/doc/scPipe_tutorial.R dependencyCount: 166 Package: scran Version: 1.26.2 Depends: SingleCellExperiment, scuttle Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, edgeR, limma, igraph, statmod, DelayedArray, DelayedMatrixStats, BiocSingular, bluster, metapod, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, HDF5Array, scRNAseq, dynamicTreeCut, ResidualMatrix, ScaledMatrix, DESeq2, monocle, Biobase, pheatmap, scater License: GPL-3 Archs: x64 MD5sum: b75ee89e2df29916dfe93c06861bd160 NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scran git_branch: RELEASE_3_16 git_last_commit: 0cdaef6 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/scran_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/scran_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.2/scran_1.26.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scran_1.26.2.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, BayesSpace, BioTIP, celda, ChromSCape, CiteFuse, conclus, Dino, FLAMES, IRISFGM, msImpute, mumosa, pipeComp, scDblFinder, scDD, scTreeViz, SingleCellSignalR, singleCellTK, Spaniel, spatialHeatmap, mixhvg, SC.MEB suggestsMe: APL, batchelor, bluster, CellTrails, clusterExperiment, destiny, dittoSeq, ExperimentSubset, fcoex, ggspavis, Glimma, glmGamPoi, iSEEu, miloR, Nebulosa, nnSVG, PCAtools, schex, scone, scuttle, SingleR, splatter, SPOTlight, tidySingleCellExperiment, transformGamPoi, TSCAN, velociraptor, Voyager, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell dependencyCount: 60 Package: scReClassify Version: 1.4.0 Depends: R (>= 4.1) Imports: randomForest, e1071, stats, SummarizedExperiment, SingleCellExperiment, methods Suggests: testthat, knitr, BiocStyle, rmarkdown, DT, mclust, dplyr License: GPL-3 + file LICENSE Archs: x64 MD5sum: 1667b94f560ceec6975c8c8a20919eda NeedsCompilation: no Title: scReClassify: post hoc cell type classification of single-cell RNA-seq data Description: A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier. biocViews: Software, Transcriptomics, SingleCell, Classification, SupportVectorMachine Author: Pengyi Yang [aut] (), Taiyun Kim [aut, cre] () Maintainer: Taiyun Kim URL: https://github.com/SydneyBioX/scReClassify, http://www.bioconductor.org/packages/release/bioc/html/scReClassify.html VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scReClassify/issues git_url: https://git.bioconductor.org/packages/scReClassify git_branch: RELEASE_3_16 git_last_commit: 0d6f4ef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scReClassify_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scReClassify_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scReClassify_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scReClassify_1.4.0.tgz vignettes: vignettes/scReClassify/inst/doc/scReClassify.html vignetteTitles: An introduction to scReClassify package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scReClassify/inst/doc/scReClassify.R dependencyCount: 31 Package: scRecover Version: 1.14.1 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: 93001b9271a8167d75cb6b759a3a9bb6 NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: RELEASE_3_16 git_last_commit: 2abcfcd git_last_commit_date: 2023-01-10 Date/Publication: 2023-01-10 source.ver: src/contrib/scRecover_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/scRecover_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/scRecover_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scRecover_1.14.1.tgz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 46 Package: ScreenR Version: 1.0.0 Depends: R (>= 4.2) Imports: methods (>= 4.0), rlang (>= 0.4), stringr (>= 1.4), limma (>= 3.46), patchwork (>= 1.1), tibble (>= 3.1.6), scales (>= 1.1.1), ggvenn (>= 0.1.9), purrr (>= 0.3.4), ggplot2 (>= 3.3), stats, tidyr (>= 1.2), magrittr (>= 1.0), dplyr (>= 1.0), edgeR (>= 3.32), tidyselect (>= 1.1.2) Suggests: rmarkdown (>= 2.11), knitr (>= 1.37), testthat (>= 3.0.0), BiocStyle (>= 2.22.0), covr (>= 3.5) License: MIT + file LICENSE MD5sum: f47489699de0e17251ed8253deef5ffb NeedsCompilation: no Title: Package to Perform High Throughput Biological Screening Description: ScreenR is a package suitable to perform hit identification in loss of function High Throughput Biological Screenings performed using barcoded shRNA-based libraries. ScreenR combines the computing power of software such as edgeR with the simplicity of use of the Tidyverse metapackage. ScreenR executes a pipeline able to find candidate hits from barcode counts, and integrates a wide range of visualization modes for each step of the analysis. biocViews: Software, AssayDomain, GeneExpression Author: Emanuel Michele Soda [aut, cre] (0000-0002-2301-6465), Elena Ceccacci [aut] (0000-0002-2285-8994), Saverio Minucci [fnd, ths] (0000-0001-5678-536X) Maintainer: Emanuel Michele Soda URL: https://emanuelsoda.github.io/ScreenR/ VignetteBuilder: knitr BugReports: https://github.com/EmanuelSoda/ScreenR/issues git_url: https://git.bioconductor.org/packages/ScreenR git_branch: RELEASE_3_16 git_last_commit: e401f4b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ScreenR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ScreenR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ScreenR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ScreenR_1.0.0.tgz vignettes: vignettes/ScreenR/inst/doc/Analysis_Example.html vignetteTitles: ScreenR Example Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ScreenR/inst/doc/Analysis_Example.R dependencyCount: 50 Package: scRepertoire Version: 1.8.0 Depends: ggplot2, R (>= 4.0) Imports: stringdist, dplyr, reshape2, ggalluvial, stringr, vegan, powerTCR, SingleCellExperiment, SummarizedExperiment, plyr, parallel, doParallel, methods, utils, rlang, igraph, ggraph, tidygraph, SeuratObject Suggests: knitr, rmarkdown, BiocStyle, circlize, scales, Seurat, scater License: GPL-2 MD5sum: 390b786725eb6e85d55d6b53fbc90092 NeedsCompilation: no Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: RELEASE_3_16 git_last_commit: a07f550 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scRepertoire_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scRepertoire_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scRepertoire_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scRepertoire_1.8.0.tgz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R dependencyCount: 105 Package: scruff Version: 1.16.0 Depends: R (>= 4.0) Imports: data.table, GenomicAlignments, GenomicFeatures, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: f6cc681e4b17304cabb5588bd717daeb NeedsCompilation: no Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: RELEASE_3_16 git_last_commit: 6d4b87c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scruff_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scruff_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scruff_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scruff_1.16.0.tgz vignettes: vignettes/scruff/inst/doc/scruff.html vignetteTitles: Process Single Cell RNA-Seq reads using scruff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scruff/inst/doc/scruff.R dependencyCount: 168 Package: scry Version: 1.10.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: markdown, BiocGenerics, covr, DuoClustering2018, ggplot2, knitr, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: c9aa38d89dd8f2ed22d68b444ab4fe49 NeedsCompilation: no Title: Small-Count Analysis Methods for High-Dimensional Data Description: Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq. biocViews: DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Software, Sequencing, SingleCell, Transcriptomics Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] Maintainer: Kelly Street URL: https://bioconductor.org/packages/scry.html VignetteBuilder: knitr BugReports: https://github.com/kstreet13/scry/issues git_url: https://git.bioconductor.org/packages/scry git_branch: RELEASE_3_16 git_last_commit: d416920 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scry_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scry_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scry_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scry_1.10.0.tgz vignettes: vignettes/scry/inst/doc/bigdata.html, vignettes/scry/inst/doc/scry.html vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scry/inst/doc/bigdata.R, vignettes/scry/inst/doc/scry.R dependencyCount: 48 Package: scShapes Version: 1.4.0 Depends: R (>= 4.1) Imports: Matrix, stats, methods, pscl, VGAM, dgof, BiocParallel, MASS, emdbook, magrittr, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: GPL-3 MD5sum: 5c6e4b1c85855eece061f9db8b0902db NeedsCompilation: yes Title: A Statistical Framework for Modeling and Identifying Differential Distributions in Single-cell RNA-sequencing Data Description: We present a novel statistical framework for identifying differential distributions in single-cell RNA-sequencing (scRNA-seq) data between treatment conditions by modeling gene expression read counts using generalized linear models (GLMs). We model each gene independently under each treatment condition using error distributions Poisson (P), Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) with log link function and model based normalization for differences in sequencing depth. Since all four distributions considered in our framework belong to the same family of distributions, we first perform a Kolmogorov-Smirnov (KS) test to select genes belonging to the family of ZINB distributions. Genes passing the KS test will be then modeled using GLMs. Model selection is done by calculating the Bayesian Information Criterion (BIC) and likelihood ratio test (LRT) statistic. biocViews: RNASeq, SingleCell, MultipleComparison, GeneExpression Author: Malindrie Dharmaratne [cre, aut] () Maintainer: Malindrie Dharmaratne URL: https://github.com/Malindrie/scShapes VignetteBuilder: knitr BugReports: https://github.com/Malindrie/scShapes/issues git_url: https://git.bioconductor.org/packages/scShapes git_branch: RELEASE_3_16 git_last_commit: 2dfb237 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scShapes_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scShapes_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scShapes_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scShapes_1.4.0.tgz vignettes: vignettes/scShapes/inst/doc/vignette_scShapes.html vignetteTitles: The vignette for running scShapes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scShapes/inst/doc/vignette_scShapes.R dependencyCount: 34 Package: scTensor Version: 2.8.0 Depends: R (>= 4.1.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor (>= 1.1.5), ccTensor (>= 1.0.2), rTensor (>= 1.4.8), abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi (>= 1.29.2), grDevices, graphics, stats, utils, outliers, Category, meshr (>= 1.99.1), GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBaseDbi, Seurat, scTGIF, Homo.sapiens License: Artistic-2.0 MD5sum: 1d9aac945bfe75794f8eb6778cdcee23 NeedsCompilation: no Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: RELEASE_3_16 git_last_commit: 4ead911 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scTensor_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTensor_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTensor_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scTensor_2.8.0.tgz vignettes: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html, vignettes/scTensor/inst/doc/scTensor.html vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor: 2. Interpretation of HTML report, scTensor: 3. Simulation of CCI, scTensor: 4. Reanalysis of the results of scTensor, scTensor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R, vignettes/scTensor/inst/doc/scTensor.R dependencyCount: 281 Package: scTGIF Version: 1.12.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: aa3fb4222a640c7ef58b6135ff4163c8 NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: RELEASE_3_16 git_last_commit: a1b6570 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scTGIF_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTGIF_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTGIF_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scTGIF_1.12.0.tgz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R suggestsMe: scTensor dependencyCount: 210 Package: scTHI Version: 1.10.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown License: GPL-2 MD5sum: 37be49390b4d0b017204a350cbb6bcee NeedsCompilation: no Title: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data Description: scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment. biocViews: Software,SingleCell Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre] Maintainer: Michele Ceccarelli VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: RELEASE_3_16 git_last_commit: 5642554 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scTHI_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTHI_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTHI_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scTHI_1.10.0.tgz vignettes: vignettes/scTHI/inst/doc/vignette.html vignetteTitles: Using scTHI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTHI/inst/doc/vignette.R dependencyCount: 17 Package: scTreeViz Version: 1.4.0 Depends: R (>= 4.0), methods, epivizr, SummarizedExperiment Imports: data.table, S4Vectors, digest, Matrix, Rtsne, httr, igraph, clustree, scran, sys, epivizrData, epivizrServer, ggraph, scater, Seurat, SingleCellExperiment, ggplot2, stats, utils Suggests: knitr, BiocStyle, testthat, SC3, scRNAseq, rmarkdown, msd16s, metagenomeSeq, epivizrStandalone, GenomeInfoDb License: Artistic-2.0 MD5sum: dbc0c09c7df3c30b7469ad195ed5697e NeedsCompilation: no Title: R/Bioconductor package to interactively explore and visualize single cell RNA-seq datasets with hierarhical annotations Description: scTreeViz provides classes to support interactive data aggregation and visualization of single cell RNA-seq datasets with hierarchies for e.g. cell clusters at different resolutions. The `TreeIndex` class provides methods to manage hierarchy and split the tree at a given resolution or across resolutions. The `TreeViz` class extends `SummarizedExperiment` and can performs quick aggregations on the count matrix defined by clusters. biocViews: Visualization, Infrastructure, GUI, SingleCell Author: Jayaram Kancherla [aut, cre], Hector Corrada Bravo [aut], Kazi Tasnim Zinat [aut], Stephanie Hicks [aut] Maintainer: Jayaram Kancherla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTreeViz git_branch: RELEASE_3_16 git_last_commit: b21f115 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/scTreeViz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/scTreeViz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/scTreeViz_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scTreeViz_1.4.0.tgz vignettes: vignettes/scTreeViz/inst/doc/ExploreTreeViz.html vignetteTitles: Explore Data using scTreeViz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTreeViz/inst/doc/ExploreTreeViz.R dependencyCount: 248 Package: scuttle Version: 1.8.4 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat, scran License: GPL-3 MD5sum: d3809c66c36cadd0be0ab8a7070fbbbc NeedsCompilation: yes Title: Single-Cell RNA-Seq Analysis Utilities Description: Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport Author: Aaron Lun [aut, cre], Davis McCarthy [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: RELEASE_3_16 git_last_commit: ac64d31 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/scuttle_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/scuttle_1.8.4.zip mac.binary.ver: bin/macosx/contrib/4.2/scuttle_1.8.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/scuttle_1.8.4.tgz vignettes: vignettes/scuttle/inst/doc/misc.html, vignettes/scuttle/inst/doc/norm.html, vignettes/scuttle/inst/doc/qc.html vignetteTitles: 3. Other functions, 2. Normalization, 1. Quality control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scuttle/inst/doc/misc.R, vignettes/scuttle/inst/doc/norm.R, vignettes/scuttle/inst/doc/qc.R dependsOnMe: scater, scran, OSCA.advanced, OSCA.basic, OSCA.intro, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: BASiCS, BASiCStan, batchelor, DropletUtils, FLAMES, imcRtools, mia, mumosa, muscat, scDblFinder, singleCellTK, spatialHeatmap, velociraptor, spatialLIBD, mixhvg suggestsMe: bluster, miloR, SingleR, splatter, TSCAN, HCAData, MouseThymusAgeing, Platypus linksToMe: DropletUtils, scran dependencyCount: 40 Package: SDAMS Version: 1.18.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL Archs: x64 MD5sum: 26931c63b22d09333f3162665d260617 NeedsCompilation: no Title: Differential Abundant/Expression Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data Description: This Package utilizes a Semi-parametric Differential Abundance/expression analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single-cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry, SingleCell Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: RELEASE_3_16 git_last_commit: 707bfe2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SDAMS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SDAMS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SDAMS_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SDAMS_1.18.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 59 Package: sechm Version: 1.6.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, seriation, ComplexHeatmap, circlize, methods, randomcoloR, stats, grid, grDevices, matrixStats Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 3ec4df827db4129275a8782e806d1f24 NeedsCompilation: no Title: sechm: Complex Heatmaps from a SummarizedExperiment Description: sechm provides a simple interface between SummarizedExperiment objects and the ComplexHeatmap package. It enables plotting annotated heatmaps from SE objects, with easy access to rowData and colData columns, and implements a number of features to make the generation of heatmaps easier and more flexible. These functionalities used to be part of the SEtools package. biocViews: GeneExpression, Visualization Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/sechm git_url: https://git.bioconductor.org/packages/sechm git_branch: RELEASE_3_16 git_last_commit: 446fbc9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sechm_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sechm_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sechm_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sechm_1.6.0.tgz vignettes: vignettes/sechm/inst/doc/sechm.html vignetteTitles: sechm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sechm/inst/doc/sechm.R dependsOnMe: SEtools dependencyCount: 70 Package: segmenter Version: 1.4.0 Depends: R (>= 4.1) Imports: ChIPseeker, GenomicRanges, SummarizedExperiment, IRanges, S4Vectors, bamsignals, ComplexHeatmap, graphics, stats, utils, methods, chromhmmData Suggests: testthat, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg18.knownGene, Gviz License: GPL-3 MD5sum: ae36cdc0da8a3592632abcaa3654a66f NeedsCompilation: no Title: Perform Chromatin Segmentation Analysis in R by Calling ChromHMM Description: Chromatin segmentation analysis transforms ChIP-seq data into signals over the genome. The latter represents the observed states in a multivariate Markov model to predict the chromatin's underlying states. ChromHMM, written in Java, integrates histone modification datasets to learn the chromatin states de-novo. The goal of this package is to call chromHMM from within R, capture the output files in an S4 object and interface to other relevant Bioconductor analysis tools. In addition, segmenter provides functions to test, select and visualize the output of the segmentation. biocViews: Software, HistoneModification Author: Mahmoud Ahmed [aut, cre] () Maintainer: Mahmoud Ahmed VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/segmenter/issues git_url: https://git.bioconductor.org/packages/segmenter git_branch: RELEASE_3_16 git_last_commit: edc3f51 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/segmenter_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/segmenter_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/segmenter_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/segmenter_1.4.0.tgz vignettes: vignettes/segmenter/inst/doc/segmenter.html vignetteTitles: Chromatin Segmentation Analysis Using segmenter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmenter/inst/doc/segmenter.R dependencyCount: 170 Package: segmentSeq Version: 2.32.0 Depends: R (>= 3.5.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 037157695e46d02bd8d08a675432fea6 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: RELEASE_3_16 git_last_commit: 4376804 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/segmentSeq_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/segmentSeq_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/segmentSeq_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/segmentSeq_2.32.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 56 Package: selectKSigs Version: 1.10.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 MD5sum: 7d3257cd0a511c5fb850296e773f3141 NeedsCompilation: yes Title: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation Description: A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Clustering Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/selectKSigs VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs git_url: https://git.bioconductor.org/packages/selectKSigs git_branch: RELEASE_3_16 git_last_commit: d786147 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/selectKSigs_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/selectKSigs_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/selectKSigs_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/selectKSigs_1.10.0.tgz vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R dependencyCount: 124 Package: SELEX Version: 1.30.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) Archs: x64 MD5sum: d2a0d577241fb483771537befa65ffc2 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data. biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J. Bussemaker Maintainer: Harmen J. Bussemaker URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: RELEASE_3_16 git_last_commit: af75d43 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SELEX_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SELEX_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SELEX_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SELEX_1.30.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 19 Package: SemDist Version: 1.32.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 27cd2f1337d1af51aad1684bcdddefa8 NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: RELEASE_3_16 git_last_commit: 35c48bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SemDist_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SemDist_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SemDist_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SemDist_1.32.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 50 Package: semisup Version: 1.22.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: a71ff9963ae3472e35ca4e0d8eb7e606 NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: RELEASE_3_16 git_last_commit: 5a87e13 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/semisup_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/semisup_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/semisup_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/semisup_1.22.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: SEPIRA Version: 1.18.0 Depends: R (>= 3.5.0) Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1), stats Suggests: knitr, rmarkdown, testthat, igraph License: GPL-3 MD5sum: 9dcc9607f7cfb5109e20a1bbdbe02dc0 NeedsCompilation: no Title: Systems EPigenomics Inference of Regulatory Activity Description: SEPIRA (Systems EPigenomics Inference of Regulatory Activity) is an algorithm that infers sample-specific transcription factor activity from the genome-wide expression or DNA methylation profile of the sample. biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget, NetworkInference, Network, Software Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut] Maintainer: Yuting Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEPIRA git_branch: RELEASE_3_16 git_last_commit: fc6746e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SEPIRA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SEPIRA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SEPIRA_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SEPIRA_1.18.0.tgz vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html vignetteTitles: Introduction to `SEPIRA` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R dependencyCount: 8 Package: seq2pathway Version: 1.30.0 Depends: R (>= 3.6.2) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: 4aa3b314396ba6711f18d933a55399ad NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Arjun Kinstlick git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: RELEASE_3_16 git_last_commit: 6881da6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seq2pathway_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seq2pathway_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seq2pathway_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seq2pathway_1.30.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R dependencyCount: 133 Package: seqArchR Version: 1.2.0 Depends: R (>= 4.2.0) Imports: utils, graphics, cvTools (>= 0.3.2), MASS, Matrix, methods, stats, cluster, matrixStats, fpc, cli, prettyunits, reshape2 (>= 1.4.3), reticulate (>= 1.22), BiocParallel, Biostrings, grDevices, ggplot2 (>= 3.1.1), ggseqlogo (>= 0.1) Suggests: cowplot, hopach (>= 2.42.0), BiocStyle, knitr (>= 1.22), rmarkdown (>= 1.12), testthat (>= 3.0.2), covr, vdiffr (>= 0.3.0) License: GPL-3 | file LICENSE MD5sum: 216a3a6ab91a3233799935f0d1b17b90 NeedsCompilation: no Title: Identify Different Architectures of Sequence Elements Description: seqArchR enables unsupervised discovery of _de novo_ clusters with characteristic sequence architectures characterized by position-specific motifs or composition of stretches of nucleotides, e.g., CG-richness. seqArchR does _not_ require any specifications w.r.t. the number of clusters, the length of any individual motifs, or the distance between motifs if and when they occur in pairs/groups; it directly detects them from the data. seqArchR uses non-negative matrix factorization (NMF) as its backbone, and employs a chunking-based iterative procedure that enables processing of large sequence collections efficiently. Wrapper functions are provided for visualizing cluster architectures as sequence logos. biocViews: MotifDiscovery, GeneRegulation, MathematicalBiology, SystemsBiology, Transcriptomics, Genetics, Clustering, DimensionReduction, FeatureExtraction, DNASeq Author: Sarvesh Nikumbh [aut, cre, cph] () Maintainer: Sarvesh Nikumbh URL: https://snikumbh.github.io/seqArchR/, https://github.com/snikumbh/seqArchR SystemRequirements: Python (>= 3.5), scikit-learn (>= 0.21.2), packaging VignetteBuilder: knitr BugReports: https://github.com/snikumbh/seqArchR/issues git_url: https://git.bioconductor.org/packages/seqArchR git_branch: RELEASE_3_16 git_last_commit: 1bf21a7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqArchR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqArchR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqArchR_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqArchR_1.2.0.tgz vignettes: vignettes/seqArchR/inst/doc/seqArchR.html vignetteTitles: Example usage of _seqArchR_ on simulated DNA sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqArchR/inst/doc/seqArchR.R dependencyCount: 86 Package: SeqArray Version: 1.38.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.31.1) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, digest, crayon, knitr, markdown, rmarkdown, Rsamtools, VariantAnnotation License: GPL-3 MD5sum: 57a086f0b8eed10c74c6827317acf435 NeedsCompilation: yes Title: Data Management of Large-Scale Whole-Genome Sequence Variant Calls Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_16 git_last_commit: 37a86c1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SeqArray_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqArray_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqArray_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SeqArray_1.38.0.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: GBScleanR, SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, ggmanh, VariantExperiment, coxmeg, GMMAT, MAGEE suggestsMe: DelayedDataFrame, HIBAG, VCFArray dependencyCount: 21 Package: seqbias Version: 1.46.0 Depends: R (>= 3.0.2), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods LinkingTo: Rhtslib (>= 1.99.1), zlibbioc Suggests: Rsamtools, ggplot2 License: LGPL-3 Archs: x64 MD5sum: c05c07af008352e3fc607d7a56e3d244 NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/seqbias git_branch: RELEASE_3_16 git_last_commit: d15d8fa git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqbias_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqbias_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqbias_1.46.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON dependencyCount: 20 Package: seqCAT Version: 1.20.0 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE Archs: x64 MD5sum: 289f0b1f46c9064d7c8df8d7672d36e3 NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: RELEASE_3_16 git_last_commit: b778c7c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqCAT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqCAT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqCAT_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqCAT_1.20.0.tgz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 112 Package: seqCNA Version: 1.44.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 MD5sum: 6cdcf1f5c32255102ec548b6ec2d776b NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools git_url: https://git.bioconductor.org/packages/seqCNA git_branch: RELEASE_3_16 git_last_commit: 360ad34 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqCNA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqCNA_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqCNA_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqCNA_1.44.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R suggestsMe: Herper dependencyCount: 27 Package: seqcombo Version: 1.20.0 Depends: R (>= 3.4.0) Imports: ggplot2, grid, igraph, utils, yulab.utils Suggests: emojifont, knitr, rmarkdown, prettydoc, tibble License: Artistic-2.0 Archs: x64 MD5sum: a6e363f154d0ce8d97c4e727c3723e93 NeedsCompilation: no Title: Visualization Tool for Genetic Reassortment Description: Provides useful functions for visualizing virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: RELEASE_3_16 git_last_commit: 714f389 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqcombo_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqcombo_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqcombo_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqcombo_1.20.0.tgz vignettes: vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 38 Package: SeqGate Version: 1.8.0 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat (>= 3.0.0), edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: f16cc2c91e276bd114fdb81f3255f3e5 NeedsCompilation: no Title: Filtering of Lowly Expressed Features Description: Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified. biocViews: DifferentialExpression, GeneExpression, Transcriptomics, Sequencing, RNASeq Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre] Maintainer: Stéphanie Rialle VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: RELEASE_3_16 git_last_commit: 34dade0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SeqGate_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqGate_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqGate_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SeqGate_1.8.0.tgz vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html vignetteTitles: SeqGate: Filter lowly expressed features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R dependencyCount: 26 Package: SeqGSEA Version: 1.38.0 Depends: Biobase, doParallel, DESeq2 Imports: methods, biomaRt Suggests: GenomicRanges License: GPL (>= 3) MD5sum: 96a83a79d46b81e802608b90558dd304 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: RELEASE_3_16 git_last_commit: fe53ed1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SeqGSEA_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqGSEA_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqGSEA_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SeqGSEA_1.38.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 111 Package: seqLogo Version: 1.64.0 Depends: R (>= 4.2), methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: efcf7a417a5d90b0fd0c95592e6863bd NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] () Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: RELEASE_3_16 git_last_commit: 75ff6c0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqLogo_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqLogo_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqLogo_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqLogo_1.64.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.html vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: rGADEM, generegulation importsMe: IntEREst, PWMEnrich, rGADEM, riboSeqR, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, igvR, MAGAR, motifcounter, MotifDb, universalmotif, phangorn dependencyCount: 4 Package: seqPattern Version: 1.30.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: 46c57ea8690e017bf1e3ce733005e575 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: RELEASE_3_16 git_last_commit: 0593d54 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqPattern_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqPattern_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqPattern_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqPattern_1.30.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 21 Package: seqsetvis Version: 1.18.1 Depends: R (>= 3.6), ggplot2 Imports: cowplot, data.table, eulerr, GenomeInfoDb, GenomicAlignments, GenomicRanges, ggplotify, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, stats, UpSetR Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: d7769aba44dee5c85ecb4f2f11c4d6eb NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: RELEASE_3_16 git_last_commit: 70b164b git_last_commit_date: 2023-02-24 Date/Publication: 2023-02-24 source.ver: src/contrib/seqsetvis_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqsetvis_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.2/seqsetvis_1.18.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqsetvis_1.18.1.tgz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 91 Package: SeqSQC Version: 1.20.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: f724a66f473370cafcd4e06b1cf0b09f NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: RELEASE_3_16 git_last_commit: 56cafce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SeqSQC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqSQC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqSQC_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SeqSQC_1.20.0.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 139 Package: seqTools Version: 1.32.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 260b5048afadb964a34c70c62428132b NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers git_url: https://git.bioconductor.org/packages/seqTools git_branch: RELEASE_3_16 git_last_commit: 1e8f2be git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/seqTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/seqTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/seqTools_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/seqTools_1.32.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R importsMe: qckitfastq dependencyCount: 3 Package: SeqVarTools Version: 1.36.0 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, data.table, Suggests: BiocStyle, RUnit, stringr License: GPL-3 Archs: x64 MD5sum: a702db8f540a1c474f51a3b42c316333 NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: RELEASE_3_16 git_last_commit: be0bdfb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SeqVarTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SeqVarTools_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SeqVarTools_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SeqVarTools_1.36.0.tgz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS, VariantExperiment, GMMAT, MAGEE dependencyCount: 60 Package: sesame Version: 1.16.1 Depends: R (>= 4.1), sesameData Imports: graphics, BiocParallel, utils, methods, stringr, readr, tibble, illuminaio, MASS, wheatmap (>= 0.2.0), GenomicRanges, IRanges, grid, preprocessCore, S4Vectors, ggplot2, BiocFileCache, GenomeInfoDb, stats, SummarizedExperiment, dplyr, reshape2 Suggests: scales, knitr, DNAcopy, e1071, randomForest, RPMM, rmarkdown, testthat, tidyr, BiocStyle, ggrepel, grDevices, pals License: MIT + file LICENSE MD5sum: 2f9a62f9dd05a24dfb095f3be153f6db NeedsCompilation: no Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips Description: Tools For analyzing Illumina Infinium DNA methylation arrays.SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features accurate detection calling, intelligent inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre], Wubin Ding [ctb], David Goldberg [ctb], Ethan Moyer [ctb], Bret Barnes [ctb], Timothy Triche [ctb], Hui Shen [aut] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: RELEASE_3_16 git_last_commit: 6a20764 git_last_commit_date: 2022-11-13 Date/Publication: 2022-11-14 source.ver: src/contrib/sesame_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/sesame_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/sesame_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sesame_1.16.1.tgz vignettes: vignettes/sesame/inst/doc/inferences.html, vignettes/sesame/inst/doc/KYCG.html, vignettes/sesame/inst/doc/modeling.html, vignettes/sesame/inst/doc/nonhuman.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. Data Inference", "5. knowYourCG", 3. Modeling, 2. Non-human Array, 1. Quality Control, "0. Basic Usage" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/inferences.R, vignettes/sesame/inst/doc/KYCG.R, vignettes/sesame/inst/doc/modeling.R, vignettes/sesame/inst/doc/nonhuman.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: MethReg, TCGAbiolinksGUI suggestsMe: RnBeads, TCGAbiolinks, sesameData dependencyCount: 141 Package: SEtools Version: 1.12.0 Depends: R (>= 4.0), SummarizedExperiment, sechm Imports: BiocParallel, Matrix, DESeq2, S4Vectors, data.table, edgeR, openxlsx, pheatmap, stats, circlize, methods, sva Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: dbef07a3d593da625756c8440af9adef NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of tools for working with the SummarizedExperiment class, including merging, melting, aggregation functions. Plotting functions historically in this package have been moved to the sechm package. biocViews: GeneExpression Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: RELEASE_3_16 git_last_commit: ed5fccf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SEtools_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SEtools_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SEtools_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SEtools_1.12.0.tgz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 124 Package: sevenbridges Version: 1.28.0 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, data.table Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE MD5sum: 0c8045e1c3140d54ba2d925bf800688a NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Soner Koc [aut, cre], Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Seven Bridges Genomics [cph, fnd] Maintainer: Soner Koc URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: RELEASE_3_16 git_last_commit: 4fbfb90 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sevenbridges_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sevenbridges_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sevenbridges_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sevenbridges_1.28.0.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 30 Package: sevenC Version: 1.18.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: f3f755be4ac6d6344098381ca17ee684 NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: RELEASE_3_16 git_last_commit: 15f39dd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sevenC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sevenC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sevenC_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sevenC_1.18.0.tgz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 74 Package: SGSeq Version: 1.32.0 Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: 69df298bdaef5d928d8707834bb6634d NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein [cre, aut] Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: RELEASE_3_16 git_last_commit: dfa228e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SGSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SGSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SGSeq_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SGSeq_1.32.0.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 dependencyCount: 98 Package: SharedObject Version: 1.12.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: cb2be5123174af25cf69b6aa93024a45 NeedsCompilation: yes Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues git_url: https://git.bioconductor.org/packages/SharedObject git_branch: RELEASE_3_16 git_last_commit: 9ad8ed9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SharedObject_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SharedObject_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SharedObject_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SharedObject_1.12.0.tgz vignettes: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.html, vignettes/SharedObject/inst/doc/quick_start_guide.html vignetteTitles: quickStartChinese, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide_Chinese.R, vignettes/SharedObject/inst/doc/quick_start_guide.R importsMe: NewWave dependencyCount: 7 Package: shinyepico Version: 1.6.0 Depends: R (>= 4.2.0) Imports: DT (>= 0.15.0), data.table (>= 1.13.0), doParallel (>= 1.0.0), dplyr (>= 1.0.9), foreach (>= 1.5.0), GenomicRanges (>= 1.38.0), ggplot2 (>= 3.3.0), gplots (>= 3.0.0), heatmaply (>= 1.1.0), limma (>= 3.42.0), minfi (>= 1.32.0), plotly (>= 4.9.2), reshape2 (>= 1.4.0), rlang (>= 1.0.2), rmarkdown (>= 2.3.0), rtracklayer (>= 1.46.0), shiny (>= 1.5.0), shinyWidgets (>= 0.5.0), shinycssloaders (>= 0.3.0), shinyjs (>= 1.1.0), shinythemes (>= 1.1.0), statmod (>= 1.4.0), tidyr (>= 1.2.0), zip (>= 2.1.0) Suggests: knitr (>= 1.30.0), mCSEA (>= 1.10.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, testthat, minfiData License: AGPL-3 + file LICENSE Archs: x64 MD5sum: 8acfb6722b5372cc5d0fa87f58be7ef9 NeedsCompilation: no Title: ShinyÉPICo Description: ShinyÉPICo is a graphical pipeline to analyze Illumina DNA methylation arrays (450k or EPIC). It allows to calculate differentially methylated positions and differentially methylated regions in a user-friendly interface. Moreover, it includes several options to export the results and obtain files to perform downstream analysis. biocViews: DifferentialMethylation,DNAMethylation,Microarray,Preprocessing,QualityControl Author: Octavio Morante-Palacios [cre, aut] Maintainer: Octavio Morante-Palacios URL: https://github.com/omorante/shiny_epico VignetteBuilder: knitr BugReports: https://github.com/omorante/shiny_epico/issues git_url: https://git.bioconductor.org/packages/shinyepico git_branch: RELEASE_3_16 git_last_commit: 628f63d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/shinyepico_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/shinyepico_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/shinyepico_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/shinyepico_1.6.0.tgz vignettes: vignettes/shinyepico/inst/doc/shiny_epico.html vignetteTitles: shinyepico hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/shinyepico/inst/doc/shiny_epico.R dependencyCount: 206 Package: shinyMethyl Version: 1.34.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>= 1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: b766e3a4181e56a57e6ce2e207b87cc7 NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: RELEASE_3_16 git_last_commit: fc4bc50 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/shinyMethyl_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/shinyMethyl_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/shinyMethyl_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/shinyMethyl_1.34.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R dependencyCount: 155 Package: ShortRead Version: 1.56.1 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib, zlibbioc Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 MD5sum: c540e3fc6e7149e6f5e7e9e92657b690 NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Michael Lawrence [ctb], Simon Anders [ctb] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/ShortRead, https://github.com/Bioconductor/ShortRead, https://support.bioconductor.org/tag/ShortRead BugReports: https://github.com/Bioconductor/ShortRead/issues git_url: https://git.bioconductor.org/packages/ShortRead git_branch: RELEASE_3_16 git_last_commit: e5758ff git_last_commit_date: 2022-11-16 Date/Publication: 2022-11-18 source.ver: src/contrib/ShortRead_1.56.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/ShortRead_1.56.1.zip mac.binary.ver: bin/macosx/contrib/4.2/ShortRead_1.56.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ShortRead_1.56.1.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, girafe, HTSeqGenie, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing, SimRAD, STRMPS importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, CellBarcode, chipseq, ChIPseqR, ChIPsim, CircSeqAlignTk, dada2, easyRNASeq, FastqCleaner, GOTHiC, icetea, IONiseR, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, genBaRcode suggestsMe: BiocParallel, CSAR, FLAMES, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, systemPipeRdata, yeastRNASeq dependencyCount: 50 Package: SIAMCAT Version: 2.2.0 Depends: R (>= 3.6.0), mlr3, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot, lmerTest, mlr3learners, mlr3tuning, paradox, lgr Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown, tidyverse, ggpubr License: GPL-3 Archs: x64 MD5sum: df0a8586b2193f669fbf1858ba2bd52c NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut] (), Jakob Wirbel [aut, cre] (), Georg Zeller [aut] (), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: RELEASE_3_16 git_last_commit: c471e3b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SIAMCAT_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIAMCAT_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIAMCAT_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SIAMCAT_2.2.0.tgz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT confounder example, SIAMCAT holdout testing, SIAMCAT meta-analysis, SIAMCAT ML pitfalls, SIAMCAT input, SIAMCAT basic vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_confounder.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_meta.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_ml_pitfalls.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R dependencyCount: 132 Package: SICtools Version: 1.28.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: b09ae746a71f43735e7d96e526d5bfd4 NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: RELEASE_3_16 git_last_commit: cbc5c48 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SICtools_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/SICtools_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SICtools_1.28.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 45 Package: SigCheck Version: 2.30.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 0b13d6bf76771429417ec8bc170f4beb NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: RELEASE_3_16 git_last_commit: f958fbe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SigCheck_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SigCheck_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SigCheck_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SigCheck_2.30.0.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R dependencyCount: 136 Package: sigFeature Version: 1.16.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr, rmarkdown License: GPL (>= 2) MD5sum: 7020610a8e78b929b07b0cb374f95910 NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: RELEASE_3_16 git_last_commit: 71d3d4e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sigFeature_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sigFeature_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sigFeature_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sigFeature_1.16.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.html vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 65 Package: SigFuge Version: 1.36.0 Depends: R (>= 3.5.0), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: 6ab3ff4d834264895d655fa22ee881af NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq, ImmunoOncology Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes git_url: https://git.bioconductor.org/packages/SigFuge git_branch: RELEASE_3_16 git_last_commit: 80e77fe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SigFuge_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SigFuge_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SigFuge_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SigFuge_1.36.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R dependencyCount: 52 Package: siggenes Version: 1.72.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: b86e037261fa175b95e97adc1f975ed5 NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: RELEASE_3_16 git_last_commit: 4f93d1a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/siggenes_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/siggenes_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/siggenes_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/siggenes_1.72.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: coexnet, minfi, trio, XDE, DeSousa2013, INCATome suggestsMe: GCSscore, logicFS dependencyCount: 16 Package: sights Version: 1.24.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 1ecdb67af4330d0b22f1f422f43915f5 NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: RELEASE_3_16 git_last_commit: e09712b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sights_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sights_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sights_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sights_1.24.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 42 Package: signatureSearch Version: 1.12.0 Depends: R(>= 3.6.0), Rcpp, SummarizedExperiment, org.Hs.eg.db Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5, GSEABase, DelayedArray, BiocGenerics, tibble LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, signatureSearchData, DT License: Artistic-2.0 MD5sum: 72eaddcbc4cca49c4adf898038baa655 NeedsCompilation: yes Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [aut], Brendan Gongol [cre, aut], Thomas Girke [aut] Maintainer: Brendan Gongol URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues git_url: https://git.bioconductor.org/packages/signatureSearch git_branch: RELEASE_3_16 git_last_commit: b007162 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/signatureSearch_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/signatureSearch_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/signatureSearch_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/signatureSearch_1.12.0.tgz vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html vignetteTitles: signatureSearch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R dependencyCount: 186 Package: signeR Version: 2.0.2 Depends: R (>= 3.0.2), VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMRplus, parallel, pvclust, ppclust, clue, survival, maxstat, survivalAnalysis, future, VGAM, MASS, kknn, glmnet, e1071, randomForest, ada, future.apply, ggplot2, pROC, pheatmap, RColorBrewer, listenv, reshape2, scales, survminer, dplyr, ggpubr, cowplot, tibble, readr, shiny, shinydashboard, shinycssloaders, shinyWidgets, bsplus, DT, magrittr, tidyr, BiocFileCache, proxy, rtracklayer, BSgenome LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, rmarkdown License: GPL-3 Archs: x64 MD5sum: bbb24d15e170e57b7afe5c8ee126bff3 NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variaton (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/rvalieris/signeR SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: RELEASE_3_16 git_last_commit: f926cfe git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/signeR_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/signeR_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/signeR_2.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/signeR_2.0.2.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html, vignettes/signeR/inst/doc/signeRFlow.html vignetteTitles: signeR, signeRFlow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R, vignettes/signeR/inst/doc/signeRFlow.R dependencyCount: 249 Package: signifinder Version: 1.0.0 Depends: R (>= 4.2.0) Imports: ggplot2, org.Hs.eg.db, patchwork, AnnotationDbi, BiocGenerics, ComplexHeatmap, DGEobj.utils, GSVA, IRanges, SummarizedExperiment, consensusOV, dplyr, ensembldb, ggridges, grid, magrittr, matrixStats, maxstat, methods, openair, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, stats, survival, survminer, viridis Suggests: BiocStyle, knitr, kableExtra, testthat (>= 3.0.0), edgeR, limma License: AGPL-3 Archs: x64 MD5sum: 79344378bc2d1b725a80158968cca848 NeedsCompilation: no Title: Implementations of transcriptional cancer signatures Description: signifinder is an R package for computing and exploring a compendium of tumor signatures. It allows computing signatures scores providing the only gene expression values and returns a single-sample score. Currently, signifinder contains 46 distinct signatures collected from the literature. biocViews: GeneExpression, GeneTarget, ImmunoOncology, BiomedicalInformatics, RNASeq, Microarray, ReportWriting, Visualization Author: Stefania Pirrotta [cre, aut], Enrica Calura [aut] Maintainer: Stefania Pirrotta URL: https://github.com/CaluraLab/signifinder VignetteBuilder: knitr BugReports: https://github.com/CaluraLab/signifinder/issues git_url: https://git.bioconductor.org/packages/signifinder git_branch: RELEASE_3_16 git_last_commit: 082a2c4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/signifinder_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/signifinder_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/signifinder_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/signifinder_1.0.0.tgz vignettes: vignettes/signifinder/inst/doc/signifinder.html vignetteTitles: signifinder vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signifinder/inst/doc/signifinder.R dependencyCount: 256 Package: sigPathway Version: 1.66.2 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 MD5sum: 2a063d125b5ac8f69ad1cd13c5b86d13 NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html git_url: https://git.bioconductor.org/packages/sigPathway git_branch: RELEASE_3_16 git_last_commit: 0351ca6 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/sigPathway_1.66.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/sigPathway_1.66.2.zip mac.binary.ver: bin/macosx/contrib/4.2/sigPathway_1.66.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sigPathway_1.66.2.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome dependencyCount: 0 Package: SigsPack Version: 1.12.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: dcf329d886a27d34206abc243be95cf8 NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: RELEASE_3_16 git_last_commit: ac1d54d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SigsPack_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SigsPack_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SigsPack_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SigsPack_1.12.0.tgz vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 99 Package: sigsquared Version: 1.30.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 4b13729de70197838016b770bacb7ef8 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: RELEASE_3_16 git_last_commit: 1694364 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sigsquared_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sigsquared_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sigsquared_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sigsquared_1.30.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 12 Package: SIM Version: 1.68.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) MD5sum: f96c6bb3a1580c4c2b4fd0d8f701df56 NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: RELEASE_3_16 git_last_commit: 4ebc1cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SIM_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIM_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIM_1.68.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 60 Package: SIMAT Version: 1.30.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: 965a2757e4c4be8a641c9f215568b3b7 NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: RELEASE_3_16 git_last_commit: 7aadd16 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SIMAT_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIMAT_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIMAT_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SIMAT_1.30.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 47 Package: SimBindProfiles Version: 1.36.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: f81a5be31f0a554f56e83a353f4979fc NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer git_url: https://git.bioconductor.org/packages/SimBindProfiles git_branch: RELEASE_3_16 git_last_commit: e9eacaf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SimBindProfiles_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SimBindProfiles_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SimBindProfiles_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SimBindProfiles_1.36.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R dependencyCount: 82 Package: SimBu Version: 1.0.2 Imports: basilisk, BiocParallel, data.table, dplyr, ggplot2, tools, Matrix (>= 1.3.3), methods, phyloseq, proxyC, RColorBrewer, RCurl, reticulate, sparseMatrixStats, SummarizedExperiment, tidyr Suggests: curl, knitr, matrixStats, rmarkdown, Seurat, testthat (>= 3.0.0) License: GPL-3 + file LICENSE MD5sum: b4694a3eb954fb32f0f230e2b026d969 NeedsCompilation: no Title: Simulate Bulk RNA-seq Datasets from Single-Cell Datasets Description: SimBu can be used to simulate bulk RNA-seq datasets with known cell type fractions. You can either use your own single-cell study for the simulation or the sfaira database. Different pre-defined simulation scenarios exist, as are options to run custom simulations. Additionally, expression values can be adapted by adding an mRNA bias, which produces more biologically relevant simulations. biocViews: Software, RNASeq, SingleCell Author: Alexander Dietrich [aut, cre] Maintainer: Alexander Dietrich URL: https://github.com/omnideconv/SimBu VignetteBuilder: knitr BugReports: https://github.com/omnideconv/SimBu/issues git_url: https://git.bioconductor.org/packages/SimBu git_branch: RELEASE_3_16 git_last_commit: 326db4d git_last_commit_date: 2023-03-13 Date/Publication: 2023-03-13 source.ver: src/contrib/SimBu_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/SimBu_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/SimBu_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SimBu_1.0.2.tgz vignettes: vignettes/SimBu/inst/doc/sfaira_vignette.html, vignettes/SimBu/inst/doc/SimBu.html, vignettes/SimBu/inst/doc/simulator_input_output.html, vignettes/SimBu/inst/doc/simulator_scaling_factors.html vignetteTitles: Public Data Integration using Sfaira, Getting started, Inputs and Outputs, Introducing mRNA bias into simulations with scaling factors hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SimBu/inst/doc/sfaira_vignette.R, vignettes/SimBu/inst/doc/SimBu.R, vignettes/SimBu/inst/doc/simulator_input_output.R, vignettes/SimBu/inst/doc/simulator_scaling_factors.R dependencyCount: 108 Package: SIMD Version: 1.16.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 MD5sum: f4e2cfcbc6b5d2dc94dcdc5110d7fcec NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: RELEASE_3_16 git_last_commit: 61c0e03 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SIMD_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIMD_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SIMD_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SIMD_1.16.0.tgz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 13 Package: SimFFPE Version: 1.10.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 Archs: x64 MD5sum: 5a6f64695c7d778346c73e54c7ed020c NeedsCompilation: no Title: NGS Read Simulator for FFPE Tissue Description: The NGS (Next-Generation Sequencing) reads from FFPE (Formalin-Fixed Paraffin-Embedded) samples contain numerous artifact chimeric reads (ACRS), which can lead to false positive structural variant calls. These ACRs are derived from the combination of two single-stranded DNA (ss-DNA) fragments with short reverse complementary regions (SRCRs). This package simulates these artifact chimeric reads as well as normal reads for FFPE samples on the whole genome / several chromosomes / large regions. biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching, DataImport Author: Lanying Wei [aut, cre] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: RELEASE_3_16 git_last_commit: dd10988 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SimFFPE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SimFFPE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SimFFPE_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SimFFPE_1.10.0.tgz vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf vignetteTitles: An introduction to SimFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R dependencyCount: 51 Package: similaRpeak Version: 1.30.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 4e89e7d64ec8640c7d47cb4a84d5e643 NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which quantify the level of similarity between ChIP-Seq profiles. More specifically, the package implements six pseudometrics specialized in pattern similarity detection in ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschênes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: RELEASE_3_16 git_last_commit: 6994ce6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/similaRpeak_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/similaRpeak_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/similaRpeak_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/similaRpeak_1.30.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R suggestsMe: metagene dependencyCount: 2 Package: SIMLR Version: 1.24.3 Depends: R (>= 4.1.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE MD5sum: cacc35ba6d18104a7f63ead0a76a327c NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [aut] (), Bo Wang [aut], Luca De Sano [cre, aut] (), Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: RELEASE_3_16 git_last_commit: 10cd5ee git_last_commit_date: 2023-01-23 Date/Publication: 2023-01-23 source.ver: src/contrib/SIMLR_1.24.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/SIMLR_1.24.3.zip mac.binary.ver: bin/macosx/contrib/4.2/SIMLR_1.24.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SIMLR_1.24.3.tgz vignettes: vignettes/SIMLR/inst/doc/vignette.pdf vignetteTitles: Single-cell Interpretation via Multi-kernel LeaRning (\Biocpkg{SIMLR}) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/vignette.R importsMe: ccImpute, SingleCellSignalR dependencyCount: 14 Package: simpleSeg Version: 1.0.2 Depends: R (>= 3.5.0) Imports: BiocParallel, EBImage, terra, stats, spatstat.geom, S4Vectors, grDevices, SummarizedExperiment, methods, cytomapper Suggests: BiocStyle, testthat (>= 3.0.0), knitr, ggplot2 License: GPL-3 Archs: x64 MD5sum: b34a6f9d422a3a22d7a7dc815f6165ab NeedsCompilation: no Title: A package to perform simple cell segmentation Description: Image segmentation is the process of identifying the borders of individual objects (in this case cells) within an image. This allows for the features of cells such as marker expression and morphology to be extracted, stored and analysed. simpleSeg provides functionality for user friendly, watershed based segmentation on multiplexed cellular images in R based on the intensity of user specified protein marker channels. simpleSeg can also be used for the normalization of single cell data obtained from multiple images. biocViews: Classification, Survival, SingleCell, Normalization Author: Nicolas Canete [aut], Alexander Nicholls [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick URL: https://github.com/SydneyBioX/simpleSeg VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/simpleSeg/issues git_url: https://git.bioconductor.org/packages/simpleSeg git_branch: RELEASE_3_16 git_last_commit: 06a74d8 git_last_commit_date: 2023-01-26 Date/Publication: 2023-01-27 source.ver: src/contrib/simpleSeg_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/simpleSeg_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/simpleSeg_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/simpleSeg_1.0.2.tgz vignettes: vignettes/simpleSeg/inst/doc/simpleSeg.html vignetteTitles: "Introduction to simpleSeg" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleSeg/inst/doc/simpleSeg.R dependencyCount: 161 Package: simplifyEnrichment Version: 1.8.0 Depends: R (>= 3.6.0), BiocGenerics, grid Imports: GOSemSim, ComplexHeatmap (>= 2.7.4), circlize, GetoptLong, digest, tm, GO.db, org.Hs.eg.db, AnnotationDbi, slam, methods, clue, grDevices, graphics, stats, utils, proxyC, Matrix, cluster (>= 1.14.2), colorspace, GlobalOptions (>= 0.1.0) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, clusterProfiler, msigdbr, DOSE, DO.db, reactome.db, flexclust, BiocManager, InteractiveComplexHeatmap (>= 0.99.11), shiny, shinydashboard, cola, hu6800.db, rmarkdown, genefilter, gridtext, fpc License: MIT + file LICENSE MD5sum: 8ddf553a7dc8af6931d516c2c69ed531 NeedsCompilation: no Title: Simplify Functional Enrichment Results Description: A new clustering algorithm, "binary cut", for clustering similarity matrices of functional terms is implemeted in this package. It also provides functions for visualizing, summarizing and comparing the clusterings. biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment Author: Zuguang Gu [aut, cre] () Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simplifyEnrichment, https://simplifyEnrichment.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simplifyEnrichment git_branch: RELEASE_3_16 git_last_commit: d0d147f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/simplifyEnrichment_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/simplifyEnrichment_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/simplifyEnrichment_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/simplifyEnrichment_1.8.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/interactive.html, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.html vignetteTitles: A Shiny app to interactively visualize clustering results, Simplify Functional Enrichment Results, Word Cloud Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/simplifyEnrichment/inst/doc/interactive.R, vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.R, vignettes/simplifyEnrichment/inst/doc/word_cloud_anno.R suggestsMe: cola, InteractiveComplexHeatmap, scITD dependencyCount: 78 Package: sincell Version: 1.30.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) MD5sum: 21d3113e56315d430122b929f5e048ba NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: RELEASE_3_16 git_last_commit: 3d840a6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sincell_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sincell_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sincell_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sincell_1.30.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R dependencyCount: 61 Package: single Version: 1.2.0 Depends: R (>= 4.1) Imports: Biostrings, BiocGenerics, dplyr, GenomicAlignments,IRanges, methods, reshape2, rlang, Rsamtools, stats, stringr, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE Archs: x64 MD5sum: 9bbf3d7d882225ff5caebd8e273a5509 NeedsCompilation: no Title: Accurate consensus sequence from nanopore reads of a gene library Description: Accurate consensus sequence from nanopore reads of a DNA gene library. SINGLe corrects for systematic errors in nanopore sequencing reads of gene libraries and it retrieves true consensus sequences of variants identified by a barcode, needing only a few reads per variant. More information in preprint doi: https://doi.org/10.1101/2020.03.25.007146. biocViews: Software, Sequencing Author: Rocio Espada [aut, cre] () Maintainer: Rocio Espada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/single git_branch: RELEASE_3_16 git_last_commit: 30e7ba3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/single_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/single_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/single_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/single_1.2.0.tgz vignettes: vignettes/single/inst/doc/single.html vignetteTitles: single hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/single/inst/doc/single.R dependencyCount: 63 Package: SingleCellExperiment Version: 1.20.1 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics, GenomicRanges, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq (>= 2.9.1), Rtsne License: GPL-3 MD5sum: 5ecee11cc8266a5bb34cab7277dcc43b NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb] Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: RELEASE_3_16 git_last_commit: 45f87e8 git_last_commit_date: 2023-03-15 Date/Publication: 2023-03-17 source.ver: src/contrib/SingleCellExperiment_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleCellExperiment_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleCellExperiment_1.20.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SingleCellExperiment_1.20.1.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/apply.html, vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Applying over a SingleCellExperiment object, 3. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/apply.R, vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: BASiCS, batchelor, BayesSpace, CATALYST, celda, CellBench, CelliD, CellTrails, CHETAH, clusterExperiment, cydar, cytomapper, DropletUtils, ExperimentSubset, iSEE, iSEEhub, LoomExperiment, MAST, mia, mumosa, NeuCA, POWSC, scAlign, scAnnotatR, scater, scDblFinder, scGPS, schex, scPipe, scran, scuttle, singleCellTK, SpatialExperiment, splatter, switchde, TENxIO, tidySingleCellExperiment, TrajectoryUtils, TreeSummarizedExperiment, tricycle, TSCAN, zinbwave, HCAData, imcdatasets, MouseGastrulationData, MouseThymusAgeing, muscData, scATAC.Explorer, scRNAseq, TENxBrainData, TENxPBMCData, TMExplorer, WeberDivechaLCdata, OSCA.intro, DIscBIO, imcExperiment importsMe: ADImpute, aggregateBioVar, airpart, ANCOMBC, APL, ASURAT, BASiCStan, bayNorm, BEARscc, BUSseq, ccfindR, CellMixS, Cepo, ChromSCape, CiteFuse, clustifyr, CoGAPS, conclus, condiments, corral, destiny, DifferentialRegulation, Dino, distinct, dittoSeq, escape, EWCE, fcoex, FEAST, fishpond, FLAMES, ggspavis, glmGamPoi, GSVA, HIPPO, ILoReg, imcRtools, infercnv, IRISFGM, iSEEu, LineagePulse, lisaClust, mbkmeans, MetaNeighbor, miloR, miQC, MuData, muscat, Nebulosa, netSmooth, NewWave, nnSVG, peco, phemd, pipeComp, SC3, SCArray, scBFA, scCB2, sccomp, scDD, scDDboost, scds, scHOT, scmap, scMerge, scMET, SCnorm, scone, scp, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scTreeViz, slalom, slingshot, Spaniel, SpatialFeatureExperiment, spatialHeatmap, spicyR, SPOTlight, SPsimSeq, standR, Statial, tradeSeq, traviz, treekoR, UCell, VAExprs, VDJdive, velociraptor, Voyager, waddR, zellkonverter, scpdata, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, digitalDLSorteR, mixhvg, SC.MEB, SCRIP, scROSHI suggestsMe: ccImpute, CellaRepertorium, cellxgenedp, DEsingle, FCBF, FuseSOM, genomicInstability, hca, HDF5Array, InteractiveComplexHeatmap, M3Drop, MOFA2, ontoProc, phenopath, progeny, QFeatures, scBubbletree, scFeatureFilter, scPCA, scRecover, SingleR, TREG, dorothea, DuoClustering2018, GSE103322, microbiomeDataSets, TabulaMurisData, simpleSingleCell, Canek, clustree, dyngen, harmony, Platypus, RaceID, Seurat, singleCellHaystack, tidydr dependencyCount: 25 Package: SingleCellSignalR Version: 1.10.0 Depends: R (>= 4.0) Imports: BiocManager, circlize, limma, igraph, gplots, grDevices, edgeR, SIMLR, data.table, pheatmap, stats, Rtsne, graphics, stringr, foreach, multtest, scran, utils, Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b0f12e269ca633d0834b368283418346 NeedsCompilation: no Title: Cell Signalling Using Single Cell RNAseq Data Analysis Description: Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions. biocViews: SingleCell, Network, Clustering, RNASeq, Classification Author: Simon Cabello-Aguilar [aut], Jacques Colinge [cre, aut] Maintainer: Jacques Colinge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: RELEASE_3_16 git_last_commit: f59a771 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SingleCellSignalR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleCellSignalR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleCellSignalR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SingleCellSignalR_1.10.0.tgz vignettes: vignettes/SingleCellSignalR/inst/doc/UsersGuide.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellSignalR/inst/doc/UsersGuide.R suggestsMe: tidySingleCellExperiment, scDiffCom dependencyCount: 98 Package: singleCellTK Version: 2.8.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, AnnotationHub, batchelor, BiocParallel, celldex, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, ensembldb, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, GSVA (>= 1.26.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix, matrixStats, methods, msigdbr, multtest, plotly, plyr, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyjs, SingleR, SoupX, sva, reshape2, shinyalert, circlize, enrichR, celda, shinycssloaders, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, tximport, fishpond, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, scDblFinder, metap, VAM (>= 0.5.3), tibble, rlang, TSCAN, TrajectoryUtils, scuttle, utils, stats Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, spelling, org.Mm.eg.db, stringr, kableExtra, shinythemes, shinyBS, shinyjqui, shinyWidgets, shinyFiles, BiocGenerics, RColorBrewer, fastmap (>= 1.1.0), harmony License: MIT + file LICENSE MD5sum: ec3251341ebd3c2f0c3d53b8392e5dc2 NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: The Single Cell Toolkit (SCTK) in the singleCellTK package provides an interface to popular tools for importing, quality control, analysis, and visualization of single cell RNA-seq data. SCTK allows users to seamlessly integrate tools from various packages at different stages of the analysis workflow. A general "a la carte" workflow gives users the ability access to multiple methods for data importing, calculation of general QC metrics, doublet detection, ambient RNA estimation and removal, filtering, normalization, batch correction or integration, dimensionality reduction, 2-D embedding, clustering, marker detection, differential expression, cell type labeling, pathway analysis, and data exporting. Curated workflows can be used to run Seurat and Celda. Streamlined quality control can be performed on the command line using the SCTK-QC pipeline. Users can analyze their data using commands in the R console or by using an interactive Shiny Graphical User Interface (GUI). Specific analyses or entire workflows can be summarized and shared with comprehensive HTML reports generated by Rmarkdown. Additional documentation and vignettes can be found at camplab.net/sctk. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology, BatchEffect, Normalization, QualityControl, DataImport, GUI Author: Yichen Wang [aut, cre] (), Irzam Sarfraz [aut] (), Rui Hong [aut], Yusuke Koga [aut], Salam Alabdullatif [aut], David Jenkins [aut] (), Vidya Akavoor [aut], Xinyun Cao [aut], Shruthi Bandyadka [aut], Anastasia Leshchyk [aut], Tyler Faits [aut], Mohammed Muzamil Khan [aut], Zhe Wang [aut], W. Evan Johnson [aut] (), Joshua David Campbell [aut] Maintainer: Yichen Wang URL: https://www.camplab.net/sctk/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_16 git_last_commit: 711d2ed git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/singleCellTK_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/singleCellTK_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/singleCellTK_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/singleCellTK_2.8.0.tgz vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html vignetteTitles: 1. Introduction to singleCellTK hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R suggestsMe: celda dependencyCount: 371 Package: SingleMoleculeFootprinting Version: 1.6.0 Depends: R (>= 4.1.0) Imports: BiocGenerics, Biostrings, BSgenome, GenomeInfoDb, GenomicRanges, data.table, grDevices, plyr, IRanges, RColorBrewer, stats, QuasR Suggests: BSgenome.Mmusculus.UCSC.mm10, devtools, ExperimentHub, knitr, parallel, rmarkdown, readr, SingleMoleculeFootprintingData, testthat (>= 3.0.0) License: GPL-3 MD5sum: 9b30f188aa5f2869010d021d039c0957 NeedsCompilation: no Title: Analysis tools for Single Molecule Footprinting (SMF) data Description: SingleMoleculeFootprinting is an R package providing functions to analyze Single Molecule Footprinting (SMF) data. Following the workflow exemplified in its vignette, the user will be able to perform basic data analysis of SMF data with minimal coding effort. Starting from an aligned bam file, we show how to perform quality controls over sequencing libraries, extract methylation information at the single molecule level accounting for the two possible kind of SMF experiments (single enzyme or double enzyme), classify single molecules based on their patterns of molecular occupancy, plot SMF information at a given genomic location biocViews: DNAMethylation, Coverage, NucleosomePositioning, DataRepresentation, Epigenetics, MethylSeq, QualityControl Author: Guido Barzaghi [aut, cre] (), Arnaud Krebs [aut] (), Mike Smith [ctb] () Maintainer: Guido Barzaghi VignetteBuilder: knitr BugReports: https://github.com/Krebslabrep/SingleMoleculeFootprinting/issues git_url: https://git.bioconductor.org/packages/SingleMoleculeFootprinting git_branch: RELEASE_3_16 git_last_commit: e3b5f76 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SingleMoleculeFootprinting_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleMoleculeFootprinting_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleMoleculeFootprinting_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SingleMoleculeFootprinting_1.6.0.tgz vignettes: vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.html vignetteTitles: SingleMoleculeFootprinting hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleMoleculeFootprinting/inst/doc/SingleMoleculeFootprinting.R dependencyCount: 113 Package: SingleR Version: 2.0.0 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocSingular, stats, utils, Rcpp, beachmat, parallel LinkingTo: Rcpp, beachmat, BiocNeighbors Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scater, scran, scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 + file LICENSE MD5sum: 920b6479bf660e746983697893524cd0 NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun URL: https://github.com/LTLA/SingleR SystemRequirements: C++17 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SingleR git_branch: RELEASE_3_16 git_last_commit: f36aaf5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SingleR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SingleR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SingleR_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SingleR_2.0.0.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleR/inst/doc/SingleR.R dependsOnMe: OSCA.advanced, OSCA.basic, OSCA.multisample, OSCA.workflows, SingleRBook importsMe: singleCellTK suggestsMe: tidySingleCellExperiment, tidyseurat dependencyCount: 45 Package: singscore Version: 1.18.0 Depends: R (>= 3.6) Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: pkgdown, BiocStyle, hexbin, knitr, rmarkdown, testthat, covr License: GPL-3 Archs: x64 MD5sum: 9bcae0cc8d8f939786e49fe16e2cf916 NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (), Dharmesh D. Bhuva [aut, cre] (), Malvika Kharbanda [aut] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: RELEASE_3_16 git_last_commit: c1f64d2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/singscore_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/singscore_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/singscore_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/singscore_1.18.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: Single sample scoring hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R importsMe: TBSignatureProfiler, SingscoreAMLMutations, clustermole suggestsMe: vissE, msigdb dependencyCount: 127 Package: SISPA Version: 1.28.0 Depends: R (>= 3.5),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: e9ca2b44074eb99bb9324ba0db3fb35c NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Set Profile Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SISPA git_branch: RELEASE_3_16 git_last_commit: ab1cbfc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SISPA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SISPA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SISPA_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SISPA_1.28.0.tgz vignettes: vignettes/SISPA/inst/doc/SISPA.html vignetteTitles: SISPA:Method for Sample Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SISPA/inst/doc/SISPA.R dependencyCount: 108 Package: sitadela Version: 1.6.0 Depends: R (>= 4.1.0) Imports: Biobase, BiocGenerics, biomaRt, Biostrings, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, methods, parallel, Rsamtools, RSQLite, rtracklayer, S4Vectors, tools, utils Suggests: BSgenome, knitr, rmarkdown, RMySQL, RUnit License: Artistic-2.0 MD5sum: db368b2865c63bd6e0bfcada9ae3a75a NeedsCompilation: no Title: An R package for the easy provision of simple but complete tab-delimited genomic annotation from a variety of sources and organisms Description: Provides an interface to build a unified database of genomic annotations and their coordinates (gene, transcript and exon levels). It is aimed to be used when simple tab-delimited annotations (or simple GRanges objects) are required instead of the more complex annotation Bioconductor packages. Also useful when combinatorial annotation elements are reuired, such as RefSeq coordinates with Ensembl biotypes. Finally, it can download, construct and handle annotations with versioned genes and transcripts (where available, e.g. RefSeq and latest Ensembl). This is particularly useful in precision medicine applications where the latter must be reported. biocViews: Software, WorkflowStep, RNASeq, Transcription, Sequencing, Transcriptomics, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, AlternativeSplicing, DataImport, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/sitadela VignetteBuilder: knitr BugReports: https://github.com/pmoulos/sitadela/issues git_url: https://git.bioconductor.org/packages/sitadela git_branch: RELEASE_3_16 git_last_commit: 0733c99 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sitadela_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sitadela_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sitadela_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sitadela_1.6.0.tgz vignettes: vignettes/sitadela/inst/doc/sitadela.html vignetteTitles: Building a simple annotation database hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sitadela/inst/doc/sitadela.R dependencyCount: 96 Package: sitePath Version: 1.14.0 Depends: R (>= 4.1) Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree, graphics, grDevices, gridExtra, methods, parallel, seqinr, stats, tidytree, utils LinkingTo: Rcpp Suggests: BiocStyle, devtools, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE MD5sum: b4439e3d7f9b7b398398d33572546508 NeedsCompilation: yes Title: Phylogeny-based sequence clustering with site polymorphism Description: Using site polymorphism is one of the ways to cluster DNA/protein sequences but it is possible for the sequences with the same polymorphism on a single site to be genetically distant. This package is aimed at clustering sequences using site polymorphism and their corresponding phylogenetic trees. By considering their location on the tree, only the structurally adjacent sequences will be clustered. However, the adjacent sequences may not necessarily have the same polymorphism. So a branch-and-bound like algorithm is used to minimize the entropy representing the purity of site polymorphism of each cluster. biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP, Software Author: Chengyang Ji [aut, cre, cph] (), Hangyu Zhou [ths], Aiping Wu [ths] Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: RELEASE_3_16 git_last_commit: 3cc4480 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sitePath_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sitePath_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sitePath_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sitePath_1.14.0.tgz vignettes: vignettes/sitePath/inst/doc/sitePath.html vignetteTitles: An introduction to sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePath.R dependencyCount: 67 Package: sizepower Version: 1.68.0 Depends: stats License: LGPL Archs: x64 MD5sum: 34414b6dda2afcd483187d55a3b37da3 NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: RELEASE_3_16 git_last_commit: 124cdde git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sizepower_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sizepower_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sizepower_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sizepower_1.68.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: skewr Version: 1.30.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: 7f8746c530c03e39121e33b80d8a2ac3 NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: RELEASE_3_16 git_last_commit: 6bdb40f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/skewr_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/skewr_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/skewr_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/skewr_1.30.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R dependencyCount: 171 Package: slalom Version: 1.20.2 Depends: R (>= 4.0) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: BiocStyle, knitr, rhdf5, rmarkdown, scater, testthat License: GPL-2 MD5sum: b077d8852c96e7c28ed9b07e8e4ae720 NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. The method uses Bayesian factor analysis with a latent variable model to identify active pathways (selected by the user, e.g. KEGG pathways) that explain variation in a single-cell RNA-seq dataset. This an R/C++ implementation of the f-scLVM Python package. See the publication describing the method at https://doi.org/10.1186/s13059-017-1334-8. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome, KEGG Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: RELEASE_3_16 git_last_commit: b3e3e95 git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/slalom_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/slalom_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.2/slalom_1.20.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/slalom_1.20.2.tgz vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 84 Package: slingshot Version: 2.6.0 Depends: R (>= 4.0), princurve (>= 2.0.4), stats, TrajectoryUtils Imports: graphics, grDevices, igraph, matrixStats, methods, S4Vectors, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, DelayedMatrixStats, knitr, mclust, mgcv, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 Archs: x64 MD5sum: 25f2470b7f5f3d2e2757391ceebbe33c NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] (, rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: RELEASE_3_16 git_last_commit: c594d05 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/slingshot_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/slingshot_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/slingshot_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/slingshot_2.6.0.tgz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology: Comparing Conditions along a Trajectory, Slingshot: Trajectory Inference for Single-Cell Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R dependsOnMe: OSCA.advanced importsMe: condiments, tradeSeq, traviz suggestsMe: Platypus, RaceID dependencyCount: 34 Package: SLqPCR Version: 1.64.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 0462865c03c2248636e5791995b5c260 NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: RELEASE_3_16 git_last_commit: e6a7b74 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SLqPCR_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SLqPCR_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SLqPCR_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SLqPCR_1.64.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R dependencyCount: 1 Package: SMAD Version: 1.14.1 Depends: R (>= 3.6.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: e42457e28fe90d128b8c2ec4c18039d6 NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to prey proteins captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software Author: Qingzhou Zhang [aut, cre] Maintainer: Qingzhou Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: RELEASE_3_16 git_last_commit: 9f5281f git_last_commit_date: 2022-11-27 Date/Publication: 2022-11-27 source.ver: src/contrib/SMAD_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SMAD_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SMAD_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SMAD_1.14.1.tgz vignettes: vignettes/SMAD/inst/doc/quickstart.html vignetteTitles: SMAD Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R dependencyCount: 30 Package: SMAP Version: 1.62.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: x64 MD5sum: 3d4c8cc9d9a9d4774f49997d51d54755 NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson git_url: https://git.bioconductor.org/packages/SMAP git_branch: RELEASE_3_16 git_last_commit: 41a3754 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SMAP_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SMAP_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SMAP_1.62.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SMAP_1.62.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R dependencyCount: 1 Package: SMITE Version: 1.26.0 Depends: R (>= 3.5), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr, rmarkdown License: GPL (>=2) Archs: x64 MD5sum: 4fc1a31c3f315c45a92bdcfe446b0726 NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: RELEASE_3_16 git_last_commit: 1293d18 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SMITE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SMITE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SMITE_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SMITE_1.26.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 150 Package: SNAGEE Version: 1.38.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: c443368f611cb4f692541821c80bd722 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: RELEASE_3_16 git_last_commit: fd19ac4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SNAGEE_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNAGEE_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SNAGEE_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SNAGEE_1.38.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R suggestsMe: SNAGEEdata dependencyCount: 1 Package: snapCGH Version: 1.68.0 Depends: R (>= 3.5.0) Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL MD5sum: 63d0bbd47335c3ea33a5ffa35c714d4a NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni git_url: https://git.bioconductor.org/packages/snapCGH git_branch: RELEASE_3_16 git_last_commit: 6de7bdb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/snapCGH_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snapCGH_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snapCGH_1.68.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/snapCGH_1.68.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP dependencyCount: 93 Package: snapcount Version: 1.10.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: 6e35629518385c48604aed59656f4f7c NeedsCompilation: no Title: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts Description: snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in). biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Rone Charles [aut, cre] Maintainer: Rone Charles URL: https://github.com/langmead-lab/snapcount VignetteBuilder: knitr BugReports: https://github.com/langmead-lab/snapcount/issues git_url: https://git.bioconductor.org/packages/snapcount git_branch: RELEASE_3_16 git_last_commit: 9dea236 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/snapcount_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snapcount_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snapcount_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/snapcount_1.10.0.tgz vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html vignetteTitles: snapcount quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R dependencyCount: 44 Package: snifter Version: 1.8.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, irlba, stats, assertthat Suggests: knitr, rmarkdown, BiocStyle, ggplot2, testthat (>= 3.0.0) License: GPL-3 MD5sum: ac58acec8a50229fab245c980b225615 NeedsCompilation: no Title: R wrapper for the python openTSNE library Description: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan URL: https://bioconductor.org/packages/snifter VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues git_url: https://git.bioconductor.org/packages/snifter git_branch: RELEASE_3_16 git_last_commit: 393f43d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/snifter_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snifter_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snifter_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/snifter_1.8.0.tgz vignettes: vignettes/snifter/inst/doc/snifter.html vignetteTitles: Introduction to snifter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snifter/inst/doc/snifter.R dependsOnMe: OSCA.advanced suggestsMe: scater dependencyCount: 25 Package: snm Version: 1.46.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: d77ab3eb4ad26badb15d9f252eedc533 NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: RELEASE_3_16 git_last_commit: 7440b8c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/snm_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snm_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snm_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/snm_1.46.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: edge, ExpressionNormalizationWorkflow dependencyCount: 51 Package: SNPediaR Version: 1.24.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: cd36052b558077e018db32954cacc6c5 NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: RELEASE_3_16 git_last_commit: df6505b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SNPediaR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNPediaR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SNPediaR_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SNPediaR_1.24.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPhood Version: 1.28.0 Depends: R (>= 3.5.0), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: e509881d53babc41b25387b234856caa NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: christian.arnold@embl.de git_url: https://git.bioconductor.org/packages/SNPhood git_branch: RELEASE_3_16 git_last_commit: a928670 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SNPhood_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNPhood_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SNPhood_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SNPhood_1.28.0.tgz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R dependencyCount: 126 Package: SNPRelate Version: 1.32.2 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, markdown, rmarkdown, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 MD5sum: a8f9659c450f726aa3202848a75fde20 NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: https://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: https://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: RELEASE_3_16 git_last_commit: c9bfefd git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/SNPRelate_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/SNPRelate_1.32.2.zip mac.binary.ver: bin/macosx/contrib/4.2/SNPRelate_1.32.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SNPRelate_1.32.2.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, coxmeg, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.48.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: x64 MD5sum: 73b24be9fc4858866ce533be746f9cfb NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: RELEASE_3_16 git_last_commit: 7d4cec7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/snpStats_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/snpStats_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/snpStats_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/snpStats_1.48.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: MAGAR importsMe: cardelino, DExMA, GeneGeneInteR, gwascat, martini, RVS, scoreInvHap, GenomicTools.fileHandler, GWASbyCluster, PhenotypeSimulator, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, ldblock, omicRexposome, omicsPrint, VariantAnnotation, adjclust, coloc, dartR, genio, pegas, statgenGWAS dependencyCount: 12 Package: soGGi Version: 1.30.0 Depends: R (>= 3.5.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) Archs: x64 MD5sum: 0a34a23bf90610c60d93d9c3de6603b5 NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: RELEASE_3_16 git_last_commit: 81e9ccd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/soGGi_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/soGGi_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/soGGi_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/soGGi_1.30.0.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R importsMe: profileplyr dependencyCount: 88 Package: sojourner Version: 1.11.0 Imports: ggplot2,dplyr,reshape2,gridExtra,EBImage,MASS,R.matlab,Rcpp,fitdistrplus,mclust,minpack.lm,mixtools,mltools,nls2,plyr,sampSurf,scales,shiny,shinyjs,sp,truncnorm,utils,stats,pixmap,rlang,graphics,grDevices,grid,compiler,lattice Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: da7adae9490fe1f96134e5e9ec7b2d2c NeedsCompilation: no Title: Statistical analysis of single molecule trajectories Description: Single molecule tracking has evolved as a novel new approach complementing genomic sequencing, it reports live biophysical properties of molecules being investigated besides properties relating their coding sequence; here we provided "sojourner" package, to address statistical and bioinformatic needs related to the analysis and comprehension of high throughput single molecule tracking data. biocViews: Technology, WorkflowStep Author: Sheng Liu [aut], Sun Jay Yoo [aut], Xiao Na Tang [aut], Young Soo Sung [aut], Carl Wu [aut], Anand Ranjan [ctb], Vu Nguyen [ctb], Sojourner Developer [cre] Maintainer: Sojourner Developer URL: https://github.com/sheng-liu/sojourner VignetteBuilder: knitr BugReports: https://github.com/sheng-liu/sojourner/issues git_url: https://git.bioconductor.org/packages/sojourner git_branch: master git_last_commit: 21765ab git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/sojourner_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sojourner_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sojourner_1.11.0.tgz vignettes: vignettes/sojourner/inst/doc/sojourner-vignette.html vignetteTitles: Sojourner: an R package for statistical analysis of single molecule trajectories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sojourner/inst/doc/sojourner-vignette.R dependencyCount: 118 Package: SomaticSignatures Version: 2.34.0 Depends: R (>= 3.5.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE MD5sum: be0b8997e550142d735f842579df68ae NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/SomaticSignatures git_branch: RELEASE_3_16 git_last_commit: 249b1ef git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SomaticSignatures_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SomaticSignatures_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SomaticSignatures_2.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SomaticSignatures_2.34.0.tgz vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: YAPSA dependencyCount: 166 Package: SOMNiBUS Version: 1.6.0 Depends: R (>= 4.1.0) Imports: graphics, Matrix, mgcv, stats, VGAM Suggests: BiocStyle, covr, devtools, dplyr, knitr, magick, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 9505cdd8f44ac8d62053d133f354494d NeedsCompilation: no Title: Smooth modeling of bisulfite sequencing Description: This package aims to analyse count-based methylation data on predefined genomic regions, such as those obtained by targeted sequencing, and thus to identify differentially methylated regions (DMRs) that are associated with phenotypes or traits. The method is built a rich flexible model that allows for the effects, on the methylation levels, of multiple covariates to vary smoothly along genomic regions. At the same time, this method also allows for sequencing errors and can adjust for variability in cell type mixture. biocViews: DNAMethylation, Regression, Epigenetics, DifferentialMethylation, Sequencing, FunctionalPrediction Author: Kaiqiong Zhao [aut], Kathleen Klein [cre] Maintainer: Kathleen Klein URL: https://github.com/kaiqiong/SOMNiBUS VignetteBuilder: knitr BugReports: https://github.com/kaiqiong/SOMNiBUS/issues git_url: https://git.bioconductor.org/packages/SOMNiBUS git_branch: RELEASE_3_16 git_last_commit: fc7dbe0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SOMNiBUS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SOMNiBUS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SOMNiBUS_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SOMNiBUS_1.6.0.tgz vignettes: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.html vignetteTitles: Analyzing Targeted Bisulfite Sequencing data with SOMNiBUS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SOMNiBUS/inst/doc/SOMNiBUS.R dependencyCount: 13 Package: SpacePAC Version: 1.36.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 763f06a3c11b0d56aa23ea99d89f1c1d NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: RELEASE_3_16 git_last_commit: 5818c8f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SpacePAC_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpacePAC_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpacePAC_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpacePAC_1.36.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC dependencyCount: 30 Package: Spaniel Version: 1.12.0 Depends: R (>= 4.0) Imports: Seurat, SingleCellExperiment, SummarizedExperiment, dplyr, methods, ggplot2, scater (>= 1.13), scran, igraph, shiny, jpeg, magrittr, utils, S4Vectors, DropletUtils, jsonlite, png Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE MD5sum: 9ba550a71f017f8fb6f9b51b6bb0ba81 NeedsCompilation: no Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. Spaniel can import data from either the original Spatial Transcriptomics system or 10X Visium technology. The package contains functions to create a SingleCellExperiment Seurat object and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen [aut, cre] Maintainer: Rachel Queen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: RELEASE_3_16 git_last_commit: 9f51311 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Spaniel_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Spaniel_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Spaniel_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Spaniel_1.12.0.tgz vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.html vignetteTitles: Spaniel 10X Visium hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette-tenX-import.R dependencyCount: 208 Package: sparrow Version: 1.4.0 Depends: R (>= 4.0) Imports: babelgene (>= 21.4), BiocGenerics, BiocParallel, BiocSet, checkmate, circlize, ComplexHeatmap (>= 2.0), data.table (>= 1.10.4), DelayedMatrixStats, edgeR (>= 3.18.1), ggplot2 (>= 2.2.0), graphics, grDevices, GSEABase, irlba, limma, Matrix, methods, plotly (>= 4.9.0), stats, utils, viridis Suggests: AnnotationDbi, BiasedUrn, Biobase (>= 2.24.0), BiocStyle, DESeq2, dplyr, dtplyr, fgsea, GSVA, GO.db, goseq, hexbin, magrittr, matrixStats, msigdbr (>= 7.4.1), KernSmooth, knitr, PANTHER.db (>= 1.0.3), R.utils, reactome.db, rmarkdown, SummarizedExperiment, statmod, stringr, testthat, webshot License: MIT + file LICENSE MD5sum: 4a5c45a2864f4460e3731f526ac08473 NeedsCompilation: no Title: Take command of set enrichment analyses through a unified interface Description: Provides a unified interface to a variety of GSEA techniques from different bioconductor packages. Results are harmonized into a single object and can be interrogated uniformly for quick exploration and interpretation of results. Interactive exploration of GSEA results is enabled through a shiny app provided by a sparrow.shiny sibling package. biocViews: GeneSetEnrichment, Pathways Author: Steve Lianoglou [aut, cre] (), Arkadiusz Gladki [ctb], Denali Therapeutics [fnd] (2018+), Genentech [fnd] (2014 - 2017) Maintainer: Steve Lianoglou URL: https://github.com/lianos/sparrow VignetteBuilder: knitr BugReports: https://github.com/lianos/sparrow/issues git_url: https://git.bioconductor.org/packages/sparrow git_branch: RELEASE_3_16 git_last_commit: 71b4d4c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sparrow_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparrow_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparrow_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sparrow_1.4.0.tgz vignettes: vignettes/sparrow/inst/doc/sparrow.html vignetteTitles: Performing gene set enrichment analyses with sparrow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparrow/inst/doc/sparrow.R suggestsMe: gCrisprTools dependencyCount: 144 Package: sparseDOSSA Version: 1.22.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: d106c8ac7b406d1ed166188f021ef5eb NeedsCompilation: no Title: Sparse Data Observations for Simulating Synthetic Abundance Description: The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis. biocViews: ImmunoOncology, Bayesian, Microbiome, Metagenomics, Software Author: Boyu Ren, Emma Schwager, Timothy Tickle, Curtis Huttenhower Maintainer: Boyu Ren, Emma Schwager , George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparseDOSSA git_branch: RELEASE_3_16 git_last_commit: 70eea3e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sparseDOSSA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparseDOSSA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparseDOSSA_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sparseDOSSA_1.22.0.tgz vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html vignetteTitles: Sparse Data Observations for the Simulation of Synthetic Abundances (sparseDOSSA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R dependencyCount: 25 Package: sparseMatrixStats Version: 1.10.0 Depends: MatrixGenerics (>= 1.5.3) Imports: Rcpp, Matrix, matrixStats (>= 0.60.0), methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 75d3bc14cc6c7f2d0759c29cbeda81a0 NeedsCompilation: yes Title: Summary Statistics for Rows and Columns of Sparse Matrices Description: High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. biocViews: Infrastructure, Software, DataRepresentation Author: Constantin Ahlmann-Eltze [aut, cre] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/sparseMatrixStats SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/sparseMatrixStats/issues git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: RELEASE_3_16 git_last_commit: 75d85ba git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sparseMatrixStats_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparseMatrixStats_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparseMatrixStats_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sparseMatrixStats_1.10.0.tgz vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html vignetteTitles: sparseMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R importsMe: atena, DelayedMatrixStats, GSVA, SimBu, adjclust, CRMetrics suggestsMe: MatrixGenerics, scPCA, Voyager, singleCellHaystack dependencyCount: 11 Package: sparsenetgls Version: 1.16.0 Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 Archs: x64 MD5sum: 8a8efdc7f9153611d00ac70b684bc259 NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: RELEASE_3_16 git_last_commit: e8fad1c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sparsenetgls_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sparsenetgls_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sparsenetgls_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sparsenetgls_1.16.0.tgz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 24 Package: SparseSignatures Version: 2.8.0 Depends: R (>= 4.1.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2 Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: 42a681efcc2193532d1251874043252b NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [cre, aut] (), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [aut] (), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: RELEASE_3_16 git_last_commit: 227242c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SparseSignatures_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SparseSignatures_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SparseSignatures_2.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SparseSignatures_2.8.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/vignette.pdf vignetteTitles: SparseSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/vignette.R dependencyCount: 93 Package: spaSim Version: 1.0.2 Depends: R (>= 4.2.0) Imports: ggplot2, methods, stats, dplyr, spatstat.geom, spatstat.random, SpatialExperiment, SummarizedExperiment, RANN Suggests: RefManageR, BiocStyle, knitr, testthat (>= 3.0.0), sessioninfo, rmarkdown, markdown License: Artistic-2.0 MD5sum: 95c108aa17d92ed93e971d1d0dab9528 NeedsCompilation: no Title: Spatial point data simulator for tissue images Description: A suite of functions for simulating spatial patterns of cells in tissue images. Output images are multitype point data in SingleCellExperiment format. Each point represents a cell, with its 2D locations and cell type. Potential cell patterns include background cells, tumour/immune cell clusters, immune rings, and blood/lymphatic vessels. biocViews: StatisticalMethod, Spatial, BiomedicalInformatics Author: Yuzhou Feng [aut, cre] (), Anna Trigos [aut] () Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/spaSim/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/spaSim git_url: https://git.bioconductor.org/packages/spaSim git_branch: RELEASE_3_16 git_last_commit: f0be014 git_last_commit_date: 2022-11-30 Date/Publication: 2022-11-30 source.ver: src/contrib/spaSim_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/spaSim_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/spaSim_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spaSim_1.0.2.tgz vignettes: vignettes/spaSim/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spaSim/inst/doc/vignette.R dependencyCount: 118 Package: SpatialCPie Version: 1.14.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 9940e07ceb443a0bf4c24d0eb0e24496 NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: RELEASE_3_16 git_last_commit: a271e75 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SpatialCPie_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialCPie_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialCPie_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpatialCPie_1.14.0.tgz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 118 Package: spatialDE Version: 1.4.3 Depends: R (>= 4.1) Imports: reticulate, basilisk (>= 1.9.10), checkmate, stats, SpatialExperiment, methods, SummarizedExperiment, Matrix, S4Vectors, ggplot2, ggrepel, scales, gridExtra Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 8a1e6dff7225af0882157e80d7768c1b NeedsCompilation: no Title: R wrapper for SpatialDE Description: SpatialDE is a method to find spatially variable genes (SVG) from spatial transcriptomics data. This package provides wrappers to use the Python SpatialDE library in R, using reticulate and basilisk. biocViews: Software, Transcriptomics Author: Davide Corso [aut] (), Milan Malfait [aut] (), Lambda Moses [aut] (), Gabriele Sales [cre] Maintainer: Gabriele Sales URL: https://github.com/sales-lab/spatialDE, https://bioconductor.org/packages/spatialDE/ VignetteBuilder: knitr BugReports: https://github.com/sales-lab/spatialDE/issues git_url: https://git.bioconductor.org/packages/spatialDE git_branch: RELEASE_3_16 git_last_commit: 2a4dac9 git_last_commit_date: 2023-01-23 Date/Publication: 2023-02-16 source.ver: src/contrib/spatialDE_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/spatialDE_1.4.3.zip mac.binary.ver: bin/macosx/contrib/4.2/spatialDE_1.4.3.tgz vignettes: vignettes/spatialDE/inst/doc/spatialDE.html vignetteTitles: Introduction to spatialDE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spatialDE/inst/doc/spatialDE.R dependencyCount: 123 Package: SpatialDecon Version: 1.8.0 Depends: R (>= 4.0.0) Imports: grDevices, stats, utils, graphics, SeuratObject, Biobase, GeomxTools, repmis, methods, Matrix, logNormReg (>= 0.4) Suggests: testthat, knitr, rmarkdown, qpdf License: MIT + file LICENSE MD5sum: 32548789c532014650a765197400da79 NeedsCompilation: no Title: Deconvolution of mixed cells from spatial and/or bulk gene expression data Description: Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data", Danaher (2022). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics, Spatial Author: Maddy Griswold [cre, aut], Patrick Danaher [aut] Maintainer: Maddy Griswold VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: RELEASE_3_16 git_last_commit: b379884 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SpatialDecon_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialDecon_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialDecon_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpatialDecon_1.8.0.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_ST.html, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: Use of SpatialDecon in a large GeoMx dataset with GeomxTools, Use of SpatialDecon in a Spatial Transcriptomics dataset, Use of SpatialDecon in a small GeoMx dataet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_NSCLC.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette_ST.R, vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R suggestsMe: GeomxTools dependencyCount: 146 Package: SpatialExperiment Version: 1.8.1 Depends: methods, SingleCellExperiment Imports: rjson, grDevices, magick, utils, S4Vectors, SummarizedExperiment, DropletUtils, BiocGenerics, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle, BumpyMatrix License: GPL-3 MD5sum: 346f3c5815f270e0a50ae487eda2cf21 NeedsCompilation: no Title: S4 Class for Spatially Resolved Transcriptomics Data Description: Defines an S4 class for storing data from spatially resolved transcriptomics (ST) experiments. The class extends SingleCellExperiment to support storage and retrieval of additional information from spot-based and molecule-based ST platforms, including spatial coordinates, images, and image metadata. A specialized constructor function is included for data from the 10x Genomics Visium platform. biocViews: DataRepresentation, DataImport, Infrastructure, ImmunoOncology, GeneExpression, Transcriptomics, SingleCell, Spatial Author: Dario Righelli [aut, cre], Davide Risso [aut], Helena L. Crowell [aut], Lukas M. Weber [aut] Maintainer: Dario Righelli URL: https://github.com/drighelli/SpatialExperiment VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: RELEASE_3_16 git_last_commit: c954f37 git_last_commit_date: 2023-03-04 Date/Publication: 2023-03-05 source.ver: src/contrib/SpatialExperiment_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialExperiment_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialExperiment_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpatialExperiment_1.8.1.tgz vignettes: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.html vignetteTitles: Introduction to the SpatialExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/SpatialExperiment.R dependsOnMe: ExperimentSubset, imcRtools, SPIAT, imcdatasets, MerfishData, MouseGastrulationData, spatialLIBD, STexampleData, TENxVisiumData, VectraPolarisData, WeberDivechaLCdata importsMe: CTSV, cytomapper, ggspavis, lisaClust, nnSVG, spaSim, spatialDE, SpatialFeatureExperiment, spicyR, SpotClean, standR, stJoincount, Voyager, SingleCellMultiModal suggestsMe: GeomxTools, SPOTlight dependencyCount: 96 Package: SpatialFeatureExperiment Version: 1.0.3 Depends: R (>= 4.2.0) Imports: BiocGenerics, BiocParallel, methods, rjson, S4Vectors, sf, SingleCellExperiment, SpatialExperiment, spdep (>= 1.1-7), SummarizedExperiment, stats, utils Suggests: BiocStyle, DropletUtils, knitr, Matrix, rmarkdown, SFEData, testthat (>= 3.0.0) License: Artistic-2.0 MD5sum: ac890508d2b42d035ea46cb68d26171b NeedsCompilation: no Title: Integrating SpatialExperiment with Simple Features in sf Description: A new S4 class integrating Simple Features with the R package sf to bring geospatial data analysis methods based on vector data to spatial transcriptomics. Also implements management of spatial neighborhood graphs and geometric operations. This pakage builds upon SpatialExperiment and SingleCellExperiment, hence methods for these parent classes can still be used. biocViews: DataRepresentation, Transcriptomics, Spatial Author: Lambda Moses [aut, cre] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses URL: https://github.com/pachterlab/SpatialFeatureExperiment VignetteBuilder: knitr BugReports: https://github.com/pachterlab/SpatialFeatureExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialFeatureExperiment git_branch: RELEASE_3_16 git_last_commit: 9a140d4 git_last_commit_date: 2023-01-12 Date/Publication: 2023-01-12 source.ver: src/contrib/SpatialFeatureExperiment_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpatialFeatureExperiment_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.2/SpatialFeatureExperiment_1.0.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpatialFeatureExperiment_1.0.3.tgz vignettes: vignettes/SpatialFeatureExperiment/inst/doc/SFE.html vignetteTitles: Introduction to the SpatialFeatureExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialFeatureExperiment/inst/doc/SFE.R importsMe: Voyager suggestsMe: SFEData dependencyCount: 112 Package: spatialHeatmap Version: 2.4.0 Depends: R (>= 3.5.0) Imports: BiocParallel, BiocFileCache, data.table, dplyr, edgeR, WGCNA, flashClust, htmlwidgets, genefilter, ggplot2, ggdendro, grImport, grid, gridExtra, gplots, igraph, limma, methods, Matrix, rsvg, shiny, dynamicTreeCut, grDevices, graphics, ggplotify, parallel, plotly, rappdirs, reshape2, scater, scuttle, scran, stats, SummarizedExperiment, SingleCellExperiment, shinydashboard, S4Vectors, tibble, utils, visNetwork, UpSetR, xml2, yaml Suggests: av, knitr, rmarkdown, BiocStyle, BiocSingular, RUnit, BiocGenerics, DESeq2, distinct, HDF5Array, kableExtra, magick, ExpressionAtlas, DT, Biobase, GEOquery, pROC, rols, shinyWidgets, shinyjs, htmltools, shinyBS, sortable License: Artistic-2.0 MD5sum: 97791b1e1fa26cb460b988f1dfd1d9e2 NeedsCompilation: no Title: spatialHeatmap Description: The spatialHeatmap package provides functionalities for visualizing cell-, tissue- and organ-specific data of biological assays by coloring the corresponding spatial features defined in anatomical images according to a numeric color key. biocViews: Spatial, Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Jordan Hayes [aut], Le Zhang [aut], Bing Yang [aut], Wolf Frommer [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Maintainer: Jianhai Zhang URL: https://github.com/jianhaizhang/spatialHeatmap VignetteBuilder: knitr BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues git_url: https://git.bioconductor.org/packages/spatialHeatmap git_branch: RELEASE_3_16 git_last_commit: ed77bd2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/spatialHeatmap_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spatialHeatmap_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spatialHeatmap_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spatialHeatmap_2.4.0.tgz vignettes: vignettes/spatialHeatmap/inst/doc/covisualize.html, vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html vignetteTitles: Co-visualizing spatial heatmaps with single cell embedding plots, spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Network Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spatialHeatmap/inst/doc/covisualize.R, vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R dependencyCount: 204 Package: spatzie Version: 1.4.0 Depends: R (>= 4.1) Imports: BiocGenerics, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicInteractions, GenomicRanges, ggplot2, IRanges, matrixStats, motifmatchr, S4Vectors, stats, SummarizedExperiment, TFBSTools, utils Suggests: BiocManager, Biostrings, knitr, pheatmap, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 MD5sum: 8332b628b7f696c5fd500736d1053eec NeedsCompilation: no Title: Identification of enriched motif pairs from chromatin interaction data Description: Identifies motifs that are significantly co-enriched from enhancer-promoter interaction data. While enhancer-promoter annotation is commonly used to define groups of interaction anchors, spatzie also supports co-enrichment analysis between preprocessed interaction anchors. Supports BEDPE interaction data derived from genome-wide assays such as HiC, ChIA-PET, and HiChIP. Can also be used to look for differentially enriched motif pairs between two interaction experiments. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC, Transcription Author: Jennifer Hammelman [aut, cre, cph] (), Konstantin Krismer [aut] (), David Gifford [ths, cph] () Maintainer: Jennifer Hammelman URL: https://spatzie.mit.edu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/spatzie git_branch: RELEASE_3_16 git_last_commit: 22a9bdb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/spatzie_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spatzie_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spatzie_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spatzie_1.4.0.tgz vignettes: vignettes/spatzie/inst/doc/individual-steps.html, vignettes/spatzie/inst/doc/single-call.html vignetteTitles: YY1 ChIA-PET motif analysis (step-by-step), YY1 ChIA-PET motif analysis (single call) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 179 Package: specL Version: 1.32.1 Depends: R (>= 3.6), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.5), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: e48e023dc22b05f77685e44f49b73519 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (), Jonas Grossmann [aut] (), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: RELEASE_3_16 git_last_commit: 2415bca git_last_commit_date: 2022-12-12 Date/Publication: 2022-12-12 source.ver: src/contrib/specL_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/specL_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.2/specL_1.32.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/specL_1.32.1.tgz vignettes: vignettes/specL/inst/doc/report.html, vignettes/specL/inst/doc/specL.html vignetteTitles: Automatic specL Workflow, Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R suggestsMe: msqc1, NestLink dependencyCount: 33 Package: SpeCond Version: 1.52.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: c1ce36dd2bb0e01e8944edf1c780d8b8 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: RELEASE_3_16 git_last_commit: f511876 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SpeCond_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpeCond_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpeCond_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpeCond_1.52.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 45 Package: Spectra Version: 1.8.3 Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>= 1.29.1) Imports: methods, IRanges, MsCoreUtils (>= 1.7.5), graphics, grDevices, stats, tools, utils, fs, BiocGenerics Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0), rmarkdown, vdiffr (>= 1.0.0) License: Artistic-2.0 MD5sum: 3d3899e739014a2bc40b5503ac8db803 NeedsCompilation: no Title: Spectra Infrastructure for Mass Spectrometry Data Description: The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Jan Stanstrup [ctb] () Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/Spectra VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Spectra/issues git_url: https://git.bioconductor.org/packages/Spectra git_branch: RELEASE_3_16 git_last_commit: 4e2d677 git_last_commit_date: 2023-03-21 Date/Publication: 2023-03-22 source.ver: src/contrib/Spectra_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/Spectra_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.2/Spectra_1.8.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Spectra_1.8.3.tgz vignettes: vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/Spectra.R dependsOnMe: MetCirc, MsBackendMassbank, MsBackendMgf, MsBackendMsp, MsBackendRawFileReader importsMe: CompoundDb, MetaboAnnotation, MobilityTransformR, MsExperiment suggestsMe: MetNet, PSMatch, xcms dependencyCount: 27 Package: SpectralTAD Version: 1.14.1 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges, utils Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: f2c8c93d8f1703cfc1ddff92f6a89031 NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Mikhail Dozmorov [aut, cre] (), Kellen Cresswell [aut], John Stansfield [aut] Maintainer: Mikhail Dozmorov URL: https://github.com/dozmorovlab/SpectralTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/SpectralTAD/issues git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: RELEASE_3_16 git_last_commit: 08b54cf git_last_commit_date: 2023-03-13 Date/Publication: 2023-03-13 source.ver: src/contrib/SpectralTAD_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpectralTAD_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SpectralTAD_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpectralTAD_1.14.1.tgz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R suggestsMe: TADCompare dependencyCount: 99 Package: SPEM Version: 1.38.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 1018dfbcf32a28dd000ba36eb4a7d86d NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: RELEASE_3_16 git_last_commit: 43ff6b0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SPEM_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPEM_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPEM_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPEM_1.38.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 9 Package: SPIA Version: 2.50.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 0ee81ea8f562f47e4272289f3c24e83e NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: RELEASE_3_16 git_last_commit: a642a9a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SPIA_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPIA_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPIA_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPIA_2.50.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 14 Package: SPIAT Version: 1.0.4 Depends: R (>= 4.2.0), SpatialExperiment Imports: apcluster (>= 1.4.7), ggplot2 (>= 3.2.1), gridExtra (>= 2.3), gtools (>= 3.8.1), reshape2 (>= 1.4.3), dplyr (>= 0.8.3), plotly (>= 4.9.0), RANN (>= 2.6.1), pracma (>= 2.2.5), dbscan (>= 1.1-5), mmand (>= 1.5.4), tibble (>= 2.1.3), grDevices, stats, utils, vroom, ComplexHeatmap, dittoSeq, spatstat.geom, alphahull, methods, spatstat.explore, raster, sp, elsa, Rtsne, umap, rlang, graphics, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, pkgdown, testthat, xROI License: Artistic-2.0 MD5sum: b21a2edf97f0d59f621da0936717fa99 NeedsCompilation: no Title: Spatial Image Analysis of Tissues Description: SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis. biocViews: BiomedicalInformatics, CellBiology, Spatial, Clustering, DataImport, ImmunoOncology, QualityControl, SingleCell, Software, Visualization Author: Anna Trigos [aut], Yuzhou Feng [aut, cre], Tianpei Yang [aut], Mabel Li [aut], John Zhu [aut], Volkan Ozcoban [aut], Maria Doyle [aut] Maintainer: Yuzhou Feng URL: https://trigosteam.github.io/SPIAT/ VignetteBuilder: knitr BugReports: https://github.com/trigosteam/SPIAT/issues git_url: https://git.bioconductor.org/packages/SPIAT git_branch: RELEASE_3_16 git_last_commit: cff761f git_last_commit_date: 2022-12-19 Date/Publication: 2022-12-20 source.ver: src/contrib/SPIAT_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPIAT_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.2/SPIAT_1.0.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPIAT_1.0.4.tgz vignettes: vignettes/SPIAT/inst/doc/basic_analysis.html, vignettes/SPIAT/inst/doc/cell-colocalisation.html, vignettes/SPIAT/inst/doc/data_reading-formatting.html, vignettes/SPIAT/inst/doc/neighborhood.html, vignettes/SPIAT/inst/doc/quality-control_visualisation.html, vignettes/SPIAT/inst/doc/spatial-heterogeneity.html, vignettes/SPIAT/inst/doc/SPIAT.html, vignettes/SPIAT/inst/doc/tissue-structure.html vignetteTitles: Basic analyses with SPIAT, Quantifying cell colocalisation with SPIAT, Reading in data and data formatting in SPIAT, Identifying cellular neighborhood with SPIAT, Quality control and visualisation with SPIAT, Spatial heterogeneity with SPIAT, Overview of the SPIAT package, Characterising tissue structure with SPIAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPIAT/inst/doc/basic_analysis.R, vignettes/SPIAT/inst/doc/cell-colocalisation.R, vignettes/SPIAT/inst/doc/data_reading-formatting.R, vignettes/SPIAT/inst/doc/neighborhood.R, vignettes/SPIAT/inst/doc/quality-control_visualisation.R, vignettes/SPIAT/inst/doc/spatial-heterogeneity.R, vignettes/SPIAT/inst/doc/SPIAT.R, vignettes/SPIAT/inst/doc/tissue-structure.R dependencyCount: 192 Package: spicyR Version: 1.10.7 Depends: R (>= 4.1) Imports: ggplot2, concaveman, BiocParallel, spatstat.explore, spatstat.geom, lmerTest, BiocGenerics, S4Vectors, lme4, methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats, data.table, dplyr, tidyr, scam, SingleCellExperiment, SpatialExperiment, SummarizedExperiment, ggforce Suggests: BiocStyle, knitr, rmarkdown, pkgdown License: GPL (>=2) MD5sum: 627a4bd11131ac6a0793477e7ce3f631 NeedsCompilation: no Title: Spatial analysis of in situ cytometry data Description: spicyR provides a series of functions to aid in the analysis of both immunofluorescence and mass cytometry imaging data as well as other assays that can deeply phenotype individual cells and their spatial location. biocViews: SingleCell, CellBasedAssays, Spatial Author: Nicolas Canete [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: RELEASE_3_16 git_last_commit: 6d21a37 git_last_commit_date: 2023-02-14 Date/Publication: 2023-02-15 source.ver: src/contrib/spicyR_1.10.7.tar.gz win.binary.ver: bin/windows/contrib/4.2/spicyR_1.10.7.zip mac.binary.ver: bin/macosx/contrib/4.2/spicyR_1.10.7.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spicyR_1.10.7.tgz vignettes: vignettes/spicyR/inst/doc/spicyR.html vignetteTitles: "Introduction to spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/spicyR.R importsMe: lisaClust dependencyCount: 163 Package: SpidermiR Version: 1.28.0 Depends: R (>= 3.0.0) Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, gdata Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: 57a99377453421e3cef1cb0603c372aa NeedsCompilation: no Title: SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data Description: The aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miRTarBase, miRandola, Miranda, PicTar and TargetScan). Furthermore, we also present a statistical test to identify pharmaco-mir relationships using the gene-drug interactions derived by DGIdb and MATADOR database. biocViews: GeneRegulation, miRNA, Network Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/SpidermiR VignetteBuilder: knitr BugReports: https://github.com/claudiacava/SpidermiR/issues git_url: https://git.bioconductor.org/packages/SpidermiR git_branch: RELEASE_3_16 git_last_commit: a28c48e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SpidermiR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpidermiR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SpidermiR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpidermiR_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: StarBioTrek dependencyCount: 64 Package: spikeLI Version: 2.58.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: e40f4f6a6df77dbdddfd08d63201a2bc NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: RELEASE_3_16 git_last_commit: 9c0a6dd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/spikeLI_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spikeLI_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spikeLI_2.58.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spikeLI_2.58.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spiky Version: 1.4.0 Depends: Rsamtools, GenomicRanges, R (>= 3.6.0) Imports: stats, scales, bamlss, methods, tools, IRanges, Biostrings, GenomicAlignments, BlandAltmanLeh, GenomeInfoDb, BSgenome, S4Vectors, graphics, ggplot2, utils Suggests: covr, testthat, equatiomatic, universalmotif, kebabs, ComplexHeatmap, rmarkdown, markdown, knitr, devtools, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg38.masked, BiocManager License: GPL-2 MD5sum: ba3890bcf94f5bf21007ec0c31b98689 NeedsCompilation: no Title: Spike-in calibration for cell-free MeDIP Description: spiky implements methods and model generation for cfMeDIP (cell-free methylated DNA immunoprecipitation) with spike-in controls. CfMeDIP is an enrichment protocol which avoids destructive conversion of scarce template, making it ideal as a "liquid biopsy," but creating certain challenges in comparing results across specimens, subjects, and experiments. The use of synthetic spike-in standard oligos allows diagnostics performed with cfMeDIP to quantitatively compare samples across subjects, experiments, and time points in both relative and absolute terms. biocViews: DifferentialMethylation, DNAMethylation, Normalization, Preprocessing, QualityControl, Sequencing Author: Samantha Wilson [aut], Lauren Harmon [aut], Tim Triche [aut, cre] Maintainer: Tim Triche URL: https://github.com/trichelab/spiky VignetteBuilder: knitr BugReports: https://github.com/trichelab/spiky/issues git_url: https://git.bioconductor.org/packages/spiky git_branch: RELEASE_3_16 git_last_commit: ca789a4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/spiky_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spiky_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spiky_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spiky_1.4.0.tgz vignettes: vignettes/spiky/inst/doc/spiky_vignette.html vignetteTitles: Spiky: Analysing cfMeDIP-seq data with spike-in controls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spiky/inst/doc/spiky_vignette.R dependencyCount: 82 Package: spkTools Version: 1.54.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: 9947bbd0341cddb329e7575fe6d2c30d NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: RELEASE_3_16 git_last_commit: 5beb1e0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/spkTools_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spkTools_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spkTools_1.54.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spkTools_1.54.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 9 Package: splatter Version: 1.22.1 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), edgeR, fitdistrplus, ggplot2, locfit, matrixStats, methods, scales, scater (>= 1.15.16), stats, SummarizedExperiment, utils, crayon, S4Vectors, grDevices Suggests: BiocStyle, covr, cowplot, magick, knitr, limSolve, lme4, progress, pscl, testthat, preprocessCore, rmarkdown, scDD, scran, mfa, phenopath, BASiCS (>= 1.7.10), zinbwave, SparseDC, BiocManager, spelling, igraph, scuttle, BiocSingular, VariantAnnotation, Biostrings, GenomeInfoDb, GenomicRanges, IRanges License: GPL-3 + file LICENSE MD5sum: dc5ba004d4cec876abaa0d3144716117 NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (), Belinda Phipson [aut] (), Christina Azodi [ctb] (), Alicia Oshlack [aut] () Maintainer: Luke Zappia URL: https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_16 git_last_commit: 22d3e70 git_last_commit_date: 2023-01-30 Date/Publication: 2023-01-30 source.ver: src/contrib/splatter_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/splatter_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.2/splatter_1.22.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/splatter_1.22.1.tgz vignettes: vignettes/splatter/inst/doc/splat_params.html, vignettes/splatter/inst/doc/splatPop.html, vignettes/splatter/inst/doc/splatter.html vignetteTitles: Splat simulation parameters, splatPop simulation, An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splat_params.R, vignettes/splatter/inst/doc/splatPop.R, vignettes/splatter/inst/doc/splatter.R importsMe: SCRIP suggestsMe: NewWave, scone, scPCA, SummarizedBenchmark, scellpam dependencyCount: 105 Package: SpliceWiz Version: 1.0.4 Depends: R (>= 3.5.0), NxtIRFdata Imports: ompBAM, methods, stats, utils, tools, parallel, magrittr, Rcpp (>= 1.0.5), data.table, fst, ggplot2, AnnotationHub, BiocFileCache, BiocGenerics, BiocParallel, Biostrings, BSgenome, DelayedArray, DelayedMatrixStats, genefilter, GenomeInfoDb, GenomicRanges, HDF5Array, IRanges, progress, plotly, R.utils, rhdf5, rtracklayer, SummarizedExperiment, S4Vectors, shiny, shinyFiles, shinyWidgets, shinydashboard, rhandsontable, DT, grDevices, heatmaply, pheatmap, matrixStats, RColorBrewer, XML LinkingTo: ompBAM, Rcpp, zlibbioc, RcppProgress Suggests: knitr, rmarkdown, openssl, crayon, egg, DESeq2, limma, DoubleExpSeq, satuRn, edgeR, Rsubread, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 4947ed9baebf2d3a2d62d76d9b01d33a NeedsCompilation: yes Title: Easy, optimized, and accurate alternative splicing analysis in R Description: Reads and fragments aligned to splice junctions can be used to quantify alternative splicing events (ASE). However, overlapping ASEs can confound their quantification. SpliceWiz quantifies ASEs, calculating percent-spliced-in (PSI) using junction reads, and intron retention using IRFinder-based quantitation. Novel filters identify ASEs that are relatively less confounded by overlapping events, whereby PSIs can be calculated with higher confidence. SpliceWiz is ultra-fast, using multi-threaded processing of BAM files. It can be run using a graphical user or command line interfaces. GUI-based interactive visualization of differential ASEs, including novel group-based RNA-seq coverage visualization, simplifies short-read RNA-seq analysis in R. biocViews: Software, Transcriptomics, RNASeq, AlternativeSplicing, Coverage, DifferentialSplicing, DifferentialExpression, GUI, Sequencing Author: Alex Chit Hei Wong [aut, cre, cph], Ulf Schmitz [ctb], William Ritchie [cph] Maintainer: Alex Chit Hei Wong URL: https://github.com/alexchwong/SpliceWiz SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SpliceWiz git_branch: RELEASE_3_16 git_last_commit: be6ae52 git_last_commit_date: 2023-03-27 Date/Publication: 2023-03-29 source.ver: src/contrib/SpliceWiz_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpliceWiz_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.2/SpliceWiz_1.0.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpliceWiz_1.0.4.tgz vignettes: vignettes/SpliceWiz/inst/doc/SW_Cookbook.html, vignettes/SpliceWiz/inst/doc/SW_QuickStart.html vignetteTitles: SpliceWiz: the cookbook, SpliceWiz: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpliceWiz/inst/doc/SW_Cookbook.R, vignettes/SpliceWiz/inst/doc/SW_QuickStart.R dependencyCount: 186 Package: SplicingFactory Version: 1.6.0 Depends: R (>= 4.1) Imports: SummarizedExperiment, methods, stats Suggests: testthat, knitr, rmarkdown, ggplot2, tidyr License: GPL-3 + file LICENSE Archs: x64 MD5sum: 1fcef6a888994540d411327c992c8a8d NeedsCompilation: no Title: Splicing Diversity Analysis for Transcriptome Data Description: The SplicingFactory R package uses transcript-level expression values to analyze splicing diversity based on various statistical measures, like Shannon entropy or the Gini index. These measures can quantify transcript isoform diversity within samples or between conditions. Additionally, the package analyzes the isoform diversity data, looking for significant changes between conditions. biocViews: Transcriptomics, RNASeq, DifferentialSplicing, AlternativeSplicing, TranscriptomeVariant Author: Peter A. Szikora [aut], Tamas Por [aut], Endre Sebestyen [aut, cre] () Maintainer: Endre Sebestyen URL: https://github.com/esebesty/SplicingFactory VignetteBuilder: knitr BugReports: https://github.com/esebesty/SplicingFactory/issues git_url: https://git.bioconductor.org/packages/SplicingFactory git_branch: RELEASE_3_16 git_last_commit: 0535291 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SplicingFactory_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SplicingFactory_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SplicingFactory_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SplicingFactory_1.6.0.tgz vignettes: vignettes/SplicingFactory/inst/doc/SplicingFactory.html vignetteTitles: SplicingFactory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SplicingFactory/inst/doc/SplicingFactory.R dependencyCount: 25 Package: SplicingGraphs Version: 1.38.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.21.2), GenomeInfoDb, GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 0ea878203cd5d6433eae5c0275ddea24 NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/SplicingGraphs BugReports: https://github.com/Bioconductor/SplicingGraphs/issues git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: RELEASE_3_16 git_last_commit: 37dc46c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SplicingGraphs_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SplicingGraphs_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SplicingGraphs_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SplicingGraphs_1.38.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 99 Package: splineTimeR Version: 1.26.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: e053a60bd52bf3218c86b1ffed66d988 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: RELEASE_3_16 git_last_commit: 412a1ac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/splineTimeR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/splineTimeR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/splineTimeR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/splineTimeR_1.26.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R dependencyCount: 64 Package: SPLINTER Version: 1.24.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: fd96ab3b07c650f9f62e90e0cf040dda NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: RELEASE_3_16 git_last_commit: 34a4110 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SPLINTER_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPLINTER_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPLINTER_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPLINTER_1.24.0.tgz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R dependencyCount: 157 Package: splots Version: 1.64.0 Imports: grid, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown, assertthat, HD2013SGI License: LGPL MD5sum: 04b3fba3517a9ff463b8c3e0eca2a18e NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: This package is provided to support legacy code and reverse dependencies, but it should not be used as a dependency for new code development. It provides a single function, plotScreen, for visualising data in microtitre plate or slide format. As a better alternative for such functionality, please consider the platetools package on CRAN (https://cran.r-project.org/package=platetools and https://github.com/Swarchal/platetools), or generic ggplot2 graphics functionality. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splots git_branch: RELEASE_3_16 git_last_commit: 84310b2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/splots_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/splots_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/splots_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/splots_1.64.0.tgz vignettes: vignettes/splots/inst/doc/splots.html vignetteTitles: splots: visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splots.R dependsOnMe: cellHTS2, HD2013SGI importsMe: RNAinteract dependencyCount: 2 Package: SPONGE Version: 1.20.0 Depends: R (>= 3.6) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, tidyverse, caret, dplyr, biomaRt, randomForest, ggridges, cvms, miRBaseConverter, ComplexHeatmap, ggplot2, MetBrewer, rlang, tnet, ggpubr, stringr, tidyr Suggests: testthat, knitr, rmarkdown, visNetwork, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: f178a02957a183a3b3ca0b03cab8d269 NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. The SPONGE package now also includes spongEffects: ceRNA modules offer patient-specific insights into the miRNA regulatory landscape. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression, RandomForest, MachineLearning, Author: Markus List [aut, cre] (), Markus Hoffmann [aut] () Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_16 git_last_commit: 65329b9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SPONGE_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPONGE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPONGE_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPONGE_1.20.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html, vignettes/SPONGE/inst/doc/spongEffects.html vignetteTitles: SPONGE vignette, spongEffects vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R, vignettes/SPONGE/inst/doc/spongEffects.R importsMe: miRspongeR suggestsMe: mirTarRnaSeq dependencyCount: 238 Package: SpotClean Version: 1.0.1 Depends: R (>= 4.2.0), Imports: stats, methods, utils, dplyr, S4Vectors, SummarizedExperiment, SpatialExperiment, Matrix, rhdf5, ggplot2, grid, readbitmap, rjson, tibble, viridis, grDevices, RColorBrewer, Seurat, rlang Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, R.utils, spelling License: GPL-3 MD5sum: ef994e8df5e2584c6418e99f7fd8c023 NeedsCompilation: yes Title: SpotClean adjusts for spot swapping in spatial transcriptomics data Description: SpotClean is a computational method to adjust for spot swapping in spatial transcriptomics data. Recent spatial transcriptomics experiments utilize slides containing thousands of spots with spot-specific barcodes that bind mRNA. Ideally, unique molecular identifiers at a spot measure spot-specific expression, but this is often not the case due to bleed from nearby spots, an artifact we refer to as spot swapping. SpotClean is able to estimate the contamination rate in observed data and decontaminate the spot swapping effect, thus increase the sensitivity and precision of downstream analyses. biocViews: DataImport, RNASeq, Sequencing, GeneExpression, Spatial, SingleCell, Transcriptomics, Preprocessing Author: Zijian Ni [aut, cre] (), Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/SpotClean VignetteBuilder: knitr BugReports: https://github.com/zijianni/SpotClean/issues git_url: https://git.bioconductor.org/packages/SpotClean git_branch: RELEASE_3_16 git_last_commit: 2f754fa git_last_commit_date: 2022-11-22 Date/Publication: 2022-11-23 source.ver: src/contrib/SpotClean_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/SpotClean_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/SpotClean_1.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SpotClean_1.0.1.tgz vignettes: vignettes/SpotClean/inst/doc/SpotClean.html vignetteTitles: SpotClean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpotClean/inst/doc/SpotClean.R dependencyCount: 203 Package: SPOTlight Version: 1.2.0 Depends: R (>= 4.1) Imports: ggplot2, NMF, Matrix, matrixStats, nnls, SingleCellExperiment, stats Suggests: BiocStyle, colorBlindness, ExperimentHub, DelayedArray, ggcorrplot, grid, igraph, jpeg, knitr, methods, png, rmarkdown, scater, scatterpie, scran, Seurat, SeuratObject, SpatialExperiment, SummarizedExperiment, S4Vectors, TabulaMurisSenisData, TENxVisiumData, testthat License: GPL-3 MD5sum: 558ff8d0c3f5413b6d1ec38e556de0d1 NeedsCompilation: no Title: `SPOTlight`: Spatial Transcriptomics Deconvolution Description: `SPOTlight`provides a method to deconvolute spatial transcriptomics spots using a seeded NMF approach along with visualization tools to assess the results. Spatially resolved gene expression profiles are key to understand tissue organization and function. However, novel spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). biocViews: SingleCell, Spatial, StatisticalMethod Author: Marc Elosua-Bayes [aut, cre], Helena L. Crowell [aut] Maintainer: Marc Elosua-Bayes URL: https://github.com/MarcElosua/SPOTlight VignetteBuilder: knitr BugReports: https://github.com/MarcElosua/SPOTlight/issues git_url: https://git.bioconductor.org/packages/SPOTlight git_branch: RELEASE_3_16 git_last_commit: e6d919d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SPOTlight_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPOTlight_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPOTlight_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPOTlight_1.2.0.tgz vignettes: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.html vignetteTitles: "SPOTlight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPOTlight/inst/doc/SPOTlight_kidney.R dependencyCount: 71 Package: spqn Version: 1.10.0 Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment, BiocGenerics Imports: graphics, stats, utils, matrixStats Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3), RUnit License: Artistic-2.0 MD5sum: a8acde152f9706f490760735727e1633 NeedsCompilation: no Title: Spatial quantile normalization Description: The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis. biocViews: NetworkInference, GraphAndNetwork, Normalization Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Yi Wang URL: https://github.com/hansenlab/spqn VignetteBuilder: knitr BugReports: https://github.com/hansenlab/spqn/issues git_url: https://git.bioconductor.org/packages/spqn git_branch: RELEASE_3_16 git_last_commit: 94ec5ce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/spqn_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/spqn_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/spqn_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/spqn_1.10.0.tgz vignettes: vignettes/spqn/inst/doc/spqn.html vignetteTitles: spqn User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spqn/inst/doc/spqn.R dependencyCount: 53 Package: SPsimSeq Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics, edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle License: GPL-2 MD5sum: 1c2cdeaa0b16602ff882f7d8c05c7011 NeedsCompilation: no Title: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data Description: SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size. biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut] Maintainer: Joris Meys URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: RELEASE_3_16 git_last_commit: e7dc76b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SPsimSeq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SPsimSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SPsimSeq_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SPsimSeq_1.8.0.tgz vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html vignetteTitles: Manual for the SPsimSeq package: semi-parametric simulation for bulk and single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R suggestsMe: benchdamic dependencyCount: 142 Package: SQLDataFrame Version: 1.12.0 Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors (>= 0.33.3), Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite, tibble Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray License: GPL-3 MD5sum: f5f9e191ee962da9f8ac38f890fcc6c1 NeedsCompilation: no Title: Representation of SQL database in DataFrame metaphor Description: SQLDataFrame is developed to lazily represent and efficiently analyze SQL-based tables in _R_. SQLDataFrame supports common and familiar 'DataFrame' operations such as '[' subsetting, rbind, cbind, etc.. The internal implementation is based on the widely adopted dplyr grammar and SQL commands. In-memory datasets or plain text files (.txt, .csv, etc.) could also be easily converted into SQLDataFrames objects (which generates a new database on-disk). biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre] (), Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/SQLDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SQLDataFrame/issues git_url: https://git.bioconductor.org/packages/SQLDataFrame git_branch: RELEASE_3_16 git_last_commit: f59afdb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SQLDataFrame_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SQLDataFrame_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SQLDataFrame_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SQLDataFrame_1.12.0.tgz vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.html, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.html vignetteTitles: SQLDataFrame Internal Implementation, SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.R, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.R dependencyCount: 41 Package: SQUADD Version: 1.48.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 3f0067adb17f580c8b30ec01eba997df NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html git_url: https://git.bioconductor.org/packages/SQUADD git_branch: RELEASE_3_16 git_last_commit: bbe8eea git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SQUADD_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SQUADD_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SQUADD_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SQUADD_1.48.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD ERK exemple, SQUADD HOW-TO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R dependencyCount: 6 Package: sRACIPE Version: 1.14.0 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest, doFuture License: MIT + file LICENSE MD5sum: d41ea26e3cea7b9f62acb072bf16305e NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Vivek Kohar [aut, cre] (), Mingyang Lu [aut] Maintainer: Vivek Kohar URL: https://vivekkohar.github.io/sRACIPE/, https://github.com/vivekkohar/sRACIPE, https://geneex.jax.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: RELEASE_3_16 git_last_commit: db2e008 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sRACIPE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sRACIPE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sRACIPE_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sRACIPE_1.14.0.tgz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 97 Package: SRAdb Version: 1.60.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: d3d4bf938492669f0d87a5e7a70dc2a2 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu BugReports: https://github.com/zhujack/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: RELEASE_3_16 git_last_commit: 5f86d95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SRAdb_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SRAdb_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SRAdb_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SRAdb_1.60.0.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R suggestsMe: parathyroidSE dependencyCount: 58 Package: srnadiff Version: 1.18.2 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, devtools, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, edgeR, baySeq, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocStyle, BiocManager LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocManager, BiocStyle License: GPL-3 MD5sum: 7bc319eb88de3845ec9800e7b30af535 NeedsCompilation: yes Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. It reads BAM files as input, and outputs a list differentially regions, together with the adjusted p-values. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: RELEASE_3_16 git_last_commit: 53b3bbd git_last_commit_date: 2023-03-06 Date/Publication: 2023-03-07 source.ver: src/contrib/srnadiff_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/srnadiff_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.2/srnadiff_1.18.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/srnadiff_1.18.2.tgz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: The srnadiff package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R dependencyCount: 211 Package: sscore Version: 1.70.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) Archs: x64 MD5sum: 646d13aa1c3bfad1f1a9932368b908ba NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html git_url: https://git.bioconductor.org/packages/sscore git_branch: RELEASE_3_16 git_last_commit: dcae690 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sscore_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sscore_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sscore_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sscore_1.70.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R dependencyCount: 12 Package: sscu Version: 2.28.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 61ae5fc803ed23477985344353be6f6e NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: RELEASE_3_16 git_last_commit: d4195a5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sscu_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sscu_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sscu_2.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sscu_2.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 29 Package: sSeq Version: 1.36.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 61d94e7af49cfcfa75b16febc9ecb0e6 NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: RELEASE_3_16 git_last_commit: 0345ac5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sSeq_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sSeq_1.36.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq suggestsMe: NBLDA dependencyCount: 3 Package: ssize Version: 1.72.0 Depends: gdata, xtable License: LGPL MD5sum: 9168306f7c0b233a935f340e7086ee0b NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: RELEASE_3_16 git_last_commit: 803d73e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ssize_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssize_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssize_1.72.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ssize_1.72.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R suggestsMe: maGUI dependencyCount: 6 Package: sSNAPPY Version: 1.2.5 Depends: R (>= 4.2.0) Imports: dplyr, magrittr, rlang, stats, purrr, BiocParallel, graphite, Rcpp, tibble, ggplot2, ggraph, igraph, reshape2, org.Hs.eg.db, SummarizedExperiment, edgeR, methods, ggforce, ggnewscale, pheatmap, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocManager, BiocStyle, cowplot, DT, htmltools, knitr, pander, rmarkdown, spelling, stringr, testthat (>= 3.0.0), tidyverse License: GPL-3 MD5sum: b858c629a43f0926eebe655fcdba2cc3 NeedsCompilation: yes Title: Single Sample directioNAl Pathway Perturbation analYsis Description: A single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential directions of changes.Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels. biocViews: Software, GeneExpression, GeneSetEnrichment, GeneSignaling Author: Wenjun Liu [aut, cre] () Maintainer: Wenjun Liu URL: https://wenjun-liu.github.io/sSNAPPY/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Wenjun-Liu/sSNAPPY/issues git_url: https://git.bioconductor.org/packages/sSNAPPY git_branch: RELEASE_3_16 git_last_commit: 483e456 git_last_commit_date: 2023-03-28 Date/Publication: 2023-03-29 source.ver: src/contrib/sSNAPPY_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/4.2/sSNAPPY_1.2.5.zip mac.binary.ver: bin/macosx/contrib/4.2/sSNAPPY_1.2.5.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sSNAPPY_1.2.5.tgz vignettes: vignettes/sSNAPPY/inst/doc/sSNAPPY.html vignetteTitles: Single Sample Directional Pathway Perturbation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSNAPPY/inst/doc/sSNAPPY.R dependencyCount: 116 Package: ssPATHS Version: 1.12.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: f791c02bf518ea43493ec9028761b680 NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: RELEASE_3_16 git_last_commit: 3a19003 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ssPATHS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssPATHS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssPATHS_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ssPATHS_1.12.0.tgz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 115 Package: ssrch Version: 1.14.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat, rmarkdown License: Artistic-2.0 MD5sum: 5d17c0e4261e24df2d4ddc9a8900c972 NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: RELEASE_3_16 git_last_commit: c7ed015 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ssrch_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssrch_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssrch_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ssrch_1.14.0.tgz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R importsMe: HumanTranscriptomeCompendium dependencyCount: 51 Package: ssviz Version: 1.32.0 Depends: R (>= 3.5.0), methods, Rsamtools, Biostrings, reshape, ggplot2, RColorBrewer, stats Suggests: knitr License: GPL-2 MD5sum: cd6def90c1942acb00bb22ba471a14c1 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: RELEASE_3_16 git_last_commit: 08a6ae2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ssviz_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ssviz_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ssviz_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ssviz_1.32.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 64 Package: stageR Version: 1.20.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: d4903f948fd25eb67e2fc0e672a7bb13 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: RELEASE_3_16 git_last_commit: 1bb57ec git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/stageR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/stageR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/stageR_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/stageR_1.20.0.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependsOnMe: rnaseqDTU suggestsMe: MethReg, satuRn dependencyCount: 25 Package: STAN Version: 2.26.2 Depends: R (>= 3.5.0), methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) MD5sum: 89d239aa5a28eefddf036067bb2700f9 NeedsCompilation: yes Title: The Genomic STate ANnotation Package Description: Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing, ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch Maintainer: Rafael Campos-Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STAN git_branch: RELEASE_3_16 git_last_commit: a982cef git_last_commit_date: 2023-01-19 Date/Publication: 2023-01-19 source.ver: src/contrib/STAN_2.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/STAN_2.26.2.zip mac.binary.ver: bin/macosx/contrib/4.2/STAN_2.26.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/STAN_2.26.2.tgz vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R dependencyCount: 156 Package: standR Version: 1.2.2 Depends: R (>= 4.1) Imports: dplyr, SpatialExperiment (>= 1.5.2), SummarizedExperiment, SingleCellExperiment, edgeR, rlang, readr, tibble, ggplot2, tidyr, ruv, limma, patchwork, S4Vectors, Biobase, BiocGenerics, grDevices, stats, methods, ggalluvial, mclustcomp, RUVSeq Suggests: knitr, ExperimentHub, rmarkdown, scater, uwot, ggpubr, ggrepel, cluster, testthat (>= 3.0.0) License: MIT + file LICENSE Archs: x64 MD5sum: 8abb663bb8b29df08f1907c54bf21fb7 NeedsCompilation: no Title: Spatial transcriptome analyses of Nanostring's DSP data in R Description: standR is an user-friendly R package providing functions to assist conducting good-practice analysis of Nanostring's GeoMX DSP data. All functions in the package are built based on the SpatialExperiment object, allowing integration into various spatial transcriptomics-related packages from Bioconductor. standR allows data inspection, quality control, normalization, batch correction and evaluation with informative visualizations. biocViews: Spatial, Transcriptomics, GeneExpression, DifferentialExpression, QualityControl, Normalization, ExperimentHubSoftware Author: Ning Liu [aut, cre] (), Dharmesh D Bhuva [aut] (), Ahmed Mohamed [aut] Maintainer: Ning Liu URL: https://github.com/DavisLaboratory/standR VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/standR/issues git_url: https://git.bioconductor.org/packages/standR git_branch: RELEASE_3_16 git_last_commit: 90adf62 git_last_commit_date: 2023-02-06 Date/Publication: 2023-02-07 source.ver: src/contrib/standR_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/standR_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.2/standR_1.2.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/standR_1.2.2.tgz vignettes: vignettes/standR/inst/doc/standR_introduction.html vignetteTitles: standR_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/standR/inst/doc/standR_introduction.R dependencyCount: 155 Package: staRank Version: 1.40.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: ddd351fb50d195137855f1f956593930 NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: ImmunoOncology, MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg git_url: https://git.bioconductor.org/packages/staRank git_branch: RELEASE_3_16 git_last_commit: fe8800c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/staRank_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/staRank_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/staRank_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/staRank_1.40.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R dependencyCount: 89 Package: StarBioTrek Version: 1.24.0 Depends: R (>= 3.3) Imports: SpidermiR, graphite, AnnotationDbi, e1071, ROCR, MLmetrics, grDevices, igraph, reshape2, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, qgraph, png, grid License: GPL (>= 3) MD5sum: 4fdfb4f3a0e48b0196d748b51250779a NeedsCompilation: no Title: StarBioTrek Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data. biocViews: GeneRegulation, Network, Pathways, KEGG Author: Claudia Cava, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/StarBioTrek VignetteBuilder: knitr BugReports: https://github.com/claudiacava/StarBioTrek/issues git_url: https://git.bioconductor.org/packages/StarBioTrek git_branch: RELEASE_3_16 git_last_commit: 0bb8bb1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/StarBioTrek_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/StarBioTrek_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/StarBioTrek_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/StarBioTrek_1.24.0.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek.R dependencyCount: 97 Package: STATegRa Version: 1.34.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: f27300e8dd916357a779d4dda4fba2d5 NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: RELEASE_3_16 git_last_commit: d046aca git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/STATegRa_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STATegRa_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STATegRa_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/STATegRa_1.34.0.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 56 Package: Statial Version: 1.0.1 Depends: R (>= 4.1.0) Imports: BiocParallel, spatstat.geom, concaveman, tidyverse, data.table, spatstat.explore, dplyr, tidyr, SingleCellExperiment, tibble, stringr, tidyselect, ggplot2, methods, stats Suggests: BiocStyle, knitr, testthat (>= 3.0.0) License: GPL-3 MD5sum: a14af7908d2777f9af41347b79f513e6 NeedsCompilation: no Title: A package to identify changes in cell state relative to spatial associations Description: Statial is a suite of functions for identifying changes in cell state. The functionality provided by Statial provides robust quantification of cell type localisation which are invariant to changes in tissue structure. In addition to this Statial uncovers changes in marker expression associated with varying levels of localisation. These features can be used to explore how the structure and function of different cell types may be altered by the agents they are surrounded with. biocViews: SingleCell, Spatial Author: Farhan Ameen [aut, cre], Sourish Iyengar [aut], Shila Ghazanfar [aut], Ellis Patrick [aut] Maintainer: Farhan Ameen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Statial git_branch: RELEASE_3_16 git_last_commit: dfa4c2a git_last_commit_date: 2022-11-16 Date/Publication: 2022-11-17 source.ver: src/contrib/Statial_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/Statial_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.2/Statial_1.0.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Statial_1.0.1.tgz vignettes: vignettes/Statial/inst/doc/Statial.html vignetteTitles: "Introduction to Statial" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Statial/inst/doc/Statial.R dependencyCount: 164 Package: statTarget Version: 1.28.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown License: LGPL (>= 3) MD5sum: 038dad1d3a88ec499eadec71c487fd31 NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, QC-RLSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: RELEASE_3_16 git_last_commit: 85dbcf8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/statTarget_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/statTarget_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/statTarget_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/statTarget_1.28.0.tgz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 26 Package: STdeconvolve Version: 1.2.0 Depends: R (>= 4.1) Imports: topicmodels, BiocParallel, Matrix, methods, mgcv, ggplot2, scatterpie, viridis, slam, stats, clue, liger, reshape2, graphics, grDevices, utils Suggests: knitr, BiocStyle, rmarkdown, testthat, rcmdcheck, gplots, gridExtra, hash, dplyr, parallel License: GPL-3 MD5sum: 1702064f1c73e0e9edcfa3d6fed9f95f NeedsCompilation: no Title: Reference-free Cell-Type Deconvolution of Multi-Cellular Spatially Resolved Transcriptomics Data Description: STdeconvolve as an unsupervised, reference-free approach to infer latent cell-type proportions and transcriptional profiles within multi-cellular spatially-resolved pixels from spatial transcriptomics (ST) datasets. STdeconvolve builds on latent Dirichlet allocation (LDA), a generative statistical model commonly used in natural language processing for discovering latent topics in collections of documents. In the context of natural language processing, given a count matrix of words in documents, LDA infers the distribution of words for each topic and the distribution of topics in each document. In the context of ST data, given a count matrix of gene expression in multi-cellular ST pixels, STdeconvolve applies LDA to infer the putative transcriptional profile for each cell-type and the proportional representation of each cell-type in each multi-cellular ST pixel. biocViews: Transcriptomics, Visualization, RNASeq, Bayesian, Spatial, Software, GeneExpression Author: Brendan Miller [aut, cre] (), Jean Fan [aut] () Maintainer: Brendan Miller URL: https://jef.works/STdeconvolve/ VignetteBuilder: knitr BugReports: https://github.com/JEFworks-Lab/STdeconvolve/issues git_url: https://git.bioconductor.org/packages/STdeconvolve git_branch: RELEASE_3_16 git_last_commit: e852c52 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/STdeconvolve_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STdeconvolve_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STdeconvolve_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/STdeconvolve_1.2.0.tgz vignettes: vignettes/STdeconvolve/inst/doc/vignette.html vignetteTitles: STdeconvolve Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STdeconvolve/inst/doc/vignette.R dependencyCount: 77 Package: stepNorm Version: 1.70.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: db5dae4267cc0357e7ec70262746ceb8 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: RELEASE_3_16 git_last_commit: 1b73255 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/stepNorm_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/stepNorm_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/stepNorm_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/stepNorm_1.70.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: stJoincount Version: 1.0.0 Depends: R (>= 4.2.0) Imports: graphics, stats, dplyr, magrittr, sp, raster, spdep, ggplot2, pheatmap, grDevices, Seurat, SpatialExperiment, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 74904917e330f82631376e7327aa2ff5 NeedsCompilation: no Title: stJoincount - Join count statistic for quantifying spatial correlation between clusters Description: stJoincount, the application of join count analysis to the spatial transcriptomics dataset. This tool converts the spatial map into a raster object (a two-dimensional image as a rectangular matrix or grid of square pixels), where clusters labelled spots are converted to adjacent pixels with a calculated resolution. A neighbors' list was created based on the rasterized sample, which identifies adjacent and diagonal neighbors for each pixel. After adding binary spatial weights to the neighbors' list, a multi-categorical join count analysis is then performed, allowing all possible combinations of cluster pairings to be tabulated. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance of observed to expected calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance. biocViews: Transcriptomics, Clustering, Spatial, BiocViews, Software Author: Jiarong Song [cre, aut] (), Rania Bassiouni [aut], David Craig [aut] Maintainer: Jiarong Song URL: https://github.com/Nina-Song/stJoincount VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stJoincount git_branch: RELEASE_3_16 git_last_commit: 7b15655 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/stJoincount_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/stJoincount_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/stJoincount_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/stJoincount_1.0.0.tgz vignettes: vignettes/stJoincount/inst/doc/stJoincount-vignette.html vignetteTitles: Introduction to stJoincount hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/stJoincount/inst/doc/stJoincount-vignette.R dependencyCount: 212 Package: strandCheckR Version: 1.16.0 Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments, GenomicRanges, IRanges, Rsamtools, S4Vectors, grid, BiocGenerics, ggplot2, reshape2, stats, gridExtra, TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr, rmarkdown Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 569154fa79aead9bfc49e48fd1f747ca NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Steve Pederson [aut] Maintainer: Thu-Hien To URL: https://github.com/UofABioinformaticsHub/strandCheckR VignetteBuilder: knitr BugReports: https://github.com/UofABioinformaticsHub/strandCheckR/issues git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: RELEASE_3_16 git_last_commit: fda7dad git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/strandCheckR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/strandCheckR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/strandCheckR_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/strandCheckR_1.16.0.tgz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 129 Package: Streamer Version: 1.44.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 MD5sum: 3243c67591846bfea9fd0bea5f241a08 NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/Streamer git_branch: RELEASE_3_16 git_last_commit: ca285bf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Streamer_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Streamer_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Streamer_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Streamer_1.44.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R importsMe: plethy dependencyCount: 10 Package: STRINGdb Version: 2.10.1 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 Archs: x64 MD5sum: ba51dc879a5187d02406748a16b9e5d1 NeedsCompilation: no Title: STRINGdb - Protein-Protein Interaction Networks and Functional Enrichment Analysis Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (https://string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: RELEASE_3_16 git_last_commit: 61be217 git_last_commit_date: 2023-01-17 Date/Publication: 2023-01-17 source.ver: src/contrib/STRINGdb_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/STRINGdb_2.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/STRINGdb_2.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/STRINGdb_2.10.1.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer importsMe: coexnet, IMMAN, netZooR, pwOmics, RITAN, XINA, crosstalkr suggestsMe: epiNEM, GeneNetworkBuilder, martini, netSmooth, PCAN, protti dependencyCount: 42 Package: STROMA4 Version: 1.22.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 MD5sum: c1b42b9b8c370631df85a136ed4b9b8a NeedsCompilation: no Title: Assign Properties to TNBC Patients Description: This package estimates four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These stromal property assignments can be combined to subtype patients. These four stromal properties were identified in Triple negative breast cancer (TNBC) patients and represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D). Additionally this package can also be used to estimate generative properties for the Lehmann subtypes, an alternative TNBC subtyping scheme (PMID: 21633166). biocViews: ImmunoOncology, GeneExpression, BiomedicalInformatics, Classification, Microarray, RNASeq, Software Author: Sadiq Saleh [aut, cre], Michael Hallett [aut] Maintainer: Sadiq Saleh git_url: https://git.bioconductor.org/packages/STROMA4 git_branch: RELEASE_3_16 git_last_commit: be5c8d9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/STROMA4_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/STROMA4_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/STROMA4_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/STROMA4_1.22.0.tgz vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf vignetteTitles: Using the STROMA4 package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R dependencyCount: 19 Package: struct Version: 1.10.0 Depends: R (>= 4.0) Imports: methods,ontologyIndex, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors, rols Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 MD5sum: f982aab8ef27607a0be3b14be1407386 NeedsCompilation: no Title: Statistics in R Using Class-based Templates Description: Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. Ontology lookup has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates. biocViews: WorkflowStep Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: RELEASE_3_16 git_last_commit: 1b988b0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/struct_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/struct_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/struct_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/struct_1.10.0.tgz vignettes: vignettes/struct/inst/doc/struct_templates_and_helper_functions.html vignetteTitles: Introduction to STRUCT - STatistics in R using Class-based Templates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/struct/inst/doc/struct_templates_and_helper_functions.R dependsOnMe: structToolbox importsMe: metabolomicsWorkbenchR dependencyCount: 51 Package: Structstrings Version: 1.14.0 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), Biostrings (>= 2.57.2) Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon, grDevices LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 MD5sum: 412ce168c7dffa496268217bb8283023 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: RELEASE_3_16 git_last_commit: fedd666 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Structstrings_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Structstrings_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Structstrings_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Structstrings_1.14.0.tgz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 26 Package: structToolbox Version: 1.10.1 Depends: R (>= 4.0), struct (>= 1.5.1) Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats, utils Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat, rappdirs License: GPL-3 Archs: x64 MD5sum: 972d651172f9c6270442c95a4ac7ee86 NeedsCompilation: no Title: Data processing & analysis tools for Metabolomics and other omics Description: An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs. biocViews: WorkflowStep, Metabolomics Author: Gavin Rhys Lloyd [aut, cre] (), Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd URL: https://github.com/computational-metabolomics/structToolbox VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: RELEASE_3_16 git_last_commit: c529c12 git_last_commit_date: 2023-02-06 Date/Publication: 2023-02-06 source.ver: src/contrib/structToolbox_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/structToolbox_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/structToolbox_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/structToolbox_1.10.1.tgz vignettes: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html vignetteTitles: Data analysis of metabolomics and other omics datasets using the structToolbox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R suggestsMe: metabolomicsWorkbenchR dependencyCount: 78 Package: StructuralVariantAnnotation Version: 1.14.1 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 4.1.0) Imports: assertthat, Biostrings, stringr, dplyr, methods, rlang, GenomicFeatures, IRanges, S4Vectors, SummarizedExperiment, GenomeInfoDb, Suggests: ggplot2, devtools, testthat (>= 2.1.0), roxygen2, rmarkdown, tidyverse, knitr, ggbio, biovizBase, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, License: GPL-3 + file LICENSE MD5sum: 4a30636d95f897549092112967511793 NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation provides a framework for analysis of structural variants within the Bioconductor ecosystem. This package contains contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (), Ruining Dong [aut] () Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: RELEASE_3_16 git_last_commit: 1936b12 git_last_commit_date: 2023-04-02 Date/Publication: 2023-04-03 source.ver: src/contrib/StructuralVariantAnnotation_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/StructuralVariantAnnotation_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.2/StructuralVariantAnnotation_1.14.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/StructuralVariantAnnotation_1.14.1.tgz vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependsOnMe: svaNUMT, svaRetro dependencyCount: 99 Package: SubCellBarCode Version: 1.14.0 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 1947da90b7589005e078ec89423052fb NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: RELEASE_3_16 git_last_commit: 4fdc616 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SubCellBarCode_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SubCellBarCode_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SubCellBarCode_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SubCellBarCode_1.14.0.tgz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 140 Package: subSeq Version: 1.28.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: 3271ea6acdf8a4370f9fd68a47c72039 NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: RELEASE_3_16 git_last_commit: 90e140c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/subSeq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/subSeq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/subSeq_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/subSeq_1.28.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 52 Package: SUITOR Version: 1.0.0 Depends: R (>= 4.2.0) Imports: stats, utils, graphics, ggplot2, BiocParallel Suggests: devtools, MutationalPatterns, RUnit, BiocManager, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 8d4ec4d05c6239e4b7c3fb66a7616264 NeedsCompilation: yes Title: Selecting the number of mutational signatures through cross-validation Description: An unsupervised cross-validation method to select the optimal number of mutational signatures. A data set of mutational counts is split into training and validation data.Signatures are estimated in the training data and then used to predict the mutations in the validation data. biocViews: Genetics, Software, SomaticMutation Author: DongHyuk Lee [aut], Bin Zhu [aut], Bill Wheeler [cre] Maintainer: Bill Wheeler VignetteBuilder: knitr BugReports: https://github.com/wheelerb/SUITOR/issues git_url: https://git.bioconductor.org/packages/SUITOR git_branch: RELEASE_3_16 git_last_commit: 32c0a62 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SUITOR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SUITOR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SUITOR_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SUITOR_1.0.0.tgz vignettes: vignettes/SUITOR/inst/doc/vignette.pdf vignetteTitles: SUITOR: selecting the number of mutational signatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SUITOR/inst/doc/vignette.R dependencyCount: 45 Package: SummarizedBenchmark Version: 2.16.0 Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo, crayon, tibble Suggests: iCOBRA, BiocStyle, rmarkdown, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) Archs: x64 MD5sum: decc3374f1f4a63a844bf0ea667898e0 NeedsCompilation: no Title: Classes and methods for performing benchmark comparisons Description: This package defines the BenchDesign and SummarizedBenchmark classes for building, executing, and evaluating benchmark experiments of computational methods. The SummarizedBenchmark class extends the RangedSummarizedExperiment object, and is designed to provide infrastructure to store and compare the results of applying different methods to a shared data set. This class provides an integrated interface to store metadata such as method parameters and software versions as well as ground truths (when these are available) and evaluation metrics. biocViews: Software, Infrastructure Author: Alejandro Reyes [aut] (), Patrick Kimes [aut, cre] () Maintainer: Patrick Kimes URL: https://github.com/areyesq89/SummarizedBenchmark, http://bioconductor.org/packages/SummarizedBenchmark/ VignetteBuilder: knitr BugReports: https://github.com/areyesq89/SummarizedBenchmark/issues git_url: https://git.bioconductor.org/packages/SummarizedBenchmark git_branch: RELEASE_3_16 git_last_commit: 3cb2382 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SummarizedBenchmark_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SummarizedBenchmark_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SummarizedBenchmark_2.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SummarizedBenchmark_2.16.0.tgz vignettes: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.html, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.html, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.html vignetteTitles: Case Study: Benchmarking non-R Methods, Case Study: Single-Cell RNA-Seq Simulation, Feature: Error Handling, Feature: Iterative Benchmarking, Feature: Parallelization, SummarizedBenchmark: Class Details, SummarizedBenchmark: Full Case Study, SummarizedBenchmark: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.R, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.R, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.R suggestsMe: benchmarkfdrData2019 dependencyCount: 77 Package: SummarizedExperiment Version: 1.28.0 Depends: R (>= 4.0.0), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.41.5), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.33.7), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), DelayedArray (>= 0.15.10) Suggests: HDF5Array (>= 1.7.5), annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, jsonlite, rhdf5, airway (>= 1.15.1), BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: ebe83ab2c9515946b79b94ecc9a04c3c NeedsCompilation: no Title: SummarizedExperiment container Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan [aut], Valerie Obenchain [aut], Jim Hester [aut], Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/SummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: RELEASE_3_16 git_last_commit: ba55dac git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SummarizedExperiment_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SummarizedExperiment_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SummarizedExperiment_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SummarizedExperiment_1.28.0.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AffiXcan, AllelicImbalance, ASpediaFI, atena, bambu, BDMMAcorrect, BiocSklearn, BiSeq, bnbc, BrainSABER, bsseq, CAGEfightR, celaref, clusterExperiment, compartmap, CoreGx, coseq, csaw, CSSQ, DaMiRseq, deco, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB, DMCHMM, ENmix, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, exomePeak2, ExperimentSubset, ExpressionAtlas, extraChIPs, FEAST, FRASER, GenomicAlignments, GenomicFiles, GenomicSuperSignature, GRmetrics, GSEABenchmarkeR, HelloRanges, hermes, HiCDOC, hipathia, InteractionSet, IntEREst, iSEE, iSEEhex, iSEEhub, ISLET, isomiRs, ivygapSE, lefser, lipidr, LoomExperiment, Macarron, made4, MatrixQCvis, MBASED, methrix, methylPipe, MetNet, mia, miaSim, miaViz, minfi, miRmine, moanin, mpra, MultiAssayExperiment, NADfinder, NBAMSeq, NewWave, OUTRIDER, padma, PDATK, phenomis, PhIPData, profileplyr, qmtools, qsvaR, recount, recount3, RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scAnnotatR, scGPS, scone, scTreeViz, SDAMS, SeqGate, SEtools, SGSeq, signatureSearch, SingleCellExperiment, singleCellTK, SingleR, soGGi, spqn, ssPATHS, stageR, SummarizedBenchmark, survtype, TENxIO, tidySummarizedExperiment, TissueEnrich, TNBC.CMS, TREG, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, benchmarkfdrData2019, BioPlex, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, curatedMetagenomicData, fission, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, MicrobiomeBenchmarkData, microbiomeDataSets, microRNAome, MouseGastrulationData, MouseThymusAgeing, ObMiTi, parathyroidSE, restfulSEData, sampleClassifierData, spatialDmelxsim, spqnData, timecoursedata, tuberculosis, DRomics, ordinalbayes importsMe: ADAM, ADImpute, aggregateBioVar, airpart, ALDEx2, alpine, ANCOMBC, animalcules, anota2seq, APAlyzer, apeglm, APL, appreci8R, ASICS, ASURAT, ATACseqTFEA, AUCell, autonomics, awst, barcodetrackR, BASiCS, BASiCStan, batchelor, BayesSpace, bayNorm, BBCAnalyzer, beer, benchdamic, bigPint, BiocOncoTK, BioNERO, biosigner, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BloodGen3Module, BRGenomics, BUMHMM, BUScorrect, BUSseq, CAGEr, CATALYST, CBEA, cBioPortalData, ccfindR, celda, CelliD, CellMixS, CellTrails, censcyt, Cepo, CeTF, CHETAH, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse, clustifyr, cmapR, CNVfilteR, CNVRanger, coexnet, CoGAPS, comapr, combi, conclus, condiments, consensusDE, consICA, CopyNumberPlots, corral, countsimQC, CTSV, cydar, CyTOFpower, cytoKernel, cytomapper, DAMEfinder, dasper, debCAM, debrowser, DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport, deltaCaptureC, DEP, DEScan2, destiny, DEWSeq, diffcyt, DifferentialRegulation, diffUTR, Dino, DiscoRhythm, distinct, dittoSeq, DMRcate, DominoEffect, doppelgangR, doseR, DropletUtils, Dune, easyRNASeq, eisaR, ELMER, ensemblVEP, epialleleR, epigraHMM, EpiMix, epimutacions, epistack, epivizrData, erma, escape, EWCE, FCBF, fcScan, FindIT2, fishpond, FLAMES, FuseSOM, GARS, gCrisprTools, gemma.R, GeneTonic, genomicInstability, getDEE2, ggbio, ggspavis, Glimma, glmGamPoi, glmSparseNet, GRaNIE, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HTSeqGenie, HumanTranscriptomeCompendium, hummingbird, iasva, icetea, ideal, IgGeneUsage, ILoReg, imcRtools, infercnv, INSPEcT, InterMineR, iSEEu, LACE, LineagePulse, lineagespot, lionessR, lisaClust, MADSEQ, MAI, marr, MAST, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, MetaboAnnotation, metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg, MethylAid, methylscaper, methylumi, MicrobiotaProcess, midasHLA, miloR, MinimumDistance, miRSM, missMethyl, MLInterfaces, MLSeq, monaLisa, MoonlightR, motifbreakR, motifmatchr, MPRAnalyze, MsExperiment, MsFeatures, msgbsR, MSPrep, msqrob2, MuData, MultiDataSet, multiOmicsViz, mumosa, muscat, musicatk, MWASTools, NanoMethViz, Nebulosa, NetActivity, netSmooth, nnSVG, NormalyzerDE, NxtIRFcore, oligoClasses, omicRexposome, OmicsLonDA, omicsPrint, omicsViewer, oncomix, ontoProc, ORFik, OVESEG, PAIRADISE, pairkat, pcaExplorer, peco, PharmacoGx, phemd, phenopath, PhosR, pipeComp, pmp, POMA, POWSC, proActiv, proDA, psichomics, PureCN, QFeatures, qsmooth, quantiseqr, R453Plus1Toolbox, RadioGx, RaggedExperiment, RareVariantVis, RcisTarget, receptLoss, regionReport, regsplice, rgsepd, rifi, Rmmquant, RNAAgeCalc, RNAsense, roar, RolDE, ropls, rScudo, RTCGAToolbox, RTN, satuRn, SBGNview, SC3, SCArray, scater, scBFA, scCB2, scDblFinder, scDD, scDDboost, scds, scHOT, scmap, scMerge, scMET, scmeth, SCnorm, scoreInvHap, scp, scPipe, scran, scReClassify, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, sechm, segmenter, seqCAT, sesame, sigFeature, signifinder, SigsPack, SimBu, simpleSeg, singscore, slalom, slingshot, snapcount, SNPhood, Spaniel, spaSim, SpatialCPie, spatialDE, SpatialExperiment, SpatialFeatureExperiment, spatialHeatmap, spatzie, SPIAT, spicyR, splatter, SpliceWiz, SplicingFactory, SpotClean, srnadiff, sSNAPPY, standR, stJoincount, struct, StructuralVariantAnnotation, supersigs, switchde, systemPipeR, systemPipeTools, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, TEKRABber, tenXplore, tidybulk, tidySingleCellExperiment, TOAST, tomoda, ToxicoGx, tradeSeq, TrajectoryUtils, transformGamPoi, TraRe, traviz, TreeSummarizedExperiment, Trendy, tricycle, TSCAN, tscR, TTMap, TVTB, tximeta, UCell, VAExprs, VariantFiltering, VDJdive, vidger, Voyager, wpm, xcms, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, easierData, emtdata, FieldEffectCrc, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, GSE13015, HMP2Data, IHWpaper, MerfishData, MetaGxBreast, scRNAseq, SingleCellMultiModal, spatialLIBD, TabulaMurisSenisData, TCGAWorkflowData, ExpHunterSuite, fluentGenomics, SingscoreAMLMutations, autoGO, digitalDLSorteR, DWLS, ggpicrust2, HeritSeq, IFAA, imcExperiment, microbial, MOCHA, PlasmaMutationDetector, PlasmaMutationDetector2, pulseTD, RNAseqQC, SC.MEB, SCRIP, scROSHI, VSOLassoBag, xQTLbiolinks suggestsMe: AlpsNMR, AnnotationHub, biobroom, BiocPkgTools, cageminer, dcanr, dce, dearseq, decoupleR, DelayedArray, easier, edgeR, epivizr, epivizrChart, esetVis, fobitools, GENIE3, GenomicRanges, globalSeq, gsean, hca, HDF5Array, HPiP, Informeasure, InteractiveComplexHeatmap, interactiveDisplay, MatrixGenerics, MOFA2, MSnbase, ODER, pathwayPCA, philr, podkat, PSMatch, RiboProfiling, S4Vectors, scFeatureFilter, semisup, sparrow, SPOTlight, svaNUMT, svaRetro, systemPipeShiny, TFutils, updateObject, biotmleData, curatedAdipoArray, curatedTBData, dorothea, DuoClustering2018, GSE103322, pRolocdata, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, Canek, clustree, conos, dyngen, lfc, Platypus, polyRAD, RaceID, seqgendiff, Seurat, Signac, singleCellHaystack, tidydr dependencyCount: 24 Package: Summix Version: 2.4.0 Depends: R (>= 4.1) Imports: nloptr, methods Suggests: rmarkdown, markdown, knitr License: MIT + file LICENSE MD5sum: bc5f7036a26622c777fdd02b400c2996 NeedsCompilation: no Title: Summix: A method to estimate and adjust for population structure in genetic summary data Description: This package contains the Summix method for estimating and adjusting for ancestry in genetic summary allele frequency data. The function summix estimates reference ancestry proportions using a mixture model. The adjAF function produces ancestry adjusted allele frequencies for an observed sample with ancestry proportions matching a target person or sample. biocViews: StatisticalMethod, WholeGenome, Genetics Author: Audrey Hendricks [cre], Stoneman Haley [aut] Maintainer: Audrey Hendricks VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Summix/issues git_url: https://git.bioconductor.org/packages/Summix git_branch: RELEASE_3_16 git_last_commit: b5a6d27 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Summix_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Summix_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Summix_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Summix_2.4.0.tgz vignettes: vignettes/Summix/inst/doc/Summix.html vignetteTitles: Summix.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Summix/inst/doc/Summix.R dependencyCount: 38 Package: supersigs Version: 1.6.0 Depends: R (>= 4.1) Imports: assertthat, caret, dplyr, tidyr, rsample, methods, rlang, utils, Biostrings, stats, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, testthat, VariantAnnotation License: GPL-3 MD5sum: 49cfd96235732486d67c5176889685ce NeedsCompilation: no Title: Supervised mutational signatures Description: Generate SuperSigs (supervised mutational signatures) from single nucleotide variants in the cancer genome. Functions included in the package allow the user to learn supervised mutational signatures from their data and apply them to new data. The methodology is based on the one described in Afsari (2021, ELife). biocViews: FeatureExtraction, Classification, Regression, Sequencing, WholeGenome, SomaticMutation Author: Albert Kuo [aut, cre] (), Yifan Zhang [aut], Bahman Afsari [aut], Cristian Tomasetti [aut] Maintainer: Albert Kuo URL: https://tomasettilab.github.io/supersigs/ VignetteBuilder: knitr BugReports: https://github.com/TomasettiLab/supersigs/issues git_url: https://git.bioconductor.org/packages/supersigs git_branch: RELEASE_3_16 git_last_commit: 83a5936 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/supersigs_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/supersigs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/supersigs_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/supersigs_1.6.0.tgz vignettes: vignettes/supersigs/inst/doc/supersigs.html vignetteTitles: Using supersigs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supersigs/inst/doc/supersigs.R dependencyCount: 108 Package: supraHex Version: 1.36.0 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 MD5sum: 6c220ff4ae16d78ec3b607bc9df620f8 NeedsCompilation: no Title: supraHex: a supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. biocViews: Software, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org git_url: https://git.bioconductor.org/packages/supraHex git_branch: RELEASE_3_16 git_last_commit: b75798f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/supraHex_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/supraHex_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/supraHex_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/supraHex_1.36.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R dependsOnMe: dnet importsMe: Pi suggestsMe: OmnipathR, TCGAbiolinks dependencyCount: 48 Package: surfaltr Version: 1.4.0 Depends: R (>= 4.0) Imports: dplyr (>= 1.0.6), biomaRt (>= 2.46.0), protr (>= 1.6-2), seqinr (>= 4.2-5), ggplot2 (>= 3.3.2), utils (>= 2.10.1), stringr (>= 1.4.0), Biostrings (>= 2.58.0),readr (>= 1.4.0), httr (>= 1.4.2), testthat(>= 3.0.0), xml2(>= 1.3.2), msa (>= 1.22.0), methods (>= 4.0.3) Suggests: knitr, rmarkdown, devtools, kableExtra License: MIT + file LICENSE MD5sum: 61b0802b41060b3b51ab1665e00cc70f NeedsCompilation: no Title: Rapid Comparison of Surface Protein Isoform Membrane Topologies Through surfaltr Description: Cell surface proteins form a major fraction of the druggable proteome and can be used for tissue-specific delivery of oligonucleotide/cell-based therapeutics. Alternatively spliced surface protein isoforms have been shown to differ in their subcellular localization and/or their transmembrane (TM) topology. Surface proteins are hydrophobic and remain difficult to study thereby necessitating the use of TM topology prediction methods such as TMHMM and Phobius. However, there exists a need for bioinformatic approaches to streamline batch processing of isoforms for comparing and visualizing topologies. To address this gap, we have developed an R package, surfaltr. It pairs inputted isoforms, either known alternatively spliced or novel, with their APPRIS annotated principal counterparts, predicts their TM topologies using TMHMM or Phobius, and generates a customizable graphical output. Further, surfaltr facilitates the prioritization of biologically diverse isoform pairs through the incorporation of three different ranking metrics and through protein alignment functions. Citations for programs mentioned here can be found in the vignette. biocViews: Software, Visualization, DataRepresentation, SplicedAlignment, Alignment, MultipleSequenceAlignment, MultipleComparison Author: Pooja Gangras [aut, cre] (), Aditi Merchant [aut] Maintainer: Pooja Gangras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/surfaltr git_branch: RELEASE_3_16 git_last_commit: 855143b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/surfaltr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/surfaltr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/surfaltr_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/surfaltr_1.4.0.tgz vignettes: vignettes/surfaltr/inst/doc/surfaltr_vignette.html vignetteTitles: surfaltr_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/surfaltr/inst/doc/surfaltr_vignette.R dependencyCount: 115 Package: survcomp Version: 1.48.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 MD5sum: dd57b9cd18086ae87f150c7032c9fb68 NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains [aut, cre], Markus Schroeder [aut], Catharina Olsen [aut], Christos Sotiriou [aut], Gianluca Bontempi [aut], John Quackenbush [aut], Samuel Branders [aut], Zhaleh Safikhani [aut] Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: RELEASE_3_16 git_last_commit: 85135e0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/survcomp_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/survcomp_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.2/survcomp_1.48.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/survcomp_1.48.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: metaseqR2, PDATK, pencal, plsRcox, SIGN suggestsMe: glmSparseNet, GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 38 Package: survtype Version: 1.14.0 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: aecb8987bfdc5d67dcf63a180c4674c0 NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: RELEASE_3_16 git_last_commit: cef589f git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/survtype_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/survtype_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/survtype_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/survtype_1.14.0.tgz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 141 Package: sva Version: 3.46.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 MD5sum: 5d69841c0ad44bab0fdc2b5d75ddfaa7 NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: RELEASE_3_16 git_last_commit: 4aac49c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/sva_3.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/sva_3.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/sva_3.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/sva_3.46.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: DeMixT, SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, BioNERO, bnbc, bnem, crossmeta, DaMiRseq, debrowser, DExMA, doppelgangR, edge, KnowSeq, MBECS, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth, qsvaR, SEtools, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, cate, cinaR, dSVA, oncoPredict, scITD, seqgendiff suggestsMe: Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TBSignatureProfiler, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, curatedTBData, FieldEffectCrc, CAGEWorkflow, DGEobj.utils, DRomics, SuperLearner dependencyCount: 71 Package: svaNUMT Version: 1.4.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, StructuralVariantAnnotation, BiocGenerics, Biostrings, R (>= 4.0) Imports: assertthat, stringr, dplyr, methods, rlang, GenomeInfoDb, S4Vectors, GenomicFeatures Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, readr, plyranges, circlize, IRanges, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE Archs: x64 MD5sum: d1e808b533a592484b5cb9de024d8450 NeedsCompilation: no Title: NUMT detection from structural variant calls Description: svaNUMT contains functions for detecting NUMT events from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies NUMTs by nuclear-mitochondrial breakend junctions. The main function reports candidate NUMTs if there is a pair of valid insertion sites found on the nuclear genome within a certain distance threshold. The candidate NUMTs are reported by events. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Ruining Dong [aut, cre] () Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaNUMT/issues git_url: https://git.bioconductor.org/packages/svaNUMT git_branch: RELEASE_3_16 git_last_commit: e339d8e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/svaNUMT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/svaNUMT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/svaNUMT_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/svaNUMT_1.4.0.tgz vignettes: vignettes/svaNUMT/inst/doc/svaNUMT.html vignetteTitles: svaNUMT Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaNUMT/inst/doc/svaNUMT.R dependencyCount: 100 Package: svaRetro Version: 1.4.0 Depends: GenomicRanges, rtracklayer, BiocGenerics, StructuralVariantAnnotation, R (>= 4.0) Imports: VariantAnnotation, assertthat, Biostrings, stringr, dplyr, methods, rlang, GenomicFeatures, GenomeInfoDb, S4Vectors, utils Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, ggplot2, devtools, testthat (>= 2.1.0), roxygen2, knitr, BiocStyle, plyranges, circlize, tictoc, IRanges, stats, SummarizedExperiment, rmarkdown License: GPL-3 + file LICENSE MD5sum: 84f9594d028a7791d26b7ea62dea96a1 NeedsCompilation: no Title: Retrotransposed transcript detection from structural variants Description: svaRetro contains functions for detecting retrotransposed transcripts (RTs) from structural variant calls. It takes structural variant calls in GRanges of breakend notation and identifies RTs by exon-exon junctions and insertion sites. The candidate RTs are reported by events and annotated with information of the inserted transcripts. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation, Coverage, VariantDetection Author: Ruining Dong [aut, cre] () Maintainer: Ruining Dong VignetteBuilder: knitr BugReports: https://github.com/PapenfussLab/svaRetro/issues git_url: https://git.bioconductor.org/packages/svaRetro git_branch: RELEASE_3_16 git_last_commit: 3326991 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/svaRetro_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/svaRetro_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/svaRetro_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/svaRetro_1.4.0.tgz vignettes: vignettes/svaRetro/inst/doc/svaRetro.html vignetteTitles: svaRetro Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/svaRetro/inst/doc/svaRetro.R dependencyCount: 100 Package: SWATH2stats Version: 1.28.0 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: 652a9e37d37dedd750fb32021152278d NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold [aut] Maintainer: Peter Blattmann URL: https://peterblattmann.github.io/SWATH2stats/ VignetteBuilder: knitr BugReports: https://github.com/peterblattmann/SWATH2stats git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: RELEASE_3_16 git_last_commit: d1914af git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SWATH2stats_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SWATH2stats_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SWATH2stats_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SWATH2stats_1.28.0.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 91 Package: SwathXtend Version: 2.20.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: fed95fd0568e907b6bc90a46b34b5c10 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: RELEASE_3_16 git_last_commit: 9a19247 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SwathXtend_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SwathXtend_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SwathXtend_2.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SwathXtend_2.20.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 21 Package: swfdr Version: 1.24.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: 35e13193e069155f2623fbc4f11a3d8c NeedsCompilation: no Title: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek URL: https://github.com/leekgroup/swfdr VignetteBuilder: knitr BugReports: https://github.com/leekgroup/swfdr/issues git_url: https://git.bioconductor.org/packages/swfdr git_branch: RELEASE_3_16 git_last_commit: f59fdb5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/swfdr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/swfdr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/swfdr_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/swfdr_1.24.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 4 Package: switchBox Version: 1.34.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 MD5sum: f54ba376ab76a386ea1c335c328b34e2 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: RELEASE_3_16 git_last_commit: 48f0ae1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/switchBox_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/switchBox_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/switchBox_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/switchBox_1.34.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R importsMe: PDATK suggestsMe: multiclassPairs dependencyCount: 11 Package: switchde Version: 1.24.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) Archs: x64 MD5sum: fb994bc073c1a68a331aad99d5b76ea2 NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: RELEASE_3_16 git_last_commit: e00371e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/switchde_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/switchde_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/switchde_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/switchde_1.24.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 56 Package: synapsis Version: 1.4.0 Depends: R (>= 4.1) Imports: EBImage, stats, utils, graphics Suggests: knitr, rmarkdown, testthat (>= 3.0.0), ggplot2, tidyverse, BiocStyle License: MIT + file LICENSE MD5sum: fc0dccb7e5fea77f2b64e8ffbae911fe NeedsCompilation: no Title: An R package to automate the analysis of double-strand break repair during meiosis Description: Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand. biocViews: Software, SingleCell Author: Lucy McNeill [aut, cre, cph] (), Wayne Crismani [rev, ctb] () Maintainer: Lucy McNeill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapsis git_branch: RELEASE_3_16 git_last_commit: fb0e1df git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/synapsis_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/synapsis_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/synapsis_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/synapsis_1.4.0.tgz vignettes: vignettes/synapsis/inst/doc/synapsis_tutorial.html vignetteTitles: Using-synapsis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/synapsis/inst/doc/synapsis_tutorial.R dependencyCount: 50 Package: synapter Version: 2.22.0 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle, knitr License: GPL-2 MD5sum: 3d1e3e743df356ad82b3a2a9dc7ecb7a NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: RELEASE_3_16 git_last_commit: 948d05b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/synapter_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/synapter_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/synapter_2.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/synapter_2.22.0.tgz vignettes: vignettes/synapter/inst/doc/fragmentmatching.html, vignettes/synapter/inst/doc/synapter.html, vignettes/synapter/inst/doc/synapter2.html vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation, Synapter2 and synergise2 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R, vignettes/synapter/inst/doc/synapter.R, vignettes/synapter/inst/doc/synapter2.R dependsOnMe: synapterdata dependencyCount: 122 Package: synergyfinder Version: 3.6.3 Depends: R (>= 4.0.0) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: ac8d447937580430da7f4269f3ed8c39 NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. It provides efficient implementations for 1.the popular synergy scoring models, including HSA, Loewe, Bliss, and ZIP to quantify the degree of drug combination synergy; 2. higher order drug combination data analysis and synergy landscape visualization for unlimited number of drugs in a combination; 3. statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 4. synergy barometer for harmonizing multiple synergy scoring methods to provide a consensus metric of synergy; 5. evaluation of synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential of the drug combinations. Based on this package, we also provide a web application (https://synergyfinder.org/) for users who prefer graphical user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: https://synergyfinder.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: RELEASE_3_16 git_last_commit: d0479eb git_last_commit_date: 2023-02-13 Date/Publication: 2023-02-13 source.ver: src/contrib/synergyfinder_3.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/synergyfinder_3.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/synergyfinder_3.6.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/synergyfinder_3.6.3.tgz vignettes: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.html vignetteTitles: User tutorial of the SynergyFinder Plus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synergyfinder/inst/doc/User_tutorual_of_the_SynergyFinder_plus.R dependencyCount: 203 Package: SynExtend Version: 1.10.2 Depends: R (>= 4.2.0), DECIPHER (>= 2.24.0) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats, parallel, graphics, grDevices Suggests: BiocStyle, knitr, igraph, markdown, rmarkdown License: GPL-3 MD5sum: 1c6641df1ea66beda1bd88c733cf4666 NeedsCompilation: yes Title: Tools for Working With Synteny Objects Description: Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects. biocViews: Genetics, Clustering, ComparativeGenomics, DataImport Author: Nicholas Cooley [aut, cre] (), Aidan Lakshman [aut, ctb] (), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SynExtend git_branch: RELEASE_3_16 git_last_commit: 7040613 git_last_commit_date: 2022-11-04 Date/Publication: 2022-11-04 source.ver: src/contrib/SynExtend_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/SynExtend_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.2/SynExtend_1.10.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SynExtend_1.10.2.tgz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.pdf vignetteTitles: UsingSynExtend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 36 Package: synlet Version: 1.28.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat, rmarkdown License: GPL-3 Archs: x64 MD5sum: cef6bc081e9d5f31339a8228830e5bed NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: RELEASE_3_16 git_last_commit: 57e2dd8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/synlet_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/synlet_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/synlet_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/synlet_1.28.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 81 Package: SynMut Version: 1.14.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: d46c4e5655a2d4df45fe3188ae962947 NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: RELEASE_3_16 git_last_commit: a8cb1e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/SynMut_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/SynMut_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/SynMut_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/SynMut_1.14.0.tgz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 37 Package: syntenet Version: 1.0.4 Depends: R (>= 4.2) Imports: Rcpp (>= 1.0.8), GenomicRanges, rlang, Biostrings, rtracklayer, utils, methods, igraph, stats, grDevices, RColorBrewer, pheatmap, ggplot2, ggnetwork, intergraph, networkD3 LinkingTo: Rcpp, testthat Suggests: BiocStyle, ggtree, labdsv, covr, knitr, rmarkdown, testthat (>= 3.0.0), xml2 License: GPL-3 MD5sum: 10a7002eab7ce77f6e18fec12e9f3a86 NeedsCompilation: yes Title: Inference And Analysis Of Synteny Networks Description: syntenet can be used to infer synteny networks from whole-genome protein sequences and analyze them. Anchor pairs are detected with the MCScanX algorithm, which was ported to this package with the Rcpp framework for R and C++ integration. Anchor pairs from synteny analyses are treated as an undirected unweighted graph (i.e., a synteny network), and users can perform: i. network clustering; ii. phylogenomic profiling (by identifying which species contain which clusters) and; iii. microsynteny-based phylogeny reconstruction with maximum likelihood. biocViews: Software, NetworkInference, FunctionalGenomics, ComparativeGenomics, Phylogenetics, SystemsBiology, GraphAndNetwork, WholeGenome, Network Author: Fabrício Almeida-Silva [aut, cre] (), Tao Zhao [aut] (), Kristian K Ullrich [aut] (), Yves Van de Peer [aut] () Maintainer: Fabrício Almeida-Silva URL: https://github.com/almeidasilvaf/syntenet VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/syntenet git_url: https://git.bioconductor.org/packages/syntenet git_branch: RELEASE_3_16 git_last_commit: 2d45907 git_last_commit_date: 2023-02-01 Date/Publication: 2023-02-02 source.ver: src/contrib/syntenet_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.2/syntenet_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.2/syntenet_1.0.4.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/syntenet_1.0.4.tgz vignettes: vignettes/syntenet/inst/doc/syntenet.html vignetteTitles: Inference and Analysis of Synteny Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/syntenet/inst/doc/syntenet.R dependencyCount: 120 Package: systemPipeR Version: 2.4.0 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, SummarizedExperiment, ggplot2, yaml, stringr, magrittr, S4Vectors, crayon, BiocGenerics, htmlwidgets Suggests: BiocStyle, knitr, rmarkdown, systemPipeRdata, GenomicAlignments, grid, dplyr, testthat, rjson, annotate, AnnotationDbi, kableExtra, GO.db, GenomeInfoDb, DT, rtracklayer, limma, edgeR, DESeq2, IRanges, batchtools, GenomicFeatures (>= 1.31.3), VariantAnnotation (>= 1.25.11) License: Artistic-2.0 Archs: x64 MD5sum: cb33df0647b67074761e0df3cc5caee4 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, Workflow Author: Thomas Girke Maintainer: Thomas Girke URL: https://systempipe.org/, https://github.com/tgirke/systemPipeR SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: RELEASE_3_16 git_last_commit: a5ab185 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/systemPipeR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/systemPipeR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/systemPipeR_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/systemPipeR_2.4.0.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflows collection, systemPipeR: Workflow and Visualization Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind, RNASeqR suggestsMe: systemPipeShiny, systemPipeTools, systemPipeRdata dependencyCount: 101 Package: systemPipeShiny Version: 1.8.1 Depends: R (>= 4.0.0), shiny (>= 1.6.0), spsUtil (>= 0.2.2), spsComps (>= 0.3.2), drawer (>= 0.2) Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, htmltools, glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce, shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus (>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats, styler, tibble, utils, vroom (>= 1.3.1), yaml, R6, RSQLite, openssl Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR (>= 2.2.0), systemPipeRdata (>= 2.0.0), rhandsontable, zip, callr, pushbar, fs, readr, R.utils, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, Rtsne, UpSetR, tidyr, esquisse (>= 1.1.0), cicerone License: GPL (>= 3) MD5sum: ccc4e2e8ec24202683e481fcb851cb24 NeedsCompilation: no Title: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization Description: systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. biocViews: ShinyApps, Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy [aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke [aut] Maintainer: Le Zhang URL: https://systempipe.org/sps, https://github.com/systemPipeR/systemPipeShiny VignetteBuilder: knitr BugReports: https://github.com/systemPipeR/systemPipeShiny/issues git_url: https://git.bioconductor.org/packages/systemPipeShiny git_branch: RELEASE_3_16 git_last_commit: ef7bc0a git_last_commit_date: 2022-11-04 Date/Publication: 2022-11-06 source.ver: src/contrib/systemPipeShiny_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/systemPipeShiny_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/systemPipeShiny_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/systemPipeShiny_1.8.1.tgz vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html vignetteTitles: systemPipeShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R dependencyCount: 123 Package: systemPipeTools Version: 1.6.0 Imports: DESeq2, GGally, Rtsne, SummarizedExperiment, ape, dplyr, ggplot2, ggrepel, ggtree, glmpca, pheatmap, plotly, tibble, magrittr, DT, stats Suggests: systemPipeR, knitr, BiocStyle, rmarkdown, testthat (>= 3.0.0), BiocGenerics, Biostrings, methods License: Artistic-2.0 MD5sum: 51a1df70131eff836ee669a1bbf6750c NeedsCompilation: no Title: Tools for data visualization Description: systemPipeTools package extends the widely used systemPipeR (SPR) workflow environment with an enhanced toolkit for data visualization, including utilities to automate the data visualizaton for analysis of differentially expressed genes (DEGs). systemPipeTools provides data transformation and data exploration functions via scatterplots, hierarchical clustering heatMaps, principal component analysis, multidimensional scaling, generalized principal components, t-Distributed Stochastic Neighbor embedding (t-SNE), and MA and volcano plots. All these utilities can be integrated with the modular design of the systemPipeR environment that allows users to easily substitute any of these features and/or custom with alternatives. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering, DifferentialExpression, MultidimensionalScaling, PrincipalComponent Author: Daniela Cassol [aut, cre], Ponmathi Ramasamy [aut], Le Zhang [aut], Thomas Girke [aut] Maintainer: Daniela Cassol VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeTools git_branch: RELEASE_3_16 git_last_commit: 89b8880 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/systemPipeTools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/systemPipeTools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/systemPipeTools_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/systemPipeTools_1.6.0.tgz vignettes: vignettes/systemPipeTools/inst/doc/systemPipeTools.html vignetteTitles: systemPipeTools: Data Visualizations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeTools/inst/doc/systemPipeTools.R dependencyCount: 144 Package: TADCompare Version: 1.8.0 Depends: R (>= 4.0) Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2, tidyr, ggpubr, RColorBrewer, reshape2, cowplot Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr, pheatmap, rGREAT, SpectralTAD License: MIT + file LICENSE MD5sum: 07692165540958508adabd852bee8a77 NeedsCompilation: no Title: TADCompare: Identification and characterization of differential TADs Description: TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Kellen Cresswell , Mikhail Dozmorov Maintainer: Kellen Cresswell URL: https://github.com/dozmorovlab/TADCompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/TADCompare/issues git_url: https://git.bioconductor.org/packages/TADCompare git_branch: RELEASE_3_16 git_last_commit: ca77240 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TADCompare_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TADCompare_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TADCompare_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TADCompare_1.8.0.tgz vignettes: vignettes/TADCompare/inst/doc/Input_Data.html, vignettes/TADCompare/inst/doc/Ontology_Analysis.html, vignettes/TADCompare/inst/doc/TADCompare.html vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis, TAD comparison between two conditions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R, vignettes/TADCompare/inst/doc/Ontology_Analysis.R, vignettes/TADCompare/inst/doc/TADCompare.R dependencyCount: 146 Package: tanggle Version: 1.4.0 Depends: R (>= 4.1), ggplot2 (>= 2.2.0), ggtree Imports: ape (>= 5.0), phangorn (>= 2.5), utils, methods Suggests: tinytest, BiocStyle, ggimage, knitr, rmarkdown License: Artistic-2.0 MD5sum: 2480f1b040c62cfd66b0b69ebf31caf5 NeedsCompilation: no Title: Visualization of Phylogenetic Networks Description: Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) and 'phangorn' Schliep (2011) . biocViews: Software, Visualization, Phylogenetics, Alignment, Clustering, MultipleSequenceAlignment, DataImport Author: Klaus Schliep [aut, cre] (), Marta Vidal-Garcia [aut], Claudia Solis-Lemus [aut] (), Leann Biancani [aut], Eren Ada [aut], L. Francisco Henao Diaz [aut], Guangchuang Yu [ctb] Maintainer: Klaus Schliep URL: https://klausvigo.github.io/tanggle, https://github.com/KlausVigo/tanggle VignetteBuilder: knitr BugReports: https://github.com/KlausVigo/tanggle/issues git_url: https://git.bioconductor.org/packages/tanggle git_branch: RELEASE_3_16 git_last_commit: b9b1438 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tanggle_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tanggle_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tanggle_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tanggle_1.4.0.tgz vignettes: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.html, vignettes/tanggle/inst/doc/tanggle_vignette.html vignetteTitles: ***tanggle***: Visualización de redes filogenéticas con *ggplot2*, ***tanggle***: Visualization of phylogenetic networks in a *ggplot2* framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tanggle/inst/doc/tanggle_vignette_espanol.R, vignettes/tanggle/inst/doc/tanggle_vignette.R dependencyCount: 63 Package: TAPseq Version: 1.10.0 Depends: R (>= 4.0.0) Imports: methods, GenomicAlignments, GenomicRanges, IRanges, BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome, GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer, BiocStyle License: MIT + file LICENSE MD5sum: c017fa2efd661038a4cb667c9a5c7ff8 NeedsCompilation: no Title: Targeted scRNA-seq primer design for TAP-seq Description: Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn. biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens Author: Andreas R. Gschwind [aut, cre] (), Lars Velten [aut] (), Lars M. Steinmetz [aut] Maintainer: Andreas R. Gschwind URL: https://github.com/argschwind/TAPseq SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TAPseq git_branch: RELEASE_3_16 git_last_commit: 9a1ac4c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TAPseq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TAPseq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TAPseq_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TAPseq_1.10.0.tgz vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html, vignettes/TAPseq/inst/doc/tapseq_target_genes.html vignetteTitles: TAP-seq primer design workflow, Select target genes for TAP-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R, vignettes/TAPseq/inst/doc/tapseq_target_genes.R dependencyCount: 97 Package: target Version: 1.12.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 MD5sum: 8a1125a27214acd5e92bb42b603dbb38 NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: RELEASE_3_16 git_last_commit: 2b4f16e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/target_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/target_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/target_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/target_1.12.0.tgz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 49 Package: TargetDecoy Version: 1.4.0 Depends: R (>= 4.1) Imports: ggplot2, ggpubr, methods, miniUI, mzID, mzR, shiny, stats Suggests: BiocStyle, knitr, msdata, sessioninfo, rmarkdown, gridExtra, testthat (>= 3.0.0), covr License: Artistic-2.0 MD5sum: 72e36c275e0e07c3378964a7a4c0fcef NeedsCompilation: no Title: Diagnostic Plots to Evaluate the Target Decoy Approach Description: A first step in the data analysis of Mass Spectrometry (MS) based proteomics data is to identify peptides and proteins. With this respect the huge number of experimental mass spectra typically have to be assigned to theoretical peptides derived from a sequence database. Search engines are used for this purpose. These tools compare each of the observed spectra to all candidate theoretical spectra derived from the sequence data base and calculate a score for each comparison. The observed spectrum is then assigned to the theoretical peptide with the best score, which is also referred to as the peptide to spectrum match (PSM). It is of course crucial for the downstream analysis to evaluate the quality of these matches. Therefore False Discovery Rate (FDR) control is used to return a reliable list PSMs. The FDR, however, requires a good characterisation of the score distribution of PSMs that are matched to the wrong peptide (bad target hits). In proteomics, the target decoy approach (TDA) is typically used for this purpose. The TDA method matches the spectra to a database of real (targets) and nonsense peptides (decoys). A popular approach to generate these decoys is to reverse the target database. Hence, all the PSMs that match to a decoy are known to be bad hits and the distribution of their scores are used to estimate the distribution of the bad scoring target PSMs. A crucial assumption of the TDA is that the decoy PSM hits have similar properties as bad target hits so that the decoy PSM scores are a good simulation of the target PSM scores. Users, however, typically do not evaluate these assumptions. To this end we developed TargetDecoy to generate diagnostic plots to evaluate the quality of the target decoy method. biocViews: MassSpectrometry, Proteomics, QualityControl, Software, Visualization Author: Elke Debrie [aut, cre], Lieven Clement [aut] (), Milan Malfait [aut] () Maintainer: Elke Debrie URL: https://www.bioconductor.org/packages/TargetDecoy, https://statomics.github.io/TargetDecoy/, https://github.com/statOmics/TargetDecoy/ VignetteBuilder: knitr BugReports: https://github.com/statOmics/TargetDecoy/issues git_url: https://git.bioconductor.org/packages/TargetDecoy git_branch: RELEASE_3_16 git_last_commit: 7771d73 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TargetDecoy_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TargetDecoy_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TargetDecoy_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TargetDecoy_1.4.0.tgz vignettes: vignettes/TargetDecoy/inst/doc/TargetDecoy.html vignetteTitles: Introduction to TargetDecoy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetDecoy/inst/doc/TargetDecoy.R dependencyCount: 122 Package: TargetScore Version: 1.36.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: e2a10f9f4a48039ad6a819f05975a004 NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: RELEASE_3_16 git_last_commit: 73487c8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TargetScore_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TargetScore_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TargetScore_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TargetScore_1.36.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R suggestsMe: TargetScoreData dependencyCount: 9 Package: TargetSearch Version: 2.0.0 Imports: graphics, grDevices, methods, ncdf4, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) MD5sum: 8df8c07d6bac6b02c0870c5f71f4756a NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a flexible, fast and accurate method for targeted pre-processing of GC-MS data. The user provides a (often very large) set of GC chromatograms and a metabolite library of targets. The package will automatically search those targets in the chromatograms resulting in a data matrix that can be used for further data analysis. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza [aut, cre], Jan Lisec [aut], Henning Redestig [aut], Matt Hannah [aut] Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: RELEASE_3_16 git_last_commit: 2fbc9bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TargetSearch_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TargetSearch_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TargetSearch_2.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TargetSearch_2.0.0.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TarSeqQC Version: 1.28.0 Depends: R (>= 3.5.1), methods, GenomicRanges, Rsamtools (>= 1.9.2), ggplot2, plyr, openxlsx Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot, graphics, GenomicAlignments, Hmisc Suggests: BiocManager, RUnit License: GPL (>=2) Archs: x64 MD5sum: 6e1741858778147023bc2d1abc837c5c NeedsCompilation: no Title: TARgeted SEQuencing Experiment Quality Control Description: The package allows the representation of targeted experiment in R. This is based on current packages and incorporates functions to do a quality control over this kind of experiments and a fast exploration of the sequenced regions. An xlsx file is generated as output. biocViews: Software, Sequencing, TargetedResequencing, QualityControl, Visualization, Coverage, Alignment, DataImport Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S. Llera and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar git_url: https://git.bioconductor.org/packages/TarSeqQC git_branch: RELEASE_3_16 git_last_commit: ef4f4fc git_last_commit_date: 2022-11-01 Date/Publication: 2023-03-20 source.ver: src/contrib/TarSeqQC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TarSeqQC_1.27.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TarSeqQC_1.27.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TarSeqQC_1.28.0.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R dependencyCount: 111 Package: TBSignatureProfiler Version: 1.10.0 Depends: R (>= 4.2.0) Imports: ASSIGN (>= 1.23.1), BiocGenerics, BiocParallel, ComplexHeatmap, DESeq2, DT, edgeR, gdata, ggplot2, GSVA, magrittr, methods, RColorBrewer, reshape2, rlang, ROCit, S4Vectors, singscore, stats, SummarizedExperiment Suggests: BiocStyle, caret, circlize, class, covr, dplyr, e1071, glmnet, HGNChelper, impute, knitr, lintr, MASS, plyr, pROC, randomForest, rmarkdown, shiny, spelling, sva, testthat License: MIT + file LICENSE MD5sum: 4a82cba1b4a800c2cb1b547353dec36d NeedsCompilation: no Title: Profile RNA-Seq Data Using TB Pathway Signatures Description: Gene signatures of TB progression, TB disease, and other TB disease states have been validated and published previously. This package aggregates known signatures and provides computational tools to enlist their usage on other datasets. The TBSignatureProfiler makes it easy to profile RNA-Seq data using these signatures and includes common signature profiling tools including ASSIGN, GSVA, and ssGSEA. Original models for some gene signatures are also available. A shiny app provides some functionality alongside for detailed command line accessibility. biocViews: GeneExpression, DifferentialExpression Author: Aubrey R. Odom-Mabey [aut, cre, dtm] (), David Jenkins [aut, org] (), Xutao Wang [aut], Yue Zhao [ctb] (), Christian Love [ctb], W. Evan Johnson [aut] Maintainer: Aubrey R. Odom-Mabey URL: https://github.com/compbiomed/TBSignatureProfiler https://compbiomed.github.io/TBSignatureProfiler-docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/TBSignatureProfiler/issues git_url: https://git.bioconductor.org/packages/TBSignatureProfiler git_branch: RELEASE_3_16 git_last_commit: d5d3e43 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TBSignatureProfiler_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TBSignatureProfiler_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TBSignatureProfiler_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TBSignatureProfiler_1.10.0.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R dependencyCount: 170 Package: TCC Version: 1.38.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: 12a23d4a631d9b030171a6ada2a281aa NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: RELEASE_3_16 git_last_commit: 81d0810 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TCC_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCC_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TCC_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TCC_1.38.0.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR, ExpHunterSuite dependencyCount: 101 Package: TCGAbiolinks Version: 2.25.3 Depends: R (>= 4.0) Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics, tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite (>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0), IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data (>= 1.15.1), readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame, AnnotationHub, ExperimentHub, pathview, clusterProfiler, Seurat, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, supraHex, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid License: GPL (>= 3) MD5sum: 5afde8cc1dc8402969dbb0a8b91b95f1 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Tiago Chedraoui Silva , Antonio Colaprico URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: master git_last_commit: 3d99f1c7 git_last_commit_date: 2022-09-19 Date/Publication: 2022-09-20 source.ver: src/contrib/TCGAbiolinks_2.25.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCGAbiolinks_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TCGAbiolinks_2.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TCGAbiolinks_2.25.3.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/gui.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/stemness_score.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "9. Graphical User Interface (GUI)", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 11. Stemness score, 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/gui.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/stemness_score.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R importsMe: ELMER, MoonlightR, musicatk, TCGAbiolinksGUI, SingscoreAMLMutations dependencyCount: 111 Package: TCGAbiolinksGUI Version: 1.23.0 Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly, readr, maftools, stringr (>= 1.1.0), SummarizedExperiment, ggrepel, data.table, caret, shinyFiles (>= 0.6.2), ggplot2 (>= 2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel, TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker, sesame, shinyBS (>= 0.61) Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2, BiocStyle, animation, rmarkdown, pander License: GPL (>= 3) MD5sum: 7f2d0ed9452b1738afbdd71e4d688e0a NeedsCompilation: no Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data" Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data. A demo version of GUI is found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/" biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Sequencing, Pathways, Network, DNASeq Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Michele Ceccarelli, Gianluca Bontempi , Benjamin P. Berman , Houtan Noushmehr Maintainer: Tiago C. Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI git_branch: master git_last_commit: 5f99383 git_last_commit_date: 2022-04-26 Date/Publication: 2022-05-01 source.ver: src/contrib/TCGAbiolinksGUI_1.23.0.tar.gz vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html, vignettes/TCGAbiolinksGUI/inst/doc/Cases.html, vignettes/TCGAbiolinksGUI/inst/doc/data.html, vignettes/TCGAbiolinksGUI/inst/doc/index.html, vignettes/TCGAbiolinksGUI/inst/doc/integrative.html vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu", "1. Introduction", "4. Integrative analysis menu" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R, vignettes/TCGAbiolinksGUI/inst/doc/index.R dependencyCount: 306 Package: TCGAutils Version: 1.18.0 Depends: R (>= 4.2.0) Imports: AnnotationDbi, BiocGenerics, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: AnnotationHub, BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, httr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, mirbase.db, org.Hs.eg.db, RColorBrewer, readr, rmarkdown, RTCGAToolbox (>= 2.17.4), rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 25c4dc4801b06776e510643c1376d7da NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. Exported functions include those that import data from flat files into Bioconductor objects, convert row annotations, and identifier translation via the GDC API. biocViews: Software, WorkflowStep, Preprocessing, DataImport Author: Marcel Ramos [aut, cre] (), Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: RELEASE_3_16 git_last_commit: 04f65bf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TCGAutils_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCGAutils_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TCGAutils_1.18.0.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R importsMe: cBioPortalData, RTCGAToolbox, terraTCGAdata suggestsMe: CNVRanger, dce, glmSparseNet, curatedTCGAData dependencyCount: 106 Package: TCseq Version: 1.22.6 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: 9c30b9dbc0b1bf244bd0b5d3444a7a50 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: RELEASE_3_16 git_last_commit: 768d487 git_last_commit_date: 2023-01-30 Date/Publication: 2023-01-30 source.ver: src/contrib/TCseq_1.22.6.tar.gz win.binary.ver: bin/windows/contrib/4.2/TCseq_1.22.6.zip mac.binary.ver: bin/macosx/contrib/4.2/TCseq_1.22.6.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TCseq_1.22.6.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R dependencyCount: 79 Package: TDARACNE Version: 1.47.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 81de074a2c2d3b99621259e550c4b48b NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro git_url: https://git.bioconductor.org/packages/TDARACNE git_branch: master git_last_commit: 785c8df git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TDARACNE_1.47.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TDARACNE_1.47.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TDARACNE_1.47.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R dependencyCount: 12 Package: TEKRABber Version: 1.2.0 Depends: R (>= 4.1) Imports: apeglm, biomaRt, DESeq2, Rcpp (>= 1.0.7), SCBN, SummarizedExperiment, stats, utils LinkingTo: Rcpp Suggests: BiocStyle, ggpubr, rmarkdown, shiny, knitr, testthat (>= 3.0.0) License: GPL (>= 2) MD5sum: 16a4253a47f26d2ac6b4fcdf21d4f1fb NeedsCompilation: yes Title: An R package estimates the correlations of orthologs and transposable elements between two species Description: TEKRABber is made to provide a user-friendly pipeline for comparing orthologs and transposable elements (TEs) between two species. It considers the orthology confidence between two species from BioMart to normalize expression counts and detect differentially expressed orthologs/TEs. Then it provides one to one correlation analysis for desired orthologs and TEs. There is also an app function to have a first insight on the result. Users can prepare orthologs/TEs RNA-seq expression data by their own preference to run TEKRABber following the data structure mentioned in the vignettes. biocViews: DifferentialExpression, Normalization, Transcription, GeneExpression Author: Yao-Chung Chen [aut, cre] (), Katja Nowick [aut] () Maintainer: Yao-Chung Chen URL: https://github.com/ferygood/TEKRABber VignetteBuilder: knitr BugReports: https://github.com/ferygood/TEKRABber/issues git_url: https://git.bioconductor.org/packages/TEKRABber git_branch: RELEASE_3_16 git_last_commit: ff8c3a9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TEKRABber_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TEKRABber_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TEKRABber_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TEKRABber_1.2.0.tgz vignettes: vignettes/TEKRABber/inst/doc/TEKRABber.html vignetteTitles: TEKRABber hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEKRABber/inst/doc/TEKRABber.R dependencyCount: 119 Package: TENxIO Version: 1.0.1 Depends: R (>= 4.2.0), SingleCellExperiment, SummarizedExperiment Imports: BiocBaseUtils, BiocGenerics, BiocIO, GenomeInfoDb, GenomicRanges, Matrix, MatrixGenerics, methods, RCurl, readr, R.utils, S4Vectors, utils Suggests: BiocStyle, DropletTestFiles, ExperimentHub, HDF5Array, knitr, RaggedExperiment, rhdf5, rmarkdown, Rsamtools, tinytest License: Artistic-2.0 Archs: x64 MD5sum: 42126bb9a4ca896a9dfc9e19511e9f44 NeedsCompilation: no Title: Import methods for 10X Genomics files Description: Provides a structured S4 approach to importing data files from the 10X pipelines. It mainly supports Single Cell Multiome ATAC + Gene Expression data among other data types. The main Bioconductor data representations used are SingleCellExperiment and RaggedExperiment. biocViews: Software, Infrastructure, DataImport, SingleCell Author: Marcel Ramos [aut, cre] () Maintainer: Marcel Ramos URL: https://github.com/waldronlab/TENxIO VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TENxIO/issues git_url: https://git.bioconductor.org/packages/TENxIO git_branch: RELEASE_3_16 git_last_commit: b5238b9 git_last_commit_date: 2023-02-10 Date/Publication: 2023-02-10 source.ver: src/contrib/TENxIO_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/TENxIO_1.0.1.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TENxIO_1.0.1.tgz vignettes: vignettes/TENxIO/inst/doc/TENxIO.html vignetteTitles: TENxIO Quick Start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TENxIO/inst/doc/TENxIO.R dependencyCount: 56 Package: tenXplore Version: 1.20.0 Depends: R (>= 4.0), shiny, restfulSE (>= 0.99.12) Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils Suggests: org.Hs.eg.db, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 4e1563821ba68558aea2382b37a23ad2 NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: RELEASE_3_16 git_last_commit: 32ae3e4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tenXplore_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tenXplore_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tenXplore_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tenXplore_1.20.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 126 Package: TEQC Version: 4.20.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: b95b2e99070291633668d7502e461678 NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Sarah Bonnin git_url: https://git.bioconductor.org/packages/TEQC git_branch: RELEASE_3_16 git_last_commit: 9ef9c21 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TEQC_4.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TEQC_4.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TEQC_4.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TEQC_4.20.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 33 Package: ternarynet Version: 1.42.0 Depends: R (>= 4.0) Imports: utils, igraph, methods, graphics, stats, BiocParallel Suggests: testthat Enhances: Rmpi, snow License: GPL (>= 2) MD5sum: 6f78dc913b97261c00e84e7f98a75978 NeedsCompilation: yes Title: Ternary Network Estimation Description: Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI. biocViews: Software, CellBiology, GraphAndNetwork, Network, Bayesian Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: McCall N. Matthew git_url: https://git.bioconductor.org/packages/ternarynet git_branch: RELEASE_3_16 git_last_commit: f8b1db5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ternarynet_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ternarynet_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ternarynet_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ternarynet_1.42.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 22 Package: terraTCGAdata Version: 1.2.0 Depends: R (>= 4.2.0), AnVIL, MultiAssayExperiment Imports: BiocFileCache, dplyr, GenomicRanges, methods, RaggedExperiment, readr, S4Vectors, stats, tidyr, TCGAutils, utils Suggests: knitr, rmarkdown, BiocStyle, withr, testthat (>= 3.0.0) License: Artistic-2.0 Archs: x64 MD5sum: 3221a03fdf1396b4e2a49c65e6f87416 NeedsCompilation: no Title: OpenAccess TCGA Data on Terra as MultiAssayExperiment Description: Leverage the existing open access TCGA data on Terra with well-established Bioconductor infrastructure. Make use of the Terra data model without learning its complexities. With a few functions, you can copy / download and generate a MultiAssayExperiment from the TCGA example workspaces provided by Terra. biocViews: Software, Infrastructure, DataImport Author: Marcel Ramos [aut, cre] () Maintainer: Marcel Ramos URL: https://github.com/waldronlab/terraTCGAdata VignetteBuilder: knitr BugReports: https://github.com/waldronlab/terraTCGAdata/issues git_url: https://git.bioconductor.org/packages/terraTCGAdata git_branch: RELEASE_3_16 git_last_commit: e8fbfed git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/terraTCGAdata_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/terraTCGAdata_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/terraTCGAdata_1.2.0.tgz vignettes: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.html vignetteTitles: Obtain Terra TCGA data as MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/terraTCGAdata/inst/doc/terraTCGAdata.R dependencyCount: 137 Package: TFARM Version: 1.20.0 Depends: R (>= 3.5.0) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 9e2b009df5b44b3eb068c6d6397b670a NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: RELEASE_3_16 git_last_commit: 365306e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TFARM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFARM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFARM_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TFARM_1.20.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 61 Package: TFBSTools Version: 1.36.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>= 1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0), rmarkdown License: GPL-2 MD5sum: f908ba9781df2d61148fc5917705f7db NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: RELEASE_3_16 git_last_commit: 3358c89 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TFBSTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFBSTools_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFBSTools_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TFBSTools_1.36.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: ATACCoGAPS, ATACseqTFEA, chromVAR, enrichTF, esATAC, MatrixRider, monaLisa, motifmatchr, motifStack, primirTSS, spatzie suggestsMe: enhancerHomologSearch, GRaNIE, MAGAR, MethReg, pageRank, universalmotif, JASPAR2018, JASPAR2020, JASPAR2022, CAGEWorkflow, Signac dependencyCount: 120 Package: TFEA.ChIP Version: 1.18.0 Depends: R (>= 3.5) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 Archs: x64 MD5sum: 9aa80290e35d064a55ea90ec42cd38a1 NeedsCompilation: no Title: Analyze Transcription Factor Enrichment Description: Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology Author: Laura Puente Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: RELEASE_3_16 git_last_commit: b8eab49 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TFEA.ChIP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFEA.ChIP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFEA.ChIP_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TFEA.ChIP_1.18.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R dependencyCount: 100 Package: TFHAZ Version: 1.20.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods, ORFik Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 81fe2f7eeb0d80381bd27accfe1ee059 NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Silvia Cascianelli, Marco Masseroli Maintainer: Gaia Ceddia VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_16 git_last_commit: 0259ec3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TFHAZ_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFHAZ_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFHAZ_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TFHAZ_1.20.0.tgz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R dependencyCount: 137 Package: TFutils Version: 1.18.0 Depends: R (>= 4.1.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl, AnnotationDbi, org.Hs.eg.db, utils Suggests: knitr, data.table, testthat, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, S4Vectors, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomicFiles, GenomeInfoDb, SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer, rmarkdown License: Artistic-2.0 MD5sum: 779b08559c6b039c5b422f9cf36a76c6 NeedsCompilation: no Title: TFutils Description: This package helps users to work with TF metadata from various sources. Significant catalogs of TFs and classifications thereof are made available. Tools for working with motif scans are also provided. biocViews: Transcriptomics Author: Vincent Carey [aut, cre], Shweta Gopaulakrishnan [aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: RELEASE_3_16 git_last_commit: fa5538c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TFutils_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TFutils_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TFutils_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TFutils_1.18.0.tgz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R dependencyCount: 116 Package: tidybulk Version: 1.10.1 Depends: R (>= 4.1.0) Imports: tibble, readr, dplyr, magrittr, tidyr, stringi, stringr, rlang, purrr, tidyselect, preprocessCore, stats, parallel, utils, lifecycle, scales, SummarizedExperiment, GenomicRanges, methods, S4Vectors, crayon Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager, Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva, GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, ggplot2, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional, survminer, tidySummarizedExperiment, markdown, uwot, matrixStats, igraph, EGSEA License: GPL-3 MD5sum: 693e44897fc005741ff72c1f511279dd NeedsCompilation: no Title: Brings transcriptomics to the tidyverse Description: This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidybulk VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidybulk/issues git_url: https://git.bioconductor.org/packages/tidybulk git_branch: RELEASE_3_16 git_last_commit: a2fae5d git_last_commit_date: 2023-03-14 Date/Publication: 2023-03-15 source.ver: src/contrib/tidybulk_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tidybulk_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tidybulk_1.10.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tidybulk_1.10.1.tgz vignettes: vignettes/tidybulk/inst/doc/comparison_with_base_R.html, vignettes/tidybulk/inst/doc/introduction.html, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.html, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.html vignetteTitles: Comparison with base R, Overview of the tidybulk package, Manuscript code - differential feature abundance, Manuscript code - transcriptional signature identification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/comparison_with_base_R.R, vignettes/tidybulk/inst/doc/introduction.R, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.R, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.R dependencyCount: 65 Package: tidySingleCellExperiment Version: 1.8.2 Depends: R (>= 4.0.0), ttservice, SingleCellExperiment Imports: SummarizedExperiment, dplyr, tibble, tidyr, ggplot2, plotly, magrittr, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, Matrix, uwot, celldex, dittoSeq, EnsDb.Hsapiens.v86 License: GPL-3 MD5sum: 72dd21b78d01687f6a9f8e2c95683b97 NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: tidySingleCellExperiment is an adapter that abstracts the 'SingleCellExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySingleCellExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySingleCellExperiment/issues git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment git_branch: RELEASE_3_16 git_last_commit: 010c041 git_last_commit_date: 2023-04-03 Date/Publication: 2023-04-05 source.ver: src/contrib/tidySingleCellExperiment_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/tidySingleCellExperiment_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.2/tidySingleCellExperiment_1.8.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tidySingleCellExperiment_1.8.0.tgz vignettes: vignettes/tidySingleCellExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySingleCellExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R suggestsMe: sccomp dependencyCount: 97 Package: tidySummarizedExperiment Version: 1.8.1 Depends: R (>= 4.0.0), SummarizedExperiment Imports: tibble (>= 3.0.4), dplyr, magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, plotly, utils, S4Vectors, tidyselect, ellipsis, vctrs, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown License: GPL-3 MD5sum: 7568eace449e2978cea306944440b5b3 NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: tidySummarizedExperiment is an adapter that abstracts the 'SummarizedExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola URL: https://github.com/stemangiola/tidySummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/stemangiola/tidySummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: RELEASE_3_16 git_last_commit: fe4668c git_last_commit_date: 2023-03-14 Date/Publication: 2023-03-15 source.ver: src/contrib/tidySummarizedExperiment_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tidySummarizedExperiment_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tidySummarizedExperiment_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tidySummarizedExperiment_1.8.1.tgz vignettes: vignettes/tidySummarizedExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySummarizedExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R suggestsMe: tidybulk dependencyCount: 95 Package: tigre Version: 1.52.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 MD5sum: 4cf1e1fcc9a8ffe4df11fd396d377ccc NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: RELEASE_3_16 git_last_commit: cca6602 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tigre_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tigre_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tigre_1.52.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tigre_1.52.0.tgz vignettes: vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre.R dependencyCount: 53 Package: TileDBArray Version: 1.8.0 Depends: DelayedArray (>= 0.15.16) Imports: methods, Rcpp, tiledb, S4Vectors LinkingTo: Rcpp Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE Archs: x64 MD5sum: ce512bce0bcab97cb7dfa89cc6c698d3 NeedsCompilation: yes Title: Using TileDB as a DelayedArray Backend Description: Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances. biocViews: DataRepresentation, Infrastructure, Software Author: Aaron Lun [aut, cre], Genentech, Inc. [cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/TileDBArray VignetteBuilder: knitr BugReports: https://github.com/LTLA/TileDBArray git_url: https://git.bioconductor.org/packages/TileDBArray git_branch: RELEASE_3_16 git_last_commit: 8189c8c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TileDBArray_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TileDBArray_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TileDBArray_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TileDBArray_1.8.0.tgz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R dependencyCount: 25 Package: tilingArray Version: 1.76.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: x64 MD5sum: db497c7a02fc20e9e761363e8d355127 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: RELEASE_3_16 git_last_commit: ca84b31 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tilingArray_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tilingArray_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tilingArray_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tilingArray_1.76.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R dependsOnMe: davidTiling importsMe: ADaCGH2, snapCGH dependencyCount: 84 Package: timecourse Version: 1.70.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL Archs: x64 MD5sum: ef063c2d4a6c4c361c15e067b1002853 NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: RELEASE_3_16 git_last_commit: 2071cd3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/timecourse_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/timecourse_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/timecourse_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/timecourse_1.70.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 10 Package: timeOmics Version: 1.10.0 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, propr, lmtest, plyr Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 MD5sum: 967874e6cc33f8ad51ec8c5f879bf51f NeedsCompilation: no Title: Time-Course Multi-Omics data integration Description: timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step. biocViews: Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software, Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics, Classification, Regression, ImmunoOncology, GenePrediction, MultipleComparison Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut] Maintainer: Antoine Bodein VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: RELEASE_3_16 git_last_commit: 6f1a0b1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/timeOmics_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/timeOmics_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/timeOmics_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/timeOmics_1.9.1.tgz vignettes: vignettes/timeOmics/inst/doc/vignette.html vignetteTitles: timeOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timeOmics/inst/doc/vignette.R suggestsMe: netOmics dependencyCount: 69 Package: timescape Version: 1.22.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 26eb09368fda9c2d6e31e94f96379fb7 NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: RELEASE_3_16 git_last_commit: a5861cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/timescape_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/timescape_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/timescape_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/timescape_1.22.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 48 Package: TimiRGeN Version: 1.8.0 Depends: R (>= 4.1), Mfuzz, MultiAssayExperiment Imports: biomaRt, clusterProfiler, dplyr (>= 0.8.4), FreqProf, gtools (>= 3.8.1), gplots, ggdendro, gghighlight, ggplot2, graphics, grDevices, igraph (>= 1.2.4.2), RCy3, readxl, reshape2, rWikiPathways, scales, stats, tidyr (>= 1.0.2), stringr (>= 1.4.0) Suggests: BiocManager, kableExtra, knitr (>= 1.27), org.Hs.eg.db, org.Mm.eg.db, testthat, rmarkdown License: GPL-3 MD5sum: 9eb549b65cb9047c272a6c9229404fe8 NeedsCompilation: no Title: Time sensitive microRNA-mRNA integration, analysis and network generation tool Description: TimiRGeN (Time Incorporated miR-mRNA Generation of Networks) is a novel R package which functionally analyses and integrates time course miRNA-mRNA differential expression data. This tool can generate small networks within R or export results into cytoscape or pathvisio for more detailed network construction and hypothesis generation. This tool is created for researchers that wish to dive deep into time series multi-omic datasets. TimiRGeN goes further than many other tools in terms of data reduction. Here, potentially hundreds-of-thousands of potential miRNA-mRNA interactions can be whittled down into a handful of high confidence miRNA-mRNA interactions affecting a signalling pathway, across a time course. biocViews: Clustering, miRNA, Network, Pathways, Software, TimeCourse, Visualization Author: Krutik Patel [aut, cre] Maintainer: Krutik Patel URL: https://github.com/Krutik6/TimiRGeN/ VignetteBuilder: knitr BugReports: https://github.com/Krutik6/TimiRGeN/issues git_url: https://git.bioconductor.org/packages/TimiRGeN git_branch: RELEASE_3_16 git_last_commit: 6150789 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TimiRGeN_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TimiRGeN_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TimiRGeN_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TimiRGeN_1.8.0.tgz vignettes: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.html vignetteTitles: TimiRGeN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.R dependencyCount: 193 Package: TIN Version: 1.30.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d861f54c0fbb411b291242e97c22dfe0 NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: RELEASE_3_16 git_last_commit: 2b436b2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TIN_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TIN_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TIN_1.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TIN_1.30.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 136 Package: TissueEnrich Version: 1.18.0 Depends: R (>= 3.5), ensurer (>= 1.1.0), ggplot2 (>= 2.2.1), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 66d1517d81b3a0820ba5f31b7de8897a NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_16 git_last_commit: 7ed874b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TissueEnrich_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TissueEnrich_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TissueEnrich_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TissueEnrich_1.18.0.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 87 Package: TitanCNA Version: 1.36.0 Depends: R (>= 3.5.1) Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 MD5sum: 0cd1ed23092554bf772dabd6ecf7f789 NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation, ImmunoOncology Author: Gavin Ha Maintainer: Gavin Ha URL: https://github.com/gavinha/TitanCNA git_url: https://git.bioconductor.org/packages/TitanCNA git_branch: RELEASE_3_16 git_last_commit: 22598a0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TitanCNA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TitanCNA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TitanCNA_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TitanCNA_1.36.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 101 Package: tkWidgets Version: 1.76.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 Archs: x64 MD5sum: c0beebd9f2d5e41045c916781192bb39 NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: RELEASE_3_16 git_last_commit: f5b9781 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tkWidgets_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tkWidgets_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tkWidgets_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tkWidgets_1.76.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: tLOH Version: 1.6.0 Depends: R (>= 4.2) Imports: scales, stats, utils, ggplot2, data.table, purrr, dplyr, VariantAnnotation, GenomicRanges, MatrixGenerics, bestNormalize, depmixS4, naniar, stringr Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 10d4ea1a71e6973a284038f909611192 NeedsCompilation: no Title: Assessment of evidence for LOH in spatial transcriptomics pre-processed data using Bayes factor calculations Description: tLOH, or transcriptomicsLOH, assesses evidence for loss of heterozygosity (LOH) in pre-processed spatial transcriptomics data. This tool requires spatial transcriptomics cluster and allele count information at likely heterozygous single-nucleotide polymorphism (SNP) positions in VCF format. Bayes factors are calculated at each SNP to determine likelihood of potential loss of heterozygosity event. Two plotting functions are included to visualize allele fraction and aggregated Bayes factor per chromosome. Data generated with the 10X Genomics Visium Spatial Gene Expression platform must be pre-processed to obtain an individual sample VCF with columns for each cluster. Required fields are allele depth (AD) with counts for reference/alternative alleles and read depth (DP). biocViews: CopyNumberVariation, Transcription, SNP, GeneExpression, Transcriptomics Author: Michelle Webb [cre, aut], David Craig [aut] Maintainer: Michelle Webb URL: https://github.com/USCDTG/tLOH VignetteBuilder: knitr BugReports: https://github.com/USCDTG/tLOH/issues git_url: https://git.bioconductor.org/packages/tLOH git_branch: RELEASE_3_16 git_last_commit: cb1b340 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tLOH_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tLOH_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tLOH_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tLOH_1.6.0.tgz vignettes: vignettes/tLOH/inst/doc/tLOH_vignette.html vignetteTitles: tLOH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tLOH/inst/doc/tLOH_vignette.R dependencyCount: 168 Package: TMixClust Version: 1.20.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: 76cb81a8acc87a1b7dd5ee534a309ac6 NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: RELEASE_3_16 git_last_commit: df27f53 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TMixClust_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TMixClust_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TMixClust_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TMixClust_1.20.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 31 Package: TNBC.CMS Version: 1.14.0 Depends: R (>= 3.6.0), e1071, quadprog, SummarizedExperiment Imports: GSVA (>= 1.26.0), pheatmap, grDevices, RColorBrewer, pracma, GGally, R.utils, forestplot, ggplot2, ggpubr, survival, grid, stats, methods Suggests: knitr License: GPL-3 MD5sum: 314b1384a31ac1ccc1d5c9e5b8c4a74e NeedsCompilation: no Title: TNBC.CMS: Prediction of TNBC Consensus Molecular Subtypes Description: This package implements a machine learning-based classifier for the assignment of consensus molecular subtypes to TNBC samples. It also provides functions to summarize genomic and clinical characteristics. biocViews: Classification, Clustering, GeneExpression, GenePrediction, SupportVectorMachine Author: Doyeong Yu, Jihyun Kim, In Hae Park, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TNBC.CMS git_branch: RELEASE_3_16 git_last_commit: 8425ff0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TNBC.CMS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TNBC.CMS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TNBC.CMS_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TNBC.CMS_1.14.0.tgz vignettes: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.pdf vignetteTitles: TNBC.CMS.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.R dependencyCount: 169 Package: TnT Version: 1.20.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 MD5sum: a658e628d0cd011c9790b5e4a863edd2 NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: RELEASE_3_16 git_last_commit: 03fb02d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TnT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TnT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TnT_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TnT_1.20.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 51 Package: TOAST Version: 1.12.0 Depends: R (>= 3.6), EpiDISH, limma, nnls, quadprog Imports: stats, methods, SummarizedExperiment, corpcor, doParallel, parallel, ggplot2, tidyr, GGally Suggests: BiocStyle, knitr, rmarkdown, gplots, matrixStats, Matrix License: GPL-2 MD5sum: 19d4a9cfaee4758a218b62b3f39710a3 NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. tree-based differential analysis 3. improve variable selection in reference-free deconvolution 4. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Weiwei Zhang and Luxiao Chen and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: RELEASE_3_16 git_last_commit: 15ad879 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TOAST_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TOAST_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TOAST_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TOAST_1.12.0.tgz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R dependencyCount: 83 Package: tomoda Version: 1.8.0 Depends: R (>= 4.0.0) Imports: methods, stats, grDevices, reshape2, Rtsne, umap, RColorBrewer, ggplot2, ggrepel, SummarizedExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: 52a7481bd26fa5e0240d7bf6fff8236b NeedsCompilation: no Title: Tomo-seq data analysis Description: This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Clustering, Visualization Author: Wendao Liu [aut, cre] () Maintainer: Wendao Liu URL: https://github.com/liuwd15/tomoda VignetteBuilder: knitr BugReports: https://github.com/liuwd15/tomoda/issues git_url: https://git.bioconductor.org/packages/tomoda git_branch: RELEASE_3_16 git_last_commit: 73377e4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tomoda_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tomoda_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tomoda_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tomoda_1.8.0.tgz vignettes: vignettes/tomoda/inst/doc/tomoda.html vignetteTitles: tomoda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoda/inst/doc/tomoda.R dependencyCount: 72 Package: tomoseqr Version: 1.2.0 Depends: R (>= 4.2) Imports: grDevices, graphics, animation, tibble, dplyr, stringr, purrr, methods, shiny, BiocFileCache, readr, tools, plotly, ggplot2 Suggests: rmarkdown, knitr, BiocStyle, testthat (>= 3.0.0) License: MIT + file LICENSE MD5sum: 9b339c66c67d881e3de6c4706ff499eb NeedsCompilation: no Title: R Package for Analyzing Tomo-seq Data Description: `tomoseqr` is an R package for analyzing Tomo-seq data. Tomo-seq is a genome-wide RNA tomography method that combines combining high-throughput RNA sequencing with cryosectioning for spatially resolved transcriptomics. `tomoseqr` reconstructs 3D expression patterns from tomo-seq data and visualizes the reconstructed 3D expression patterns. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Spatial, Visualization, Software Author: Ryosuke Matsuzawa [aut, cre] () Maintainer: Ryosuke Matsuzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tomoseqr git_branch: RELEASE_3_16 git_last_commit: fd6e346 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tomoseqr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tomoseqr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tomoseqr_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tomoseqr_1.2.0.tgz vignettes: vignettes/tomoseqr/inst/doc/tomoseqr.html vignetteTitles: tomoseqr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoseqr/inst/doc/tomoseqr.R dependencyCount: 103 Package: topconfects Version: 1.14.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2 Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr, rmarkdown, BiocStyle License: LGPL-2.1 | file LICENSE MD5sum: 0e5c2485aff039d6f5d39dc4d791d06b NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: RELEASE_3_16 git_last_commit: f45128c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/topconfects_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/topconfects_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/topconfects_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/topconfects_1.14.0.tgz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: MetaVolcanoR, weitrix dependencyCount: 37 Package: topdownr Version: 1.20.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.4-2), MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.27.5) Suggests: topdownrdata (>= 0.2), knitr, rmarkdown, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: a118dd4c6d72d5c7be989e1a7e25bee4 NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (), Pavel Shliaha [aut] (), Ole Nørregaard Jensen [aut] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/topdownr/issues/ git_url: https://git.bioconductor.org/packages/topdownr git_branch: RELEASE_3_16 git_last_commit: 88fd9d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/topdownr_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/topdownr_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/topdownr_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/topdownr_1.20.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R dependsOnMe: topdownrdata dependencyCount: 84 Package: topGO Version: 2.50.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: f9797ef2a4716a4e6f7d2f2c0acbfb69 NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa git_url: https://git.bioconductor.org/packages/topGO git_branch: RELEASE_3_16 git_last_commit: befbff4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/topGO_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/topGO_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.2/topGO_2.50.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/topGO_2.50.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: BgeeDB, cellTree, compEpiTools, EGSEA, ideal, moanin, tRanslatome, ccTutorial importsMe: APL, BioMM, cellity, consICA, FoldGO, GOSim, GRaNIE, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO, ExpHunterSuite suggestsMe: FGNet, geva, IntramiRExploreR, miRNAtap, pareg, Ringo dependencyCount: 52 Package: ToxicoGx Version: 2.2.0 Depends: R (>= 4.1), CoreGx Imports: SummarizedExperiment, BiocGenerics, S4Vectors, Biobase, BiocParallel, ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods, reshape2, tidyr, data.table, assertthat, scales, graphics, grDevices, parallel, stats, utils, limma, jsonlite Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools, PharmacoGx, xtable, markdown License: MIT + file LICENSE MD5sum: c374971908c993adba7e5d9aca528181 NeedsCompilation: no Title: Analysis of Large-Scale Toxico-Genomic Data Description: Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: RELEASE_3_16 git_last_commit: cda9788 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ToxicoGx_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ToxicoGx_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ToxicoGx_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ToxicoGx_2.2.0.tgz vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R dependencyCount: 137 Package: TPP Version: 3.26.1 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines, stats, stringr, tibble, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 14eb8e2ff969efaabde4564d58faaafb NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: RELEASE_3_16 git_last_commit: d24d99a git_last_commit_date: 2023-02-09 Date/Publication: 2023-02-10 source.ver: src/contrib/TPP_3.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/TPP_3.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/TPP_3.26.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TPP_3.26.1.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R suggestsMe: Rtpca dependencyCount: 96 Package: TPP2D Version: 1.14.0 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS, BiocParallel, limma Suggests: knitr, testthat, rmarkdown License: GPL-3 MD5sum: fbb04dc6285a3032070945a15b08ddb8 NeedsCompilation: no Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP) Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: RELEASE_3_16 git_last_commit: ba3a310 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TPP2D_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TPP2D_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TPP2D_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TPP2D_1.14.0.tgz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 62 Package: tracktables Version: 1.32.0 Depends: R (>= 3.5.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 3f955c8f91ed30c263e3c8667247d20c NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: RELEASE_3_16 git_last_commit: 0447a8a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tracktables_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tracktables_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tracktables_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tracktables_1.32.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 47 Package: trackViewer Version: 1.34.0 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid, Rcpp Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, Rgraphviz, InteractionSet, graph, utils, rhdf5 LinkingTo: Rcpp Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools, rmarkdown, motifStack License: GPL (>= 2) MD5sum: 550412052e73e2bff301e426fd352584 NeedsCompilation: yes Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou [aut, cre] (), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: RELEASE_3_16 git_last_commit: 9daa482 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/trackViewer_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/trackViewer_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/trackViewer_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/trackViewer_1.34.0.tgz vignettes: vignettes/trackViewer/inst/doc/changeTracksStyles.html, vignettes/trackViewer/inst/doc/dandelionPlot.html, vignettes/trackViewer/inst/doc/lollipopPlot.html, vignettes/trackViewer/inst/doc/plotInteractionData.html, vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette: change the track styles, trackViewer Vignette: dandelionPlot, trackViewer Vignette: lollipopPlot, trackViewer Vignette: plot interaction data, trackViewer Vignette: overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/changeTracksStyles.R, vignettes/trackViewer/inst/doc/dandelionPlot.R, vignettes/trackViewer/inst/doc/lollipopPlot.R, vignettes/trackViewer/inst/doc/plotInteractionData.R, vignettes/trackViewer/inst/doc/trackViewer.R importsMe: NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 161 Package: tradeSeq Version: 1.12.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, igraph, ggplot2, princurve, methods, S4Vectors, tibble, Matrix, TrajectoryUtils, viridis, matrixStats, MASS Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment License: MIT + file LICENSE Archs: x64 MD5sum: c51d9231c2694ca6129c3940f693bddc NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (), Kelly Street [aut, ctb], Lieven Clement [aut, ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: RELEASE_3_16 git_last_commit: 5fdc85b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tradeSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tradeSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tradeSeq_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tradeSeq_1.12.0.tgz vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html, vignettes/tradeSeq/inst/doc/Monocle.html, vignettes/tradeSeq/inst/doc/multipleConditions.html, vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: More details on working with fitGAM, Monocle + tradeSeq, Differential expression across conditions, The tradeSeq workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R, vignettes/tradeSeq/inst/doc/Monocle.R, vignettes/tradeSeq/inst/doc/tradeSeq.R dependsOnMe: OSCA.advanced dependencyCount: 74 Package: TrajectoryGeometry Version: 1.6.0 Depends: R (>= 4.1) Imports: pracma, rgl, ggplot2, stats, methods Suggests: dplyr, knitr, RColorBrewer, rmarkdown License: MIT + file LICENSE MD5sum: 1a5951a5f19fe7189b4f0e48956284ae NeedsCompilation: no Title: This Package Discovers Directionality in Time and Pseudo-times Series of Gene Expression Patterns Description: Given a time series or pseudo-times series of gene expression data, we might wish to know: Do the changes in gene expression in these data exhibit directionality? Are there turning points in this directionality. Do different subsets of the data move in different directions? This package uses spherical geometry to probe these sorts of questions. In particular, if we are looking at (say) the first n dimensions of the PCA of gene expression, directionality can be detected as the clustering of points on the (n-1)-dimensional sphere. biocViews: BiologicalQuestion, StatisticalMethod, GeneExpression, SingleCell Author: Michael Shapiro [aut, cre] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TrajectoryGeometry git_branch: RELEASE_3_16 git_last_commit: 387a24d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TrajectoryGeometry_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TrajectoryGeometry_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TrajectoryGeometry_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TrajectoryGeometry_1.6.0.tgz vignettes: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.html, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.html vignetteTitles: SingleCellTrajectoryAnalysis, TrajectoryGeometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TrajectoryGeometry/inst/doc/SingleCellTrajectoryAnalysis.R, vignettes/TrajectoryGeometry/inst/doc/TrajectoryGeometry.R dependencyCount: 63 Package: TrajectoryUtils Version: 1.6.0 Depends: SingleCellExperiment Imports: methods, stats, Matrix, igraph, S4Vectors, SummarizedExperiment Suggests: BiocNeighbors, DelayedArray, DelayedMatrixStats, BiocParallel, testthat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: d213bded89a23b02a3da25319644b0c0 NeedsCompilation: no Title: Single-Cell Trajectory Analysis Utilities Description: Implements low-level utilities for single-cell trajectory analysis, primarily intended for re-use inside higher-level packages. Include a function to create a cluster-level minimum spanning tree and data structures to hold pseudotime inference results. biocViews: GeneExpression, SingleCell Author: Aaron Lun [aut, cre], Kelly Street [aut] Maintainer: Aaron Lun URL: https://bioconductor.org/packages/TrajectoryUtils VignetteBuilder: knitr BugReports: https://github.com/LTLA/TrajectoryUtils/issues git_url: https://git.bioconductor.org/packages/TrajectoryUtils git_branch: RELEASE_3_16 git_last_commit: 60747e6 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TrajectoryUtils_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TrajectoryUtils_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TrajectoryUtils_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TrajectoryUtils_1.6.0.tgz vignettes: vignettes/TrajectoryUtils/inst/doc/overview.html vignetteTitles: Trajectory utilities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TrajectoryUtils/inst/doc/overview.R dependsOnMe: slingshot, TSCAN importsMe: condiments, singleCellTK, tradeSeq dependencyCount: 31 Package: transcriptogramer Version: 1.20.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: ff728e344e6d72b0403d3232a4bb194c NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expressions are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: RELEASE_3_16 git_last_commit: bff6cc3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/transcriptogramer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transcriptogramer_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transcriptogramer_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/transcriptogramer_1.20.0.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 102 Package: transcriptR Version: 1.26.0 Depends: R (>= 3.5.0), methods Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: f86193cd8dde354de1a0ec4174a05606 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: RELEASE_3_16 git_last_commit: 0789312 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/transcriptR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transcriptR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transcriptR_1.26.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/transcriptR_1.26.0.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.html vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 156 Package: transformGamPoi Version: 1.4.0 Imports: glmGamPoi, DelayedArray, Matrix, MatrixGenerics, SummarizedExperiment, HDF5Array, methods, utils, Rcpp LinkingTo: Rcpp Suggests: testthat, TENxPBMCData, scran, knitr, rmarkdown License: GPL-3 MD5sum: fe8db1f7e6e1c8f386844e71cbc685a8 NeedsCompilation: yes Title: Variance Stabilizing Transformation for Gamma-Poisson Models Description: Variance-stabilizing transformations help with the analysis of heteroskedastic data (i.e., data where the variance is not constant, like count data). This package provide two types of variance stabilizing transformations: (1) methods based on the delta method (e.g., 'acosh', 'log(x+1)'), (2) model residual based (Pearson and randomized quantile residuals). biocViews: SingleCell, Normalization, Preprocessing, Regression Author: Constantin Ahlmann-Eltze [aut, cre] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/transformGamPoi VignetteBuilder: knitr BugReports: https://github.com/const-ae/transformGamPoi/issues git_url: https://git.bioconductor.org/packages/transformGamPoi git_branch: RELEASE_3_16 git_last_commit: 72df007 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/transformGamPoi_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transformGamPoi_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transformGamPoi_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/transformGamPoi_1.4.0.tgz vignettes: vignettes/transformGamPoi/inst/doc/transformGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transformGamPoi/inst/doc/transformGamPoi.R dependencyCount: 38 Package: transite Version: 1.16.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), ggseqlogo (>= 0.1), grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), utils LinkingTo: Rcpp (>= 1.0.4.8) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE Archs: x64 MD5sum: d4df2cace9fd17d5781ba2484b1353af NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (), Anna Gattinger [aut] (), Michael Yaffe [ths, cph] (), Ian Cannell [ths] () Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: RELEASE_3_16 git_last_commit: cfe6354 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/transite_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transite_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transite_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/transite_1.16.0.tgz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 57 Package: tRanslatome Version: 1.36.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq2, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 Archs: x64 MD5sum: 379b1b03daeb81ee625abd4b7c7cd0e7 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: RELEASE_3_16 git_last_commit: a17b63e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tRanslatome_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRanslatome_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRanslatome_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tRanslatome_1.36.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 117 Package: transomics2cytoscape Version: 1.8.0 Imports: RCy3, KEGGREST, dplyr, purrr, tibble Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 1e3aed736e319cfd99e796fbb91d9f26 NeedsCompilation: no Title: A tool set for 3D Trans-Omic network visualization with Cytoscape Description: transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4). biocViews: Network, Software, Pathways, DataImport, KEGG Author: Kozo Nishida [aut, cre] (), Katsuyuki Yugi [aut] () Maintainer: Kozo Nishida SystemRequirements: Cytoscape >= 3.9.1 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: RELEASE_3_16 git_last_commit: 338f4f1 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/transomics2cytoscape_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/transomics2cytoscape_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/transomics2cytoscape_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/transomics2cytoscape_1.8.0.tgz vignettes: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html vignetteTitles: transomics2cytoscape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R dependencyCount: 68 Package: TransView Version: 1.42.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.99.1) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 MD5sum: 0854804a6c5ea43ae5f1a51ba39094f3 NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/TransView git_branch: RELEASE_3_16 git_last_commit: a2ee720 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TransView_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TransView_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TransView_1.42.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TransView_1.42.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R dependencyCount: 21 Package: TraRe Version: 1.5.0 Depends: R (>= 4.1) Imports: hash, ggplot2, stats, methods, igraph, utils, glmnet, vbsr, grDevices, gplots, gtools, pvclust, R.utils, dqrng, SummarizedExperiment, BiocParallel, matrixStats Suggests: knitr, rmarkdown, BiocGenerics, RUnit, BiocStyle License: MIT + file LICENSE Archs: x64 MD5sum: 6aa97fed2b4a784bdcc92c9d26632a51 NeedsCompilation: no Title: Transcriptional Rewiring Description: TraRe (Transcriptional Rewiring) is an R package which contains the necessary tools to carry out several functions. Identification of module-based gene regulatory networks (GRN); score-based classification of these modules via a rewiring test; visualization of rewired modules to analyze condition-based GRN deregulation and drop out genes recovering via cliques methodology. For each tool, an html report can be generated containing useful information about the generated GRN and statistical data about the performed tests. These tools have been developed considering sequenced data (RNA-Seq). biocViews: GeneRegulation, RNASeq, GraphAndNetwork, Bayesian, GeneTarget, Classification Author: Jesus De La Fuente Cedeño [aut, cre, cph] (), Mikel Hernaez [aut, cph, ths] (), Charles Blatti [aut, cph] () Maintainer: Jesus De La Fuente Cedeño URL: https://github.com/ubioinformat/TraRe/tree/master VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TraRe git_branch: master git_last_commit: 6e8284d git_last_commit_date: 2022-04-26 Date/Publication: 2022-04-26 source.ver: src/contrib/TraRe_1.5.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TraRe_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/TraRe_1.6.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TraRe_1.6.2.tgz vignettes: vignettes/TraRe/inst/doc/TraRe.html vignetteTitles: TraRe: Identification of conditions dependant Gene Regulatory Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TraRe/inst/doc/TraRe.R dependencyCount: 82 Package: traseR Version: 1.28.0 Depends: R (>= 3.5.0), GenomicRanges, IRanges, BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL Archs: x64 MD5sum: aa0f49946d3ea6ac2cdbfccf681c6925 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: RELEASE_3_16 git_last_commit: a6725f2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/traseR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/traseR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.2/traseR_1.28.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/traseR_1.28.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 48 Package: Travel Version: 1.6.0 Imports: Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, inline, parallel License: GPL-3 MD5sum: da652100a57515f0dbaf7084fb35525e NeedsCompilation: yes Title: An utility to create an ALTREP object with a virtual pointer Description: Creates a virtual pointer for R's ALTREP object which does not have the data allocates in memory. The pointer is made by the file mapping of a virtual file so it behaves exactly the same as a regular pointer. All the requests to access the pointer will be sent to the underlying file system and eventually handled by a customized data-reading function. The main purpose of the package is to reduce the memory consumption when using R's vector to represent a large data. The use cases of the package include on-disk data representation, compressed vector(e.g. RLE) and etc. biocViews: Infrastructure Author: Jiefei Wang [aut, cre], Martin Morgan [aut] Maintainer: Jiefei Wang URL: https://github.com/Jiefei-Wang/Travel SystemRequirements: C++11 Windows: Dokan Linux&Mac: fuse, pkg-config VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/Travel/issues git_url: https://git.bioconductor.org/packages/Travel git_branch: RELEASE_3_16 git_last_commit: 99e6fd3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Travel_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Travel_1.6.0.zip vignettes: vignettes/Travel/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/Travel/inst/doc/vignette.R dependencyCount: 3 Package: traviz Version: 1.4.0 Depends: R (>= 4.0) Imports: ggplot2, viridis, mgcv, SingleCellExperiment, slingshot, princurve, Biobase, methods, RColorBrewer, SummarizedExperiment, grDevices, graphics, rgl Suggests: scater, dplyr, testthat (>= 3.0.0), covr, S4Vectors, rmarkdown, knitr License: MIT + file LICENSE Archs: x64 MD5sum: 6e5797164f2dc1529cc6e8724f76247b NeedsCompilation: no Title: Trajectory functions for visualization and interpretation. Description: traviz provides a suite of functions to plot trajectory related objects from Bioconductor packages. It allows plotting trajectories in reduced dimension, as well as averge gene expression smoothers as a function of pseudotime. Asides from general utility functions, traviz also allows plotting trajectories estimated by Slingshot, as well as smoothers estimated by tradeSeq. Furthermore, it allows for visualization of Slingshot trajectories using ggplot2. biocViews: GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, ctb], Kelly Street [aut, ctb], Koen Van den Berge [aut, cre] Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/traviz git_branch: RELEASE_3_16 git_last_commit: fd2c942 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/traviz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/traviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/traviz_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/traviz_1.4.0.tgz vignettes: vignettes/traviz/inst/doc/slingshot.html, vignettes/traviz/inst/doc/traviz.html vignetteTitles: ggplot2 + slingshot, traviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traviz/inst/doc/slingshot.R, vignettes/traviz/inst/doc/traviz.R dependencyCount: 87 Package: TreeAndLeaf Version: 1.10.0 Depends: R(>= 4.0) Imports: RedeR(>= 1.40.4), igraph, ape Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, geneplast, ggtree, ggplot2, dplyr, dendextend, RColorBrewer License: Artistic-2.0 MD5sum: 5c716b5d382c654865933d805b2505c4 NeedsCompilation: no Title: Displaying binary trees with focus on dendrogram leaves Description: The TreeAndLeaf package combines unrooted and force-directed graph algorithms in order to layout binary trees, aiming to represent multiple layers of information onto dendrogram leaves. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro Maintainer: Milena A. Cardoso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: RELEASE_3_16 git_last_commit: f852bb0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TreeAndLeaf_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TreeAndLeaf_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TreeAndLeaf_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TreeAndLeaf_1.10.0.tgz vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html vignetteTitles: TreeAndLeaf: an graph layout to dendrograms. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R suggestsMe: RedeR dependencyCount: 19 Package: treeio Version: 1.22.0 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble, tidytree (>= 0.3.9), utils Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, testthat, tidyr, vroom, xml2, yaml, purrr License: Artistic-2.0 MD5sum: b9488ba3eb48293ae06950a9336703b7 NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment, Phylogenetics Author: Guangchuang Yu [aut, cre] (), Tommy Tsan-Yuk Lam [ctb, ths], Shuangbin Xu [ctb] (), Bradley Jones [ctb], Casey Dunn [ctb], Tyler Bradley [ctb], Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/treeio (devel), https://docs.ropensci.org/treeio/ (docs), https://www.amazon.com/Integration-Manipulation-Visualization-Phylogenetic-Computational-ebook/dp/B0B5NLZR1Z/ (book), https://doi.org/10.1093/molbev/msz240 (paper) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: RELEASE_3_16 git_last_commit: eb24a85 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/treeio_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/treeio_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/treeio_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/treeio_1.22.0.tgz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree, MicrobiotaProcess, TreeSummarizedExperiment, EvoPhylo, ggmotif, RevGadgets, shinyTempSignal suggestsMe: enrichplot, ggtreeDendro, ggtreeExtra, rfaRm, idiogramFISH, nosoi dependencyCount: 38 Package: treekoR Version: 1.6.1 Depends: R (>= 4.1) Imports: stats, utils, tidyr, dplyr, data.table, ggiraph, ggplot2, hopach, ape, ggtree, patchwork, SingleCellExperiment, diffcyt, edgeR, lme4, multcomp Suggests: knitr, rmarkdown, BiocStyle, CATALYST, testthat (>= 3.0.0) License: GPL-3 MD5sum: d874ceff6b8e46248616ae4bde1f627a NeedsCompilation: no Title: Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations Description: treekoR is a novel framework that aims to utilise the hierarchical nature of single cell cytometry data to find robust and interpretable associations between cell subsets and patient clinical end points. These associations are aimed to recapitulate the nested proportions prevalent in workflows inovlving manual gating, which are often overlooked in workflows using automatic clustering to identify cell populations. We developed treekoR to: Derive a hierarchical tree structure of cell clusters; quantify a cell types as a proportion relative to all cells in a sample (%total), and, as the proportion relative to a parent population (%parent); perform significance testing using the calculated proportions; and provide an interactive html visualisation to help highlight key results. biocViews: Clustering, DifferentialExpression, FlowCytometry, ImmunoOncology, MassSpectrometry, SingleCell, Software, StatisticalMethod, Visualization Author: Adam Chan [aut, cre], Ellis Patrick [ctb] Maintainer: Adam Chan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/treekoR git_branch: RELEASE_3_16 git_last_commit: dedc94c git_last_commit_date: 2023-02-08 Date/Publication: 2023-02-09 source.ver: src/contrib/treekoR_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/treekoR_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.2/treekoR_1.6.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/treekoR_1.6.1.tgz vignettes: vignettes/treekoR/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treekoR/inst/doc/vignette.R dependencyCount: 181 Package: TreeSummarizedExperiment Version: 2.6.0 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18), Biostrings Imports: methods, BiocGenerics, utils, ape, rlang, dplyr, SummarizedExperiment, BiocParallel, IRanges, treeio Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 9e54fe5296c06b1ba6f98c59027f7017 NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] (), Felix G.M. Ernst [ctb] () Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: RELEASE_3_16 git_last_commit: 2a6ac19 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TreeSummarizedExperiment_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TreeSummarizedExperiment_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TreeSummarizedExperiment_2.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TreeSummarizedExperiment_2.6.0.tgz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.html vignetteTitles: 1. Introduction to TreeSE, 2. Combine TSEs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R, vignettes/TreeSummarizedExperiment/inst/doc/The_combination_of_multiple_TSEs.R dependsOnMe: ExperimentSubset, mia, miaSim, miaViz, curatedMetagenomicData, MicrobiomeBenchmarkData, microbiomeDataSets importsMe: ANCOMBC, benchdamic, CBEA suggestsMe: philr dependencyCount: 67 Package: TREG Version: 1.2.0 Depends: R (>= 4.2), SummarizedExperiment Imports: Matrix, purrr, rafalib Suggests: BiocFileCache, BiocStyle, dplyr, ggplot2, knitr, pheatmap, sessioninfo, RefManageR, rmarkdown, testthat (>= 3.0.0), tibble, tidyr, SingleCellExperiment License: Artistic-2.0 MD5sum: 72bde92193485231bc3a0761bd1ef8b7 NeedsCompilation: no Title: Tools for finding Total RNA Expression Genes in single nucleus RNA-seq data Description: RNA abundance and cell size parameters could improve RNA-seq deconvolution algorithms to more accurately estimate cell type proportions given the different cell type transcription activity levels. A Total RNA Expression Gene (TREG) can facilitate estimating total RNA content using single molecule fluorescent in situ hybridization (smFISH). We developed a data-driven approach using a measure of expression invariance to find candidate TREGs in postmortem human brain single nucleus RNA-seq. This R package implements the method for identifying candidate TREGs from snRNA-seq data. biocViews: Software, SingleCell, RNASeq, GeneExpression, Transcriptomics, Transcription, Sequencing Author: Louise Huuki-Myers [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Louise Huuki-Myers URL: https://github.com/LieberInstitute/TREG, http://research.libd.org/TREG/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/TREG git_url: https://git.bioconductor.org/packages/TREG git_branch: RELEASE_3_16 git_last_commit: 24c4208 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TREG_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TREG_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TREG_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TREG_1.2.0.tgz vignettes: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.html vignetteTitles: How to find Total RNA Expression Genes (TREGs) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TREG/inst/doc/finding_Total_RNA_Expression_Genes.R dependencyCount: 34 Package: trena Version: 1.20.0 Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, RMySQL, lassopv, randomForest, xgboost, RPostgreSQL, methods, DBI, BSgenome, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38, org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi, WGCNA Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, formatR, markdown, BiocParallel, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Athaliana.TAIR.TAIR9 License: GPL-3 MD5sum: eff8c0040b54fdacb6b0b115ad2279c0 NeedsCompilation: no Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning Description: Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available. biocViews: Transcription, GeneRegulation, NetworkInference, FeatureExtraction, Regression, SystemsBiology, GeneExpression Author: Seth Ament , Paul Shannon , Matthew Richards Maintainer: Paul Shannon URL: https://pricelab.github.io/trena/ VignetteBuilder: knitr, rmarkdown, formatR, markdown BugReports: https://github.com/PriceLab/trena/issues git_url: https://git.bioconductor.org/packages/trena git_branch: RELEASE_3_16 git_last_commit: 0b1d1b9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/trena_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/trena_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/trena_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/trena_1.20.0.tgz vignettes: vignettes/trena/inst/doc/caseStudyFour.html, vignettes/trena/inst/doc/caseStudyOne.html, vignettes/trena/inst/doc/caseStudyThree.html, vignettes/trena/inst/doc/caseStudyTwo.html, vignettes/trena/inst/doc/overview.html, vignettes/trena/inst/doc/simple.html, vignettes/trena/inst/doc/tiny.html, vignettes/trena/inst/doc/TReNA_Vignette.html vignetteTitles: "Case Study Four: a novel regulator of GATA2 in erythropoieis?", "Case Study One: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Three: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Two reproduces known regulation of NFE2 by GATA1 in erytrhop RNA-seq", "TRENA: computational prediction of gene regulation", "Explore output controls", "Tiny Vignette Example", A Brief Introduction to TReNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trena/inst/doc/overview.R, vignettes/trena/inst/doc/simple.R, vignettes/trena/inst/doc/tiny.R, vignettes/trena/inst/doc/TReNA_Vignette.R dependencyCount: 165 Package: Trendy Version: 1.20.0 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: 0055a39e66c4c429a9be2d80d871dcf9 NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: RELEASE_3_16 git_last_commit: b60003a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Trendy_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Trendy_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Trendy_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Trendy_1.20.0.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 93 Package: TRESS Version: 1.4.0 Depends: R (>= 4.1.0), parallel, S4Vectors Imports: utils, rtracklayer, Matrix, matrixStats, stats, methods, graphics, GenomicRanges, GenomicFeatures, IRanges, Rsamtools, AnnotationDbi Suggests: knitr, rmarkdown,BiocStyle License: GPL-3 + file LICENSE MD5sum: 964a835ee67e6b7b8acafcda613d1acc NeedsCompilation: no Title: Toolbox for mRNA epigenetics sequencing analysis Description: This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detection of transcriptome wide m6A methylation regions 2. detection of transcriptome wide differential m6A methylation regions. biocViews: Epigenetics, RNASeq, PeakDetection, DifferentialMethylation Author: Zhenxing Guo [aut, cre], Hao Wu [ctb] Maintainer: Zhenxing Guo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TRESS git_branch: RELEASE_3_16 git_last_commit: 52ba838 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TRESS_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/TRESS_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TRESS_1.4.0.tgz vignettes: vignettes/TRESS/inst/doc/TRESS.html vignetteTitles: The TRESS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TRESS/inst/doc/TRESS.R dependencyCount: 96 Package: tricycle Version: 1.6.0 Depends: R (>= 4.0), SingleCellExperiment Imports: methods, circular, ggplot2, ggnewscale, AnnotationDbi, scater, GenomicRanges, IRanges, S4Vectors, scattermore, dplyr, RColorBrewer, grDevices, stats, SummarizedExperiment, utils Suggests: testthat (>= 3.0.0), BiocStyle, knitr, rmarkdown, CircStats, cowplot, htmltools, Seurat, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 376bcece7ed1f6bb66cc857fb12e94bc NeedsCompilation: no Title: tricycle: Transferable Representation and Inference of cell cycle Description: The package contains functions to infer and visualize cell cycle process using Single Cell RNASeq data. It exploits the idea of transfer learning, projecting new data to the previous learned biologically interpretable space. We provide a pre-learned cell cycle space, which could be used to infer cell cycle time of human and mouse single cell samples. In addition, we also offer functions to visualize cell cycle time on different embeddings and functions to build new reference. biocViews: SingleCell, Software, Transcriptomics, RNASeq, Transcription, BiologicalQuestion, DimensionReduction, ImmunoOncology Author: Shijie Zheng [aut, cre] Maintainer: Shijie Zheng URL: https://github.com/hansenlab/tricycle VignetteBuilder: knitr BugReports: https://github.com/hansenlab/tricycle/issues git_url: https://git.bioconductor.org/packages/tricycle git_branch: RELEASE_3_16 git_last_commit: adf625e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tricycle_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tricycle_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tricycle_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tricycle_1.6.0.tgz vignettes: vignettes/tricycle/inst/doc/tricycle.html vignetteTitles: tricycle: Transferable Representation and Inference of Cell Cycle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tricycle/inst/doc/tricycle.R dependencyCount: 125 Package: trigger Version: 1.44.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 MD5sum: fb4bb1d52ccd745baf796f1fd5569590 NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/trigger git_branch: RELEASE_3_16 git_last_commit: e4282cc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/trigger_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/trigger_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/trigger_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/trigger_1.44.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R dependencyCount: 96 Package: trio Version: 3.36.0 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1) Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 Archs: x64 MD5sum: 2c9061c3b2e2a939343128eaa05b4188 NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: RELEASE_3_16 git_last_commit: 0f0eabb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/trio_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/trio_3.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/trio_3.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/trio_3.36.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 18 Package: triplex Version: 1.38.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE MD5sum: e67fc4e3ff830520e30bdeba6c306d73 NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: RELEASE_3_16 git_last_commit: 41843cf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/triplex_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/triplex_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/triplex_1.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/triplex_1.38.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 20 Package: tripr Version: 1.4.0 Depends: shiny (>= 1.6.0), shinyBS Imports: shinyjs, shinyFiles, plyr, data.table, DT, stringr, stringdist, plot3D, gridExtra, RColorBrewer, plotly, dplyr, pryr, config (>= 0.3.1), golem (>= 0.3.1), methods, grDevices, graphics, stats, utils Suggests: BiocGenerics, shinycssloaders, tidyverse, BiocManager, Biostrings, xtable, rlist, motifStack, knitr, rmarkdown, testthat (>= 3.0.0), fs, BiocStyle, RefManageR, biocthis Enhances: parallel License: MIT + file LICENSE Archs: x64 MD5sum: f4bb70ae994279d2b16fe633d3cb1539 NeedsCompilation: no Title: T-cell Receptor/Immunoglobulin Profiler (TRIP) Description: TRIP is a software framework that provides analytics services on antigen receptor (B cell receptor immunoglobulin, BcR IG | T cell receptor, TR) gene sequence data. It is a web application written in R Shiny. It takes as input the output files of the IMGT/HighV-Quest tool. Users can select to analyze the data from each of the input samples separately, or the combined data files from all samples and visualize the results accordingly. biocViews: BatchEffect, MultipleComparison, GeneExpression, ImmunoOncology, TargetedResequencing Author: Maria Th. Kotouza [aut], Katerina Gemenetzi [aut], Chrysi Galigalidou [aut], Elisavet Vlachonikola [aut], Nikolaos Pechlivanis [cre], Andreas Agathangelidis [aut], Raphael Sandaltzopoulos [aut], Pericles A. Mitkas [aut], Kostas Stamatopoulos [aut], Anastasia Chatzidimitriou [aut], Fotis E. Psomopoulos [aut], Iason Ofeidis [aut] Maintainer: Nikolaos Pechlivanis URL: https://github.com/BiodataAnalysisGroup/tripr VignetteBuilder: knitr BugReports: https://github.com/BiodataAnalysisGroup/tripr/issues git_url: https://git.bioconductor.org/packages/tripr git_branch: RELEASE_3_16 git_last_commit: abfb7d8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tripr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tripr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tripr_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tripr_1.4.0.tgz vignettes: vignettes/tripr/inst/doc/tripr_guide.html vignetteTitles: tripr User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tripr/inst/doc/tripr_guide.R dependencyCount: 105 Package: tRNA Version: 1.16.1 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE MD5sum: 2ac5c488cbf2308cab07cf47ebaa5621 NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] () Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: RELEASE_3_16 git_last_commit: 3be191a git_last_commit_date: 2022-12-19 Date/Publication: 2022-12-19 source.ver: src/contrib/tRNA_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRNA_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tRNA_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tRNA_1.16.1.tgz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 53 Package: tRNAdbImport Version: 1.16.0 Depends: R (>= 3.5), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, BiocGenerics, stringr, xml2, S4Vectors, methods, httr, IRanges, utils Suggests: knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE MD5sum: bb0f68515995204b16127530bae60894 NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: RELEASE_3_16 git_last_commit: 3d39f97 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tRNAdbImport_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRNAdbImport_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRNAdbImport_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tRNAdbImport_1.16.0.tgz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R importsMe: EpiTxDb dependencyCount: 62 Package: tRNAscanImport Version: 1.18.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: 8572d32d3d35e9d8a60400f8846b8721 NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/tRNAscanImport VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: RELEASE_3_16 git_last_commit: fa73364 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tRNAscanImport_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tRNAscanImport_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tRNAscanImport_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tRNAscanImport_1.18.0.tgz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 79 Package: TRONCO Version: 2.30.0 Depends: R (>= 4.1.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways License: GPL-3 MD5sum: 08f09974b84d53c3628f76b13952ebb4 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut, cre], Luca De Sano [aut] (), Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] () Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: RELEASE_3_16 git_last_commit: 5014798 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TRONCO_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TRONCO_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TRONCO_2.30.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TRONCO_2.30.0.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/vignette.R dependencyCount: 46 Package: TSCAN Version: 1.36.0 Depends: SingleCellExperiment, TrajectoryUtils Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, DelayedArray, S4Vectors Suggests: knitr, testthat, scuttle, scran, metapod, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: b40f5a58e7db2ee644afe1b9583d4e95 NeedsCompilation: no Title: Tools for Single-Cell Analysis Description: Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb] Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: RELEASE_3_16 git_last_commit: 7488959 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TSCAN_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TSCAN_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TSCAN_1.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TSCAN_1.36.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R dependsOnMe: OSCA.advanced, OSCA.multisample importsMe: FEAST, singleCellTK, DIscBIO suggestsMe: condiments dependencyCount: 88 Package: tscR Version: 1.10.0 Depends: R (>= 4.1.0), dplyr Imports: gridExtra, methods, dtw, class, kmlShape, graphics, cluster, RColorBrewer, grDevices, knitr, rmarkdown, prettydoc, grid, ggplot2, latex2exp, stats, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors Suggests: testthat License: Artistic-2.0 MD5sum: 70de47cf7873dcd92898d8243a8f0e80 NeedsCompilation: yes Title: A time series clustering package combining slope and Frechet distances Description: Clustering for time series data using slope distance and/or shape distance. biocViews: GeneExpression, Clustering, DNAMethylation, Microarray Author: Fernando Pérez-Sanz [aut, cre], Miriam Riquelme-Pérez [aut] Maintainer: Fernando Pérez-Sanz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tscR git_branch: RELEASE_3_16 git_last_commit: 2d33cb7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tscR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tscR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tscR_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tscR_1.10.0.tgz vignettes: vignettes/tscR/inst/doc/tscR.html vignetteTitles: tscR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tscR/inst/doc/tscR.R dependencyCount: 87 Package: ttgsea Version: 1.6.3 Depends: keras Imports: tm, text2vec, tokenizers, textstem, stopwords, data.table, purrr, DiagrammeR, stats Suggests: fgsea, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 Archs: x64 MD5sum: bd49eb50b6eab67a2882d0260a8afc72 NeedsCompilation: no Title: Tokenizing Text of Gene Set Enrichment Analysis Description: Functional enrichment analysis methods such as gene set enrichment analysis (GSEA) have been widely used for analyzing gene expression data. GSEA is a powerful method to infer results of gene expression data at a level of gene sets by calculating enrichment scores for predefined sets of genes. GSEA depends on the availability and accuracy of gene sets. There are overlaps between terms of gene sets or categories because multiple terms may exist for a single biological process, and it can thus lead to redundancy within enriched terms. In other words, the sets of related terms are overlapping. Using deep learning, this pakage is aimed to predict enrichment scores for unique tokens or words from text in names of gene sets to resolve this overlapping set issue. Furthermore, we can coin a new term by combining tokens and find its enrichment score by predicting such a combined tokens. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ttgsea git_branch: RELEASE_3_16 git_last_commit: 5b3d6a6 git_last_commit_date: 2022-11-11 Date/Publication: 2022-11-11 source.ver: src/contrib/ttgsea_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.2/ttgsea_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.2/ttgsea_1.6.3.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ttgsea_1.6.3.tgz vignettes: vignettes/ttgsea/inst/doc/ttgsea.html vignetteTitles: ttgsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ttgsea/inst/doc/ttgsea.R importsMe: DeepPINCS, GenProSeq dependencyCount: 138 Package: TTMap Version: 1.20.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: b3d3bcd7fc6822e1f3a854bed8b6f9ad NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: RELEASE_3_16 git_last_commit: fcf4abe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TTMap_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TTMap_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TTMap_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TTMap_1.20.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 59 Package: TurboNorm Version: 1.46.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL MD5sum: 9754fed558476f702dfeb91c3f40b31b NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: RELEASE_3_16 git_last_commit: 6fb2a91 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TurboNorm_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TurboNorm_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TurboNorm_1.46.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TurboNorm_1.46.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 17 Package: TVTB Version: 1.24.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 2edad8c2e9c3df0b1c1551b4bac79ce1 NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: RELEASE_3_16 git_last_commit: 617292c git_last_commit_date: 2022-11-01 Date/Publication: 2022-12-09 source.ver: src/contrib/TVTB_1.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.2/TVTB_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TVTB_1.24.0.tgz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 160 Package: tweeDEseq Version: 1.44.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: x64 MD5sum: b1cbbdfdc99eac72276bbf48b6129fac NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: RELEASE_3_16 git_last_commit: 8bd23d4 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/tweeDEseq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/tweeDEseq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/tweeDEseq_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tweeDEseq_1.44.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R importsMe: ptmixed dependencyCount: 23 Package: twilight Version: 1.74.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) Archs: x64 MD5sum: 90b4852f79f1e3fbcd709b1cae4af226 NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: RELEASE_3_16 git_last_commit: bca3845 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/twilight_1.74.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/twilight_1.74.0.zip mac.binary.ver: bin/macosx/contrib/4.2/twilight_1.74.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/twilight_1.74.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList dependencyCount: 8 Package: twoddpcr Version: 1.22.0 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: x64 MD5sum: 8d5875d2cf25aaad313964784255c6bb NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: RELEASE_3_16 git_last_commit: 04bc681 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/twoddpcr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/twoddpcr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/twoddpcr_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/twoddpcr_1.22.0.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 65 Package: txcutr Version: 1.4.0 Depends: R (>= 4.1.0) Imports: AnnotationDbi, GenomicFeatures, IRanges, GenomicRanges, BiocGenerics, Biostrings, S4Vectors, rtracklayer, BiocParallel, stats, methods, utils Suggests: RefManageR, BiocStyle, knitr, sessioninfo, rmarkdown, testthat (>= 3.0.0), TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 MD5sum: 0523d546264d21010fc1494c6608cd58 NeedsCompilation: no Title: Transcriptome CUTteR Description: Various mRNA sequencing library preparation methods generate sequencing reads specifically from the transcript ends. Analyses that focus on quantification of isoform usage from such data can be aided by using truncated versions of transcriptome annotations, both at the alignment or pseudo-alignment stage, as well as in downstream analysis. This package implements some convenience methods for readily generating such truncated annotations and their corresponding sequences. biocViews: Alignment, Annotation, RNASeq, Sequencing, Transcriptomics Author: Mervin Fansler [aut, cre] () Maintainer: Mervin Fansler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/txcutr git_branch: RELEASE_3_16 git_last_commit: 2d0b602 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/txcutr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/txcutr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/txcutr_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/txcutr_1.4.0.tgz vignettes: vignettes/txcutr/inst/doc/intro.html vignetteTitles: Introduction to txcutr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/txcutr/inst/doc/intro.R dependencyCount: 96 Package: tximeta Version: 1.16.1 Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, GenomicFeatures, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, GenomeInfoDb, tools, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db, DESeq2, fishpond, edgeR, limma, devtools License: GPL-2 MD5sum: 30f52ccaf177a4c334bda9032557d04a NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and alevin with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing, RNASeq, SingleCell, Transcriptomics, Transcription, GeneExpression, FunctionalGenomics, ReproducibleResearch, ReportWriting, ImmunoOncology Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter Hickey [aut, ctb], Rob Patro [aut, ctb], NIH NHGRI [fnd], CZI [fnd] Maintainer: Michael Love URL: https://github.com/mikelove/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: RELEASE_3_16 git_last_commit: 2072392 git_last_commit_date: 2023-02-12 Date/Publication: 2023-02-13 source.ver: src/contrib/tximeta_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tximeta_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tximeta_1.16.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tximeta_1.16.1.tgz vignettes: vignettes/tximeta/inst/doc/tximeta.html vignetteTitles: Transcript quantification import with automatic metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximeta/inst/doc/tximeta.R dependsOnMe: rnaseqGene importsMe: IsoformSwitchAnalyzeR suggestsMe: DESeq2, fishpond, fluentGenomics dependencyCount: 130 Package: tximport Version: 1.26.1 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, eds License: LGPL (>=2) MD5sum: 633cf06a9fd236b1ad5518a7a021390d NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: RELEASE_3_16 git_last_commit: b665ba9 git_last_commit_date: 2022-12-15 Date/Publication: 2022-12-16 source.ver: src/contrib/tximport_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/tximport_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.2/tximport_1.26.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/tximport_1.26.1.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, EventPointer, IsoformSwitchAnalyzeR, singleCellTK, tximeta, seeker suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, SummarizedBenchmark, variancePartition dependencyCount: 3 Package: TypeInfo Version: 1.64.0 Depends: methods Suggests: Biobase License: BSD_2_clause MD5sum: 19d4bcec9c20ca8c83d06ded6de554d8 NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang git_url: https://git.bioconductor.org/packages/TypeInfo git_branch: RELEASE_3_16 git_last_commit: 005a3cd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/TypeInfo_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/TypeInfo_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/TypeInfo_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/TypeInfo_1.64.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: UCell Version: 2.2.0 Depends: R(>= 4.2.0) Imports: methods, data.table(>= 1.13.6), Matrix, BiocParallel, BiocNeighbors, SingleCellExperiment, SummarizedExperiment Suggests: Seurat, scater, scRNAseq, reshape2, patchwork, ggplot2, BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: e3b72a81583fd418415b8a5329585ec5 NeedsCompilation: no Title: Rank-based signature enrichment analysis for single-cell data Description: UCell is a package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with SingleCellExperiment and Seurat objects. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, GeneExpression, CellBasedAssays Author: Massimo Andreatta [aut, cre] (), Santiago Carmona [aut] () Maintainer: Massimo Andreatta URL: https://github.com/carmonalab/UCell VignetteBuilder: knitr BugReports: https://github.com/carmonalab/UCell/issues git_url: https://git.bioconductor.org/packages/UCell git_branch: RELEASE_3_16 git_last_commit: eeac63d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/UCell_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/UCell_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/UCell_2.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/UCell_2.2.0.tgz vignettes: vignettes/UCell/inst/doc/UCell_sce.html, vignettes/UCell/inst/doc/UCell_Seurat.html, vignettes/UCell/inst/doc/UCell_vignette_basic.html vignetteTitles: 2. Using UCell with SingleCellExperiment, 3. Using UCell with Seurat, 1. Gene signature scoring with UCell hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/UCell/inst/doc/UCell_sce.R, vignettes/UCell/inst/doc/UCell_Seurat.R, vignettes/UCell/inst/doc/UCell_vignette_basic.R importsMe: escape, scGate suggestsMe: SCpubr dependencyCount: 40 Package: Ularcirc Version: 1.16.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments, Organism.dplyr, plotgardener, R.utils, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 994e706cef22c67591683a53e01ed2a6 NeedsCompilation: no Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: RELEASE_3_16 git_last_commit: c4f780d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Ularcirc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Ularcirc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Ularcirc_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Ularcirc_1.16.0.tgz vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html vignetteTitles: Ularcirc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R dependencyCount: 161 Package: UMI4Cats Version: 1.8.1 Depends: R (>= 4.0.0), SummarizedExperiment Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo, ggplot2, reshape2, regioneR, IRanges, S4Vectors, magrittr, dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools, stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments, RColorBrewer, utils, grDevices, stats, org.Hs.eg.db, annotate, TxDb.Hsapiens.UCSC.hg19.knownGene, rlang, GenomicFeatures, BiocFileCache, rappdirs, fda, BiocGenerics Suggests: knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, tidyr, testthat License: Artistic-2.0 MD5sum: 5571e5d93940170b12edf3555795a437 NeedsCompilation: no Title: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data Description: UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. biocViews: QualityControl, Preprocessing, Alignment, Normalization, Visualization, Sequencing, Coverage Author: Mireia Ramos-Rodriguez [aut, cre] (), Marc Subirana-Granes [aut], Lorenzo Pasquali [aut] Maintainer: Mireia Ramos-Rodriguez URL: https://github.com/Pasquali-lab/UMI4Cats VignetteBuilder: knitr BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues git_url: https://git.bioconductor.org/packages/UMI4Cats git_branch: RELEASE_3_16 git_last_commit: 9626832 git_last_commit_date: 2022-12-15 Date/Publication: 2022-12-15 source.ver: src/contrib/UMI4Cats_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/UMI4Cats_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/UMI4Cats_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/UMI4Cats_1.8.1.tgz vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html vignetteTitles: Analyzing UMI-4C data with UMI4Cats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R dependencyCount: 156 Package: uncoverappLib Version: 1.8.1 Imports: markdown, shiny, shinyjs, shinyBS, shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx, condformat, stringr, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs, TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils,S4Vectors, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 6b73517138698bd9aebebfa9aacc3015 NeedsCompilation: no Title: Interactive graphical application for clinical assessment of sequence coverage at the base-pair level Description: a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. uncoverAPP provides a statisticl summary of coverage given target file or genes name. biocViews: Software, Visualization, Annotation, Coverage Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut] Maintainer: Emanuela Iovino URL: https://github.com/Manuelaio/uncoverappLib VignetteBuilder: knitr BugReports: https://github.com/Manuelaio/uncoverappLib/issues git_url: https://git.bioconductor.org/packages/uncoverappLib git_branch: RELEASE_3_16 git_last_commit: 5506789 git_last_commit_date: 2023-03-30 Date/Publication: 2023-03-31 source.ver: src/contrib/uncoverappLib_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/uncoverappLib_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.2/uncoverappLib_1.8.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/uncoverappLib_1.8.1.tgz vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html vignetteTitles: uncoverappLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R dependencyCount: 185 Package: UNDO Version: 1.40.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 669423a1fa44919cc5658cd0a061b73b NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: RELEASE_3_16 git_last_commit: f3b56e2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/UNDO_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/UNDO_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/UNDO_1.40.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/UNDO_1.40.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 10 Package: unifiedWMWqPCR Version: 1.34.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: f211e2cd9f763e8247634b33004c6087 NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: RELEASE_3_16 git_last_commit: 75abdbc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/unifiedWMWqPCR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/unifiedWMWqPCR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/unifiedWMWqPCR_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/unifiedWMWqPCR_1.34.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 21 Package: UniProt.ws Version: 2.38.1 Depends: methods, utils, RSQLite, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, BiocBaseUtils, rjsoncons, jsonlite, httr, httpcache, progress Suggests: RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 7d05e85235c87ea0de642a1b442fe9ba NeedsCompilation: no Title: R Interface to UniProt Web Services Description: The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. This package provides a collection of functions for retrieving, processing, and re-packaging UniProt web services. The package makes use of UniProt's modernized REST API and allows mapping of identifiers accross different databases. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Marcel Ramos [aut, cre] () Maintainer: Marcel Ramos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_16 git_last_commit: cdb97ca git_last_commit_date: 2022-12-19 Date/Publication: 2022-12-19 source.ver: src/contrib/UniProt.ws_2.38.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/UniProt.ws_2.38.1.zip mac.binary.ver: bin/macosx/contrib/4.2/UniProt.ws_2.38.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/UniProt.ws_2.38.1.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: dagLogo, drugTargetInteractions suggestsMe: cleaver, qPLEXanalyzer dependencyCount: 70 Package: Uniquorn Version: 2.18.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation, data.table Suggests: testthat, knitr, rmarkdown, BiocGenerics License: Artistic-2.0 MD5sum: 29cbe0d26aaa3866d78607bbfc6b6855 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: 'Uniquorn' enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: RELEASE_3_16 git_last_commit: 8822afd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Uniquorn_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Uniquorn_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Uniquorn_2.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Uniquorn_2.18.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 106 Package: universalmotif Version: 1.16.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, MASS, ggplot2, yaml, IRanges, Rcpp, Biostrings, BiocGenerics, S4Vectors, rlang, grid LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, BiocParallel, seqLogo, motifStack, dplyr, ape, ggtree, processx, ggseqlogo, cowplot, GenomicRanges, ggbio Enhances: PWMEnrich, rGADEM License: GPL-3 MD5sum: 1fb81c1cc2856bddce67a6b201bb3b52 NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] (), Spencer Nystrom [ctb] () Maintainer: Benjamin Jean-Marie Tremblay URL: https://bioconductor.org/packages/universalmotif/ VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: RELEASE_3_16 git_last_commit: 17714c8 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/universalmotif_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/universalmotif_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/universalmotif_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/universalmotif_1.16.0.tgz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R importsMe: circRNAprofiler, memes, ggmotif suggestsMe: spiky dependencyCount: 51 Package: updateObject Version: 1.2.0 Depends: R (>= 4.2.0), methods, BiocGenerics, S4Vectors Imports: utils, digest Suggests: GenomicRanges, SummarizedExperiment, InteractionSet, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 Archs: x64 MD5sum: d61a2390c7b3b7aaa5d54edba8131438 NeedsCompilation: no Title: Find/fix old serialized S4 instances Description: A set of tools built around updateObject() to work with old serialized S4 instances. The package is primarily useful to package maintainers who want to update the serialized S4 instances included in their package. This is still work-in-progress. biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès [aut, cre] Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/updateObject SystemRequirements: git VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/updateObject/issues git_url: https://git.bioconductor.org/packages/updateObject git_branch: RELEASE_3_16 git_last_commit: 0c8e92c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/updateObject_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/updateObject_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/updateObject_1.2.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/updateObject_1.2.0.tgz vignettes: vignettes/updateObject/inst/doc/updateObject.html vignetteTitles: A quick introduction to the updateObject package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/updateObject/inst/doc/updateObject.R dependencyCount: 8 Package: uSORT Version: 1.24.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 MD5sum: e550d257329dce99cce8cefffc8790db NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: RELEASE_3_16 git_last_commit: 7ee1886 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/uSORT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/uSORT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/uSORT_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/uSORT_1.24.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 98 Package: VAExprs Version: 1.4.0 Depends: keras, mclust Imports: SingleCellExperiment, SummarizedExperiment, tensorflow, scater, CatEncoders, DeepPINCS, purrr, DiagrammeR, stats Suggests: SC3, knitr, testthat, reticulate, rmarkdown License: Artistic-2.0 MD5sum: 871839f6dd13bf7d7a6c2a9e9c4481be NeedsCompilation: no Title: Generating Samples of Gene Expression Data with Variational Autoencoders Description: A fundamental problem in biomedical research is the low number of observations, mostly due to a lack of available biosamples, prohibitive costs, or ethical reasons. By augmenting a few real observations with artificially generated samples, their analysis could lead to more robust and higher reproducible. One possible solution to the problem is the use of generative models, which are statistical models of data that attempt to capture the entire probability distribution from the observations. Using the variational autoencoder (VAE), a well-known deep generative model, this package is aimed to generate samples with gene expression data, especially for single-cell RNA-seq data. Furthermore, the VAE can use conditioning to produce specific cell types or subpopulations. The conditional VAE (CVAE) allows us to create targeted samples rather than completely random ones. biocViews: Software, GeneExpression, SingleCell Author: Dongmin Jung [cre, aut] () Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VAExprs git_branch: RELEASE_3_16 git_last_commit: a1eb3fd git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VAExprs_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VAExprs_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VAExprs_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VAExprs_1.4.0.tgz vignettes: vignettes/VAExprs/inst/doc/VAExprs.html vignetteTitles: VAExprs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VAExprs/inst/doc/VAExprs.R dependencyCount: 214 Package: VanillaICE Version: 1.60.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, human610quadv1bCrlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 MD5sum: 4426b6a80851eb5ae95a4d4913b681e8 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf [aut, cre] Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: RELEASE_3_16 git_last_commit: e9df26b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VanillaICE_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VanillaICE_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VanillaICE_1.60.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VanillaICE_1.60.0.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 85 Package: VarCon Version: 1.6.0 Depends: Biostrings, BSgenome, GenomicRanges, R (>= 4.1) Imports: methods, stats, IRanges, shiny, shinycssloaders, shinyFiles, ggplot2 Suggests: testthat, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: 10099480dde371330d458d120cc4ddcf NeedsCompilation: no Title: VarCon: an R package for retrieving neighboring nucleotides of an SNV Description: VarCon is an R package which converts the positional information from the annotation of an single nucleotide variation (SNV) (either referring to the coding sequence or the reference genomic sequence). It retrieves the genomic reference sequence around the position of the single nucleotide variation. To asses, whether the SNV could potentially influence binding of splicing regulatory proteins VarCon calcualtes the HEXplorer score as an estimation. Besides, VarCon additionally reports splice site strengths of splice sites within the retrieved genomic sequence and any changes due to the SNV. biocViews: FunctionalGenomics, AlternativeSplicing Author: Johannes Ptok [aut, cre] Maintainer: Johannes Ptok VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VarCon git_branch: RELEASE_3_16 git_last_commit: e5f2ffb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-02 source.ver: src/contrib/VarCon_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VarCon_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VarCon_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VarCon_1.6.0.tgz vignettes: vignettes/VarCon/inst/doc/VarCon.html vignetteTitles: Analysing SNVs with VarCon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VarCon/inst/doc/VarCon.R dependencyCount: 99 Package: variancePartition Version: 1.28.9 Depends: R (>= 4.0.0), ggplot2, limma, BiocParallel Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, Matrix (>= 1.4.0), iterators, foreach, doParallel, gplots, RhpcBLASctl, progress, reshape2, remaCor (>= 0.0.11), aod, scales, Rdpack, rlang, lme4 (>= 1.1-10), grDevices, graphics, Biobase, methods, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL-2 MD5sum: 16cb387e7335ea5feddc8832de909b0d NeedsCompilation: no Title: Quantify and interpret drivers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] Maintainer: Gabriel E. Hoffman URL: http://bioconductor.org/packages/variancePartition, https://DiseaseNeuroGenomics.github.io/variancePartition VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/variancePartition/issues git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_16 git_last_commit: 5b17a04 git_last_commit_date: 2023-03-15 Date/Publication: 2023-03-17 source.ver: src/contrib/variancePartition_1.28.9.tar.gz win.binary.ver: bin/windows/contrib/4.2/variancePartition_1.28.9.zip mac.binary.ver: bin/macosx/contrib/4.2/variancePartition_1.28.9.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/variancePartition_1.28.9.tgz vignettes: vignettes/variancePartition/inst/doc/variancePartition.pdf, vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html, vignettes/variancePartition/inst/doc/FAQ.html, vignettes/variancePartition/inst/doc/theory_practice_random_effects.html vignetteTitles: 1) Tutorial on using variancePartition, 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Frequently asked questions, 3) Theory and practice of random effects and REML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/FAQ.R, vignettes/variancePartition/inst/doc/theory_practice_random_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R importsMe: muscat, zenith suggestsMe: GRaNIE dependencyCount: 107 Package: VariantAnnotation Version: 1.44.1 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>= 1.99.0) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib (>= 1.99.3) Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 MD5sum: 4e4c9ebd75a47fb5eab735981d9a8e13 NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Bioconductor Package Maintainer [aut, cre], Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_16 git_last_commit: 2c52839 git_last_commit_date: 2023-02-14 Date/Publication: 2023-02-15 source.ver: src/contrib/VariantAnnotation_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantAnnotation_1.44.1.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantAnnotation_1.44.1.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VariantAnnotation_1.44.1.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, ensemblVEP, genotypeeval, HelloRanges, HTSeqGenie, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, seqCAT, signeR, SomaticSignatures, StructuralVariantAnnotation, svaNUMT, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector, PlasmaMutationDetector2 importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, cardelino, CNVfilteR, CopyNumberPlots, crisprDesign, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, epialleleR, fcScan, GA4GHclient, genbankr, GenomicFiles, GenVisR, ggbio, gmapR, gwascat, gwasurvivr, icetea, igvR, karyoploteR, katdetectr, lineagespot, MADSEQ, MMAPPR2, motifbreakR, MungeSumstats, musicatk, MutationalPatterns, ProteoDisco, scoreInvHap, SigsPack, SNPhood, svaRetro, TitanCNA, tLOH, TVTB, Uniquorn, VCFArray, YAPSA, COSMIC.67 suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CNVgears, CrispRVariants, GenomicDataCommons, GenomicRanges, GenomicScores, GWASTools, ldblock, omicsPrint, podkat, RVS, SeqArray, splatter, supersigs, systemPipeR, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD, SNPassoc, updog dependencyCount: 97 Package: VariantExperiment Version: 1.12.0 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, Imports: GDSArray (>= 1.11.1), DelayedDataFrame (>= 1.6.0), tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, SeqVarTools, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr, rmarkdown, markdown License: GPL-3 Archs: x64 MD5sum: 7e60243f562e405f96d047f7c6b439cb NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: RELEASE_3_16 git_last_commit: 94a91ff git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VariantExperiment_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantExperiment_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantExperiment_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VariantExperiment_1.12.0.tgz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html vignetteTitles: VariantExperiment-class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R dependencyCount: 68 Package: VariantFiltering Version: 1.34.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 MD5sum: 55c850e8503bfc0043f68ccf3f56072a NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues git_url: https://git.bioconductor.org/packages/VariantFiltering git_branch: RELEASE_3_16 git_last_commit: 0a6a94d git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VariantFiltering_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantFiltering_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantFiltering_1.34.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VariantFiltering_1.34.0.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R dependencyCount: 176 Package: VariantTools Version: 1.40.0 Depends: R (>= 3.5.0), S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3) License: Artistic-2.0 Archs: x64 MD5sum: 5efec062901de7c8c99b1973c2f96234 NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: RELEASE_3_16 git_last_commit: d949a03 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VariantTools_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VariantTools_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VariantTools_1.39.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VariantTools_1.40.0.tgz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie, MMAPPR2 suggestsMe: VariantToolsData dependencyCount: 98 Package: VaSP Version: 1.10.0 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) MD5sum: 88de32ec8405843ff7a49e58f64a70d4 NeedsCompilation: no Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.) biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu [aut, cre] (), Qian Du [aut] (), Chi Zhang [aut] () Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/VaSP VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/VaSP/issues git_url: https://git.bioconductor.org/packages/VaSP git_branch: RELEASE_3_16 git_last_commit: 5b08a6b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VaSP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VaSP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VaSP_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VaSP_1.10.0.tgz vignettes: vignettes/VaSP/inst/doc/VaSP.html vignetteTitles: user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VaSP/inst/doc/VaSP.R dependencyCount: 87 Package: vbmp Version: 1.66.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 07fb72295810b9ae2b9f5ae8ad2148bb NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: RELEASE_3_16 git_last_commit: 2040031 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vbmp_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vbmp_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vbmp_1.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vbmp_1.66.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.14.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 MD5sum: ac185a1a7e3fd02704d6fb0e2a55a0ea NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: RELEASE_3_16 git_last_commit: f992261 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VCFArray_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VCFArray_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VCFArray_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VCFArray_1.14.0.tgz vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 99 Package: VDJdive Version: 1.0.0 Depends: R (>= 4.2) Imports: basilisk, BiocParallel, cowplot, ggplot2, gridExtra, IRanges, Matrix, methods, RColorBrewer, reticulate, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: breakaway, covr, knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 9db24834afe1f1015c3f201453907523 NeedsCompilation: no Title: Analysis Tools for 10X V(D)J Data Description: This package provides functions for handling and analyzing immune receptor repertoire data, such as produced by the CellRanger V(D)J pipeline. This includes reading the data into R, merging it with paired single-cell data, quantifying clonotype abundances, calculating diversity metrics, and producing common plots. It implements the E-M Algorithm for clonotype assignment, along with other methods, which makes use of ambiguous cells for improved quantification. biocViews: Software, ImmunoOncology, SingleCell, Annotation, RNASeq, TargetedResequencing Author: Kelly Street [aut, cre] (), Mercedeh Movassagh [aut] (), Jill Lundell [aut] (), Jared Brown [ctb], Linglin Huang [ctb] Maintainer: Kelly Street URL: https://github.com/kstreet13/VDJdive VignetteBuilder: knitr BugReports: https://github.com/kstreet13/VDJdive/issues git_url: https://git.bioconductor.org/packages/VDJdive git_branch: RELEASE_3_16 git_last_commit: c9444f0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VDJdive_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VDJdive_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VDJdive_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VDJdive_1.0.0.tgz vignettes: vignettes/VDJdive/inst/doc/workflow.html vignetteTitles: VDJdive Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VDJdive/inst/doc/workflow.R dependencyCount: 77 Package: VegaMC Version: 3.36.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods License: GPL-2 MD5sum: 3a0cc3e67f2de4bb8fa8bbcd901f5913 NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: RELEASE_3_16 git_last_commit: 867716a git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VegaMC_3.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VegaMC_3.36.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VegaMC_3.36.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VegaMC_3.36.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 70 Package: velociraptor Version: 1.8.0 Depends: SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors, DelayedArray, basilisk, zellkonverter, scuttle, SingleCellExperiment, BiocParallel, BiocSingular Suggests: BiocStyle, testthat, knitr, rmarkdown, pkgdown, scran, scater, scRNAseq, Rtsne, graphics, grDevices, ggplot2, cowplot, GGally, patchwork, metR License: MIT + file LICENSE Archs: x64 MD5sum: 8658f92ea21c3bfdf30d3e51146c132b NeedsCompilation: no Title: Toolkit for Single-Cell Velocity Description: This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. biocViews: SingleCell, GeneExpression, Sequencing, Coverage Author: Kevin Rue-Albrecht [aut, cre] (), Aaron Lun [aut] (), Charlotte Soneson [aut] (), Michael Stadler [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/velociraptor VignetteBuilder: knitr BugReports: https://github.com/kevinrue/velociraptor/issues git_url: https://git.bioconductor.org/packages/velociraptor git_branch: RELEASE_3_16 git_last_commit: 97efe6b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/velociraptor_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/velociraptor_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/velociraptor_1.8.0.tgz vignettes: vignettes/velociraptor/inst/doc/velociraptor.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/velociraptor/inst/doc/velociraptor.R dependsOnMe: OSCA.advanced dependencyCount: 59 Package: veloviz Version: 1.4.0 Depends: R (>= 4.1) Imports: Rcpp, Matrix, igraph, mgcv, RSpectra, grDevices, graphics, stats LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 34fea8e5fabb1e0ac1267834fdd5d357 NeedsCompilation: yes Title: VeloViz: RNA-velocity informed 2D embeddings for visualizing cell state trajectories Description: VeloViz uses each cell’s current observed and predicted future transcriptional states inferred from RNA velocity analysis to build a nearest neighbor graph between cells in the population. Edges are then pruned based on a cosine correlation threshold and/or a distance threshold and the resulting graph is visualized using a force-directed graph layout algorithm. VeloViz can help ensure that relationships between cell states are reflected in the 2D embedding, allowing for more reliable representation of underlying cellular trajectories. biocViews: Transcriptomics, Visualization, GeneExpression, Sequencing, RNASeq, DimensionReduction Author: Lyla Atta [aut, cre] (), Jean Fan [aut] (), Arpan Sahoo [aut] () Maintainer: Lyla Atta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/veloviz git_branch: RELEASE_3_16 git_last_commit: 7442372 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/veloviz_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/veloviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/veloviz_1.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/veloviz_1.4.0.tgz vignettes: vignettes/veloviz/inst/doc/vignette.html vignetteTitles: Visualizing cell cycle trajectory in MERFISH data using VeloViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/veloviz/inst/doc/vignette.R dependencyCount: 19 Package: VennDetail Version: 1.14.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat, markdown License: GPL-2 Archs: x64 MD5sum: 34805acc261d083cd0ea4c499810738d NeedsCompilation: no Title: A package for visualization and extract details Description: A set of functions to generate high-resolution Venn,Vennpie plot,extract and combine details of these subsets with user datasets in data frame is available. biocViews: DataRepresentation,GraphAndNetwork Author: Kai Guo, Brett McGregor Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VennDetail git_branch: RELEASE_3_16 git_last_commit: ada2886 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VennDetail_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VennDetail_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VennDetail_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VennDetail_1.14.0.tgz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 48 Package: VERSO Version: 1.8.0 Depends: R (>= 4.1.0) Imports: ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: 81acd8bbcb8947ae40608ffecdc24540 NeedsCompilation: no Title: Viral Evolution ReconStructiOn (VERSO) Description: Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1. biocViews: BiomedicalInformatics, Sequencing, SomaticMutation Author: Daniele Ramazzotti [aut] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [aut] () Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/VERSO VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/VERSO git_url: https://git.bioconductor.org/packages/VERSO git_branch: RELEASE_3_16 git_last_commit: 48a06dc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VERSO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VERSO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VERSO_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VERSO_1.8.0.tgz vignettes: vignettes/VERSO/inst/doc/vignette.pdf vignetteTitles: VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/vignette.R dependencyCount: 16 Package: vidger Version: 1.18.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: a10b883b26782abcb2fc1a6516cbf09c NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: RELEASE_3_16 git_last_commit: dfad737 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vidger_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vidger_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vidger_1.18.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vidger_1.18.0.tgz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 125 Package: viper Version: 1.32.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: a666dce0810919aabec56c795813f179 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: RELEASE_3_16 git_last_commit: fdbc0ea git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/viper_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/viper_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/viper_1.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/viper_1.32.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan, aracne.networks importsMe: diggit, RTN, diggitdata, dorothea suggestsMe: decoupleR, MethReg, MOMA dependencyCount: 90 Package: ViSEAGO Version: 1.12.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt, dendextend, DiagrammeR, DT, dynamicTreeCut, fgsea, GOSemSim, ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph, methods, plotly, processx, topGO, RColorBrewer, R.utils, scales, stats, UpSetR, utils Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager License: GPL-3 MD5sum: fd75bb102de3cad17c566ab7cb5adb6e NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html, https://forgemia.inra.fr/UMR-BOA/ViSEAGO VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: RELEASE_3_16 git_last_commit: 12dddc3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/ViSEAGO_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/ViSEAGO_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.2/ViSEAGO_1.12.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/ViSEAGO_1.12.0.tgz vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 167 Package: vissE Version: 1.6.0 Depends: R (>= 4.1) Imports: igraph, methods, plyr, ggplot2, scico, RColorBrewer, tm, ggwordcloud, GSEABase, reshape2, grDevices, ggforce, msigdb, ggrepel, textstem, tidygraph, stats, scales, ggraph Suggests: testthat, org.Hs.eg.db, org.Mm.eg.db, patchwork, singscore, knitr, rmarkdown, prettydoc, BiocStyle, pkgdown, covr License: GPL-3 MD5sum: 928d3a574805cb86456f89cfc7d46c9b NeedsCompilation: no Title: Visualising Set Enrichment Analysis Results Description: This package enables the interpretation and analysis of results from a gene set enrichment analysis using network-based and text-mining approaches. Most enrichment analyses result in large lists of significant gene sets that are difficult to interpret. Tools in this package help build a similarity-based network of significant gene sets from a gene set enrichment analysis that can then be investigated for their biological function using text-mining approaches. biocViews: Software, GeneExpression, GeneSetEnrichment, NetworkEnrichment, Network Author: Dharmesh D. Bhuva [aut, cre] (), Ahmed Mohamed [ctb] Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/vissE VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/vissE/issues git_url: https://git.bioconductor.org/packages/vissE git_branch: RELEASE_3_16 git_last_commit: 6ebddf9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vissE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vissE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vissE_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vissE_1.6.0.tgz vignettes: vignettes/vissE/inst/doc/vissE.html vignetteTitles: vissE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vissE/inst/doc/vissE.R suggestsMe: msigdb dependencyCount: 165 Package: Voyager Version: 1.0.10 Depends: R (>= 4.2.0) Imports: BiocParallel, bluster, ggnewscale, ggplot2 (>= 3.4.0), Matrix, methods, patchwork, rlang, S4Vectors, scales, scico, sf, SingleCellExperiment, SpatialExperiment, SpatialFeatureExperiment, spdep, stats, SummarizedExperiment Suggests: BiocSingular, BiocStyle, cowplot, dbscan, ExperimentHub, hexbin, knitr, rmarkdown, scater, scattermore, scran, SFEData, sparseMatrixStats, testthat (>= 3.0.0), vdiffr License: Artistic-2.0 MD5sum: 9e85d0e2a442622a464b3618549d535f NeedsCompilation: no Title: From geospatial to spatial omics Description: SpatialFeatureExperiment (SFE) is a new S4 class for working with spatial single-cell genomics data. The voyager package implements basic exploratory spatial data analysis (ESDA) methods for SFE. This first version supports univariate global spatial ESDA methods such as Moran's I, permutation testing for Moran's I, and correlograms. The Voyager package also implements plotting functions to plot SFE data and ESDA results. Multivariate ESDA and univariate local metrics will be added in later versions. biocViews: GeneExpression, Spatial, Transcriptomics, Visualization Author: Lambda Moses [aut, cre] (), Kayla Jackson [aut] (), Lior Pachter [aut, rev] () Maintainer: Lambda Moses URL: https://github.com/pachterlab/voyager VignetteBuilder: knitr BugReports: https://github.com/pachterlab/voyager/issues git_url: https://git.bioconductor.org/packages/Voyager git_branch: RELEASE_3_16 git_last_commit: 5d516fc git_last_commit_date: 2023-02-22 Date/Publication: 2023-02-23 source.ver: src/contrib/Voyager_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/4.2/Voyager_1.0.10.zip mac.binary.ver: bin/macosx/contrib/4.2/Voyager_1.0.10.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Voyager_1.0.10.tgz vignettes: vignettes/Voyager/inst/doc/overview.html vignetteTitles: Functionality overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Voyager/inst/doc/overview.R dependencyCount: 134 Package: VplotR Version: 1.8.0 Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2 Imports: cowplot, magrittr, GenomeInfoDb, GenomicAlignments, RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2, methods, graphics, stats Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 MD5sum: 240a00c615f71e1d632fcabbd94545dc NeedsCompilation: no Title: Set of tools to make V-plots and compute footprint profiles Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. biocViews: NucleosomePositioning, Coverage, Sequencing, BiologicalQuestion, ATACSeq, Alignment Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/VplotR VignetteBuilder: knitr BugReports: https://github.com/js2264/VplotR/issues git_url: https://git.bioconductor.org/packages/VplotR git_branch: RELEASE_3_16 git_last_commit: ec8d656 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/VplotR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/VplotR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/VplotR_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/VplotR_1.8.0.tgz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 74 Package: vsclust Version: 1.0.0 Depends: R (>= 4.2.0) Imports: matrixStats, limma, parallel, shiny, qvalue, grDevices, stats, MultiAssayExperiment, graphics LinkingTo: Rcpp Suggests: knitr, yaml, testthat (>= 3.0.0), rmarkdown, BiocStyle, clusterProfiler License: GPL-2 MD5sum: a554079b30509a0df33c6af585d23189 NeedsCompilation: yes Title: Feature-based variance-sensitive quantitative clustering Description: Feature-based variance-sensitive clustering of omics data. Optimizes cluster assignment by taking into account individual feature variance. Includes several modules for statistical testing, clustering and enrichment analysis. biocViews: Clustering, Annotation, PrincipalComponent, DifferentialExpression, Visualization, Proteomics, Metabolomics Author: Veit Schwaemmle [aut, cre] Maintainer: Veit Schwaemmle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsclust git_branch: RELEASE_3_16 git_last_commit: 53f16ce git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vsclust_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vsclust_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vsclust_1.0.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vsclust_1.0.0.tgz vignettes: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.html, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.html vignetteTitles: VSClust on Bioconductor object, VSClust workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vsclust/inst/doc/Integrate_With_Bioconductor_Objects.R, vignettes/vsclust/inst/doc/Run_VSClust_Workflow.R dependencyCount: 91 Package: vsn Version: 3.66.0 Depends: R (>= 4.0.0), methods, Biobase Imports: affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, rmarkdown, dplyr, testthat License: Artistic-2.0 Archs: x64 MD5sum: b33b39a6724d51e90479f474e1d4d22d NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities from single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsn git_branch: RELEASE_3_16 git_last_commit: ddccd6c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vsn_3.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vsn_3.66.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vsn_3.66.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vsn_3.66.0.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: cellHTS2, webbioc, rnaseqGene importsMe: arrayQualityMetrics, bnem, coexnet, DEP, Doscheda, imageHTS, MatrixQCvis, metaseqR2, MSnbase, NormalyzerDE, pvca, Ringo, tilingArray, ExpressionNormalizationWorkflow, RNAseqQC suggestsMe: adSplit, beadarray, DAPAR, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, qmtools, scp, twilight, estrogen, wrMisc dependencyCount: 43 Package: vtpnet Version: 0.38.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 Archs: x64 MD5sum: 0d5870c5466fa6dc389417808d058bab NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: RELEASE_3_16 git_last_commit: a0b6d4b git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vtpnet_0.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vtpnet_0.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vtpnet_0.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vtpnet_0.38.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 140 Package: vulcan Version: 1.20.0 Depends: R (>= 4.0), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq2, Biobase Suggests: vulcandata License: LGPL-3 MD5sum: a47f0db9f3c3a4d7a9335194b498b4bf NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi git_url: https://git.bioconductor.org/packages/vulcan git_branch: RELEASE_3_16 git_last_commit: 5c45df0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/vulcan_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/vulcan_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/vulcan_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/vulcan_1.20.0.tgz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R dependencyCount: 196 Package: waddR Version: 1.12.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), eva, BiocFileCache, BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE MD5sum: dc7d7fe2bdadbfbc09f35577b01b53ce NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: RELEASE_3_16 git_last_commit: 06e7696 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/waddR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/waddR_1.11.0.zip mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/waddR_1.12.0.tgz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 120 Package: wateRmelon Version: 2.4.0 Depends: R (>= 3.5.0), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BiocStyle, knitr, rmarkdown, IlluminaHumanMethylationEPICmanifest, irlba, FlowSorted.Blood.EPIC, FlowSorted.Blood.450k, preprocessCore Enhances: minfi License: GPL-3 Archs: x64 MD5sum: 80eea74751dc0e3314c5e5a44224f7be NeedsCompilation: no Title: Illumina 450 and EPIC methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leo C Schalkwyk [cre, aut], Tyler J Gorrie-Stone [aut], Ruth Pidsley [aut], Chloe CY Wong [aut], Nizar Touleimat [ctb], Matthieu Defrance [ctb], Andrew Teschendorff [ctb], Jovana Maksimovic [ctb], Louis Y El Khoury [ctb], Yucheng Wang [ctb] Maintainer: Leo C Schalkwyk VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_16 git_last_commit: 31c1525 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/wateRmelon_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wateRmelon_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wateRmelon_2.4.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/wateRmelon_2.4.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.html vignetteTitles: wateRmelon User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 167 Package: wavClusteR Version: 2.32.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: e6dcd9461e28235f6fb596d78b163824 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: RELEASE_3_16 git_last_commit: 2c0d0d5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/wavClusteR_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wavClusteR_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wavClusteR_2.32.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/wavClusteR_2.32.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R dependencyCount: 148 Package: weaver Version: 1.64.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 5212a28f7b5653089a1bb4d42aae5cf4 NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: RELEASE_3_16 git_last_commit: 56e0a95 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/weaver_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/weaver_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.2/weaver_1.64.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/weaver_1.64.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R dependencyCount: 4 Package: webbioc Version: 1.70.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: a0e27956105e9032cc113005b5bf24a4 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: RELEASE_3_16 git_last_commit: f755971 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/webbioc_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/webbioc_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.2/webbioc_1.70.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/webbioc_1.70.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 86 Package: weitrix Version: 1.10.0 Depends: R (>= 3.6), SummarizedExperiment Imports: methods, utils, stats, grDevices, assertthat, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics, limma, topconfects, dplyr, purrr, ggplot2, rlang, scales, reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR, EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi, ComplexHeatmap, patchwork, testthat (>= 2.1.0) License: LGPL-2.1 | file LICENSE MD5sum: ab7da6c21916674514d701df1b6e4cb2 NeedsCompilation: no Title: Tools for matrices with precision weights, test and explore weighted or sparse data Description: Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported. biocViews: Software, DataRepresentation, DimensionReduction, GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: RELEASE_3_16 git_last_commit: 80ac6b7 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/weitrix_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/weitrix_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/weitrix_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/weitrix_1.10.0.tgz vignettes: vignettes/weitrix/inst/doc/V1_overview.html, vignettes/weitrix/inst/doc/V2_tail_length.html, vignettes/weitrix/inst/doc/V3_shift.html, vignettes/weitrix/inst/doc/V4_airway.html, vignettes/weitrix/inst/doc/V5_slam_seq.html vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail length example, 3. Alternative polyadenylation, 4. RNA-Seq expression example, 5. Proportions data example with SLAM-Seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R, vignettes/weitrix/inst/doc/V3_shift.R, vignettes/weitrix/inst/doc/V4_airway.R, vignettes/weitrix/inst/doc/V5_slam_seq.R dependencyCount: 81 Package: widgetTools Version: 1.76.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 2965b9aa8d0d346be30b38a114e263d9 NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: RELEASE_3_16 git_last_commit: 728bc56 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/widgetTools_1.76.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/widgetTools_1.76.0.zip mac.binary.ver: bin/macosx/contrib/4.2/widgetTools_1.76.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/widgetTools_1.76.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui, SeqFeatR suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.22.0 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 MD5sum: 0a62d512339c8535aefee663271a5552 NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: RELEASE_3_16 git_last_commit: 69bafbe git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/wiggleplotr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wiggleplotr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wiggleplotr_1.22.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/wiggleplotr_1.22.0.tgz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R importsMe: factR suggestsMe: MARVEL dependencyCount: 79 Package: wpm Version: 1.8.0 Depends: R (>= 4.1.0) Imports: utils, methods, cli, Biobase, SummarizedExperiment, config, golem, shiny, DT, ggplot2, dplyr, rlang, stringr, shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer, logging Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 88d7f95109b7eb3a4e92e1c8107bec9d NeedsCompilation: no Title: Well Plate Maker Description: The Well-Plate Maker (WPM) is a shiny application deployed as an R package. Functions for a command-line/script use are also available. The WPM allows users to generate well plate maps to carry out their experiments while improving the handling of batch effects. In particular, it helps controlling the "plate effect" thanks to its ability to randomize samples over multiple well plates. The algorithm for placing the samples is inspired by the backtracking algorithm: the samples are placed at random while respecting specific spatial constraints. biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect, ExperimentalDesign Author: Helene Borges [aut, cre], Thomas Burger [aut] Maintainer: Helene Borges URL: https://github.com/HelBor/wpm, https://bioconductor.org/packages/release/bioc/html/wpm.html VignetteBuilder: knitr BugReports: https://github.com/HelBor/wpm/issues git_url: https://git.bioconductor.org/packages/wpm git_branch: RELEASE_3_16 git_last_commit: d31e5e2 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/wpm_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wpm_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wpm_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/wpm_1.8.0.tgz vignettes: vignettes/wpm/inst/doc/wpm_vignette.html vignetteTitles: How to use Well Plate Maker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R dependencyCount: 103 Package: wppi Version: 1.6.0 Depends: R(>= 4.1) Imports: dplyr, igraph, logger, methods, magrittr, Matrix, OmnipathR(>= 2.99.8), progress, purrr, rlang, RCurl, stats, tibble, tidyr Suggests: knitr, testthat, rmarkdown License: MIT + file LICENSE MD5sum: 1f4c59fa6174cda720da11cbeed53b8e NeedsCompilation: no Title: Weighting protein-protein interactions Description: Protein-protein interaction data is essential for omics data analysis and modeling. Database knowledge is general, not specific for cell type, physiological condition or any other context determining which connections are functional and contribute to the signaling. Functional annotations such as Gene Ontology and Human Phenotype Ontology might help to evaluate the relevance of interactions. This package predicts functional relevance of protein-protein interactions based on functional annotations such as Human Protein Ontology and Gene Ontology, and prioritizes genes based on network topology, functional scores and a path search algorithm. biocViews: GraphAndNetwork, Network, Pathways, Software, GeneSignaling, GeneTarget, SystemsBiology, Transcriptomics, Annotation Author: Ana Galhoz [cre, aut] (), Denes Turei [aut] (), Michael P. Menden [aut] (), Albert Krewinkel [ctb, cph] (pagebreak Lua filter) Maintainer: Ana Galhoz URL: https://github.com/AnaGalhoz37/wppi VignetteBuilder: knitr BugReports: https://github.com/AnaGalhoz37/wppi/issues git_url: https://git.bioconductor.org/packages/wppi git_branch: RELEASE_3_16 git_last_commit: cd4b58e git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/wppi_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/wppi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/wppi_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/wppi_1.6.0.tgz vignettes: vignettes/wppi/inst/doc/wppi_workflow.html vignetteTitles: WPPI workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/wppi/inst/doc/wppi_workflow.R dependencyCount: 82 Package: Wrench Version: 1.16.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: fd3764fc865672c03c838108d6f5f263 NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: RELEASE_3_16 git_last_commit: 4bb1bd5 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Wrench_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Wrench_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Wrench_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Wrench_1.16.0.tgz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq dependencyCount: 10 Package: xcms Version: 3.20.0 Depends: R (>= 4.0.0), BiocParallel (>= 1.8.0), MSnbase (>= 2.21.4) Imports: mzR (>= 2.25.3), methods, Biobase, BiocGenerics, ProtGenerics (>= 1.25.1), lattice, RColorBrewer, plyr, RANN, MassSpecWavelet (>= 1.5.2), S4Vectors, robustbase, IRanges, SummarizedExperiment, MsCoreUtils, MsFeatures Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, testthat, pander, magrittr, rmarkdown, multtest, MALDIquant, pheatmap, Spectra (>= 1.1.17), MsBackendMgf, progress, signal Enhances: Rgraphviz, rgl, XML License: GPL (>= 2) + file LICENSE MD5sum: 9bf832c553d287235f84417daf1aa2e7 NeedsCompilation: yes Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith [ctb], Ralf Tautenhahn [ctb], Steffen Neumann [aut, cre] (), Paul Benton [ctb], Christopher Conley [ctb], Johannes Rainer [ctb] (), Michael Witting [ctb], William Kumler [ctb] () Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new git_url: https://git.bioconductor.org/packages/xcms git_branch: RELEASE_3_16 git_last_commit: 899f366 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/xcms_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/xcms_3.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/xcms_3.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/xcms_3.20.0.tgz vignettes: vignettes/xcms/inst/doc/LC-MS-feature-grouping.html, vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: LC-MS feature grouping, Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LCMS data preprocessing and analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/LC-MS-feature-grouping.R, vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms-lcms-ms.R, vignettes/xcms/inst/doc/xcms.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, ncGTW, proFIA, faahKO, PtH2O2lipids importsMe: CAMERA, cliqueMS, cosmiq, MobilityTransformR, Risa, LCMSQA suggestsMe: CluMSID, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, isatabr, MetabolomicsBasics, RAMClustR dependencyCount: 92 Package: xcore Version: 1.2.0 Depends: R (>= 4.2) Imports: DelayedArray (>= 0.18.0), edgeR (>= 3.34.1), foreach (>= 1.5.1), GenomicRanges (>= 1.44.0), glmnet (>= 4.1.2), IRanges (>= 2.26.0), iterators (>= 1.0.13), magrittr (>= 2.0.1), Matrix (>= 1.3.4), methods (>= 4.1.1), MultiAssayExperiment (>= 1.18.0), stats, S4Vectors (>= 0.30.0), utils Suggests: AnnotationHub (>= 3.0.2), BiocGenerics (>= 0.38.0), BiocParallel (>= 1.28), BiocStyle (>= 2.20.2), data.table (>= 1.14.0), devtools (>= 2.4.2), doParallel (>= 1.0.16), ExperimentHub (>= 2.2.0), knitr (>= 1.37), pheatmap (>= 1.0.12), proxy (>= 0.4.26), ridge (>= 3.0), rmarkdown (>= 2.11), rtracklayer (>= 1.52.0), testthat (>= 3.0.0), usethis (>= 2.0.1), xcoredata License: GPL-2 Archs: x64 MD5sum: 4eff542ecdc60f951bdad3064aa977b1 NeedsCompilation: no Title: xcore expression regulators inference Description: xcore is an R package for transcription factor activity modeling based on known molecular signatures and user's gene expression data. Accompanying xcoredata package provides a collection of molecular signatures, constructed from publicly available ChiP-seq experiments. xcore use ridge regression to model changes in expression as a linear combination of molecular signatures and find their unknown activities. Obtained, estimates can be further tested for significance to select molecular signatures with the highest predicted effect on the observed expression changes. biocViews: GeneExpression, GeneRegulation, Epigenetics, Regression, Sequencing Author: Maciej Migdał [aut, cre] (), Bogumił Kaczkowski [aut] () Maintainer: Maciej Migdał VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/xcore git_branch: RELEASE_3_16 git_last_commit: ec0fc51 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/xcore_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/xcore_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.2/xcore_1.2.0.tgz vignettes: vignettes/xcore/inst/doc/xcore_vignette.html vignetteTitles: xcore vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xcore/inst/doc/xcore_vignette.R suggestsMe: xcoredata dependencyCount: 60 Package: XDE Version: 2.44.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 MD5sum: e6790ad1ba031d70e124e5171c3f2211 NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: RELEASE_3_16 git_last_commit: a6ddedb git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/XDE_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XDE_2.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XDE_2.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/XDE_2.44.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 63 Package: Xeva Version: 1.14.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 Archs: x64 MD5sum: f7d2cb1d722ce8e8d5362f6cb028c4b1 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: Contains set of functions to perform analysis of patient-derived xenograft (PDX) data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: RELEASE_3_16 git_last_commit: 06819ae git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/Xeva_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/Xeva_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.2/Xeva_1.14.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/Xeva_1.14.0.tgz vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 161 Package: XINA Version: 1.16.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 50ff4a615fd8312da7035da4f9b70193 NeedsCompilation: no Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis Description: The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. biocViews: SystemsBiology, Proteomics, RNASeq, Network Author: Lang Ho Lee and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: RELEASE_3_16 git_last_commit: c6595c9 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/XINA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XINA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XINA_1.16.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/XINA_1.16.0.tgz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 66 Package: xmapbridge Version: 1.56.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 5ee17bb06422d3a3305879283372f926 NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: RELEASE_3_16 git_last_commit: fdf2caf git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/xmapbridge_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/xmapbridge_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.2/xmapbridge_1.56.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/xmapbridge_1.56.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: XNAString Version: 1.6.0 Depends: R (>= 4.1) Imports: utils, Biostrings, BSgenome, data.table, GenomicRanges, IRanges, methods, Rcpp, stringi, S4Vectors, future.apply, stringr, formattable, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, markdown, testthat, BSgenome.Hsapiens.UCSC.hg38, pander License: GPL-2 MD5sum: 5b98e220fe8cfb204c0615e28883d498 NeedsCompilation: yes Title: Efficient Manipulation of Modified Oligonucleotide Sequences Description: The XNAString package allows for description of base sequences and associated chemical modifications in a single object. XNAString is able to capture single stranded, as well as double stranded molecules. Chemical modifications are represented as independent strings associated with different features of the molecules (base sequence, sugar sequence, backbone sequence, modifications) and can be read or written to a HELM notation. It also enables secondary structure prediction using RNAfold from ViennaRNA. XNAString is designed to be efficient representation of nucleic-acid based therapeutics, therefore it stores information about target sequences and provides interface for matching and alignment functions from Biostrings package. biocViews: SequenceMatching, Alignment, Sequencing, Genetics Author: Anna Górska [aut], Marianna Plucinska [aut, cre], Lykke Pedersen [aut], Lukasz Kielpinski [aut], Disa Tehler [aut], Peter H. Hagedorn [aut] Maintainer: Marianna Plucinska VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XNAString git_branch: RELEASE_3_16 git_last_commit: e838451 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/XNAString_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XNAString_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XNAString_1.6.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/XNAString_1.6.0.tgz vignettes: vignettes/XNAString/inst/doc/XNAString_vignette.html vignetteTitles: XNAString classes and functionalities hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/XNAString/inst/doc/XNAString_vignette.R dependencyCount: 86 Package: XVector Version: 0.38.0 Depends: R (>= 4.0.0), methods, BiocGenerics (>= 0.37.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9) Imports: methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: x64 MD5sum: 5a4445f2351e83d7d558449672465a02 NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/XVector BugReports: https://github.com/Bioconductor/XVector/issues git_url: https://git.bioconductor.org/packages/XVector git_branch: RELEASE_3_16 git_last_commit: 8cad084 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/XVector_0.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/XVector_0.38.0.zip mac.binary.ver: bin/macosx/contrib/4.2/XVector_0.38.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/XVector_0.38.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, crisprScore, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, monaLisa, ProteoDisco, R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation, simMP suggestsMe: IRanges, musicatk linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 10 Package: yamss Version: 1.24.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 Archs: x64 MD5sum: 2da0e264f86e6a2c11b82b0678271d9d NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. biocViews: MassSpectrometry, Metabolomics, ImmunoOncology, Software Author: Leslie Myint [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: RELEASE_3_16 git_last_commit: 01b86bc git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/yamss_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/yamss_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/yamss_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/yamss_1.24.0.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 71 Package: YAPSA Version: 1.24.0 Depends: R (>= 3.6.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, PMCMRplus, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: d4a9864b6719e1de991f5a3b69fc2d22 NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann [aut, cre], Lea Jopp-Saile [aut], Carolin Andresen [aut], Zuguang Gu [aut], Matthias Schlesner [aut] Maintainer: Daniel Huebschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_16 git_last_commit: 68d1c9c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/YAPSA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/YAPSA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/YAPSA_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/YAPSA_1.24.0.tgz vignettes: vignettes/YAPSA/inst/doc/index.html, vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html, vignettes/YAPSA/inst/doc/vignette_exomes.html, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html, vignettes/YAPSA/inst/doc/vignettes_Indel.html, vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: index.html, 3. Confidence Intervals, 6. Usage of YAPSA for WES data, 2. Signature-specific cutoffs, 4. Stratified Analysis of Mutational Signatures, 5. Indel signature analysis, 1. Usage of YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R, vignettes/YAPSA/inst/doc/vignette_exomes.R, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R, vignettes/YAPSA/inst/doc/vignettes_Indel.R, vignettes/YAPSA/inst/doc/YAPSA.R dependencyCount: 193 Package: yarn Version: 1.24.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 MD5sum: 7892c395b701000bd7e5e5285dd66409 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: RELEASE_3_16 git_last_commit: d3a9c5c git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/yarn_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/yarn_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.2/yarn_1.24.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/yarn_1.24.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependsOnMe: netZooR dependencyCount: 158 Package: zellkonverter Version: 1.8.0 Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, DelayedArray, methods, S4Vectors, utils, cli Suggests: anndata, BiocFileCache, BiocStyle, covr, HDF5Array, knitr, pkgload, rmarkdown, rhdf5, scRNAseq, spelling, testthat, withr License: MIT + file LICENSE MD5sum: 71f3a2d185087edb14baa34f990a5892 NeedsCompilation: no Title: Conversion Between scRNA-seq Objects Description: Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk. biocViews: SingleCell, DataImport, DataRepresentation Author: Luke Zappia [aut, cre] (), Aaron Lun [aut] () Maintainer: Luke Zappia URL: https://github.com/theislab/zellkonverter VignetteBuilder: knitr BugReports: https://github.com/theislab/zellkonverter/issues git_url: https://git.bioconductor.org/packages/zellkonverter git_branch: RELEASE_3_16 git_last_commit: 0d157f3 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/zellkonverter_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zellkonverter_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zellkonverter_1.8.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/zellkonverter_1.8.0.tgz vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html vignetteTitles: Converting to/from AnnData to SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R dependsOnMe: OSCA.intro importsMe: velociraptor suggestsMe: cellxgenedp, HDF5Array dependencyCount: 41 Package: zenith Version: 1.0.2 Depends: R (>= 4.2.0), methods Imports: variancePartition (>= 1.26.0), limma, EnrichmentBrowser (>= 2.22.0), GSEABase (>= 1.54.0), msigdbr (>= 7.5.1), Rfast, ggplot2, tidyr, reshape2, progress, utils, Rdpack, stats Suggests: BiocStyle, BiocGenerics, knitr, pander, rmarkdown, tweeDEseqCountData, edgeR, kableExtra, RUnit License: Artistic-2.0 MD5sum: a4a3ea32743ae6b681c2af19ebf71693 NeedsCompilation: no Title: Gene set analysis following differential expression using linear (mixed) modeling with dream Description: Zenith performs gene set analysis on the result of differential expression using linear (mixed) modeling with dream by considering the correlation between gene expression traits. This package implements the camera method from the limma package proposed by Wu and Smyth (2012). Zenith is a simple extension of camera to be compatible with linear mixed models implemented in variancePartition::dream(). biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel Hoffman [aut, cre] Maintainer: Gabriel Hoffman URL: https://DiseaseNeuroGenomics.github.io/zenith VignetteBuilder: knitr BugReports: https://github.com/DiseaseNeuroGenomics/zenith/issues git_url: https://git.bioconductor.org/packages/zenith git_branch: RELEASE_3_16 git_last_commit: 8860881 git_last_commit_date: 2023-03-08 Date/Publication: 2023-03-08 source.ver: src/contrib/zenith_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.2/zenith_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.2/zenith_1.0.2.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/zenith_1.0.2.tgz vignettes: vignettes/zenith/inst/doc/loading_genesets.html, vignettes/zenith/inst/doc/zenith.html vignetteTitles: Example usage of zenith on GEUVAIDIS RNA-seq, Example usage of zenith on RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zenith/inst/doc/loading_genesets.R, vignettes/zenith/inst/doc/zenith.R dependencyCount: 169 Package: zFPKM Version: 1.20.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 688763d995e61d7e1e1e70f74efe8774 NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: RELEASE_3_16 git_last_commit: 5ff74e0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/zFPKM_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zFPKM_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zFPKM_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/zFPKM_1.20.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R suggestsMe: DGEobj.utils dependencyCount: 62 Package: zinbwave Version: 1.20.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2 License: Artistic-2.0 MD5sum: c9bd83da434b47fc7c0ebf87a6ea78e1 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: RELEASE_3_16 git_last_commit: e1ee417 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/zinbwave_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zinbwave_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zinbwave_1.20.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/zinbwave_1.20.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: benchdamic, clusterExperiment, scBFA, singleCellTK, digitalDLSorteR suggestsMe: MAST, splatter dependencyCount: 75 Package: zlibbioc Version: 1.44.0 License: Artistic-2.0 + file LICENSE MD5sum: dc1ace66ffe17c1512715b7d8271f093 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/zlibbioc BugReports: https://github.com/Bioconductor/zlibbioc/issues git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: RELEASE_3_16 git_last_commit: d39f0b0 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 source.ver: src/contrib/zlibbioc_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.2/zlibbioc_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.2/zlibbioc_1.44.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/zlibbioc_1.44.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: SimRAD importsMe: affy, affyio, affyPLM, bamsignals, CellBarcode, ChemmineOB, HiCDOC, MADSEQ, makecdfenv, NanoMethViz, oligo, ompBAM, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, ShortRead, snpStats, TransView, VariantAnnotation, XVector, jackalope suggestsMe: metacoder linksToMe: bamsignals, ChemmineOB, csaw, diffHic, epialleleR, FLAMES, maftools, methylKit, NxtIRFcore, Rfastp, Rhtslib, scPipe, seqbias, seqTools, ShortRead, SpliceWiz, jackalope dependencyCount: 0 Package: EnMCB Version: 1.10.0 Depends: R (>= 4.0) Imports: methods, stats, survivalROC, glmnet, rms, mboost, Matrix, igraph, survivalsvm, ggplot2, BiocFileCache, boot, e1071, survival, utils Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, minfi, limma, rmarkdown License: GPL-2 Archs: x64 NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression. biocViews: Normalization, DNAMethylation, MethylationArray, SupportVectorMachine Author: Xin Yu Maintainer: Xin Yu VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: RELEASE_3_16 git_last_commit: 00ea766 git_last_commit_date: 2022-11-01 Date/Publication: 2022-11-01 win.binary.ver: bin/windows/contrib/4.2/EnMCB_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.2/EnMCB_1.10.0.tgz mac.binary.big-sur-arm64.ver: bin/macosx/big-sur-arm64/contrib/4.2/EnMCB_1.10.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowUtils Version: 1.62.0 Depends: R (>= 2.2.0) Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML, flowCore (>= 1.32.0) Suggests: gatingMLData License: Artistic-2.0 Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang Maintainer: Josef Spidlen URL: https://github.com/jspidlen/flowUtils BugReports: https://github.com/jspidlen/flowUtils/issues PackageStatus: Deprecated Package: tspair Version: 1.56.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek PackageStatus: Deprecated Package: IsoGeneGUI Version: 2.34.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package PackageStatus: Deprecated Package: gaia Version: 2.42.0 Depends: R (>= 2.10) License: GPL-2 Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella PackageStatus: Deprecated Package: inveRsion Version: 1.46.2 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres PackageStatus: Deprecated Package: Rnits Version: 1.32.1 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr Package: iteremoval Version: 1.18.0 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 Title: Iteration removal method for feature selection Description: The package provides a flexible algorithm to screen features of two distinct groups in consideration of overfitting and overall performance. It was originally tailored for methylation locus screening of NGS data, and it can also be used as a generic method for feature selection. Each step of the algorithm provides a default method for simple implemention, and the method can be replaced by a user defined function. biocViews: StatisticalMethod Author: Jiacheng Chuan [aut, cre] Maintainer: Jiacheng Chuan URL: https://github.com/cihga39871/iteremoval VignetteBuilder: knitr BugReports: https://github.com/cihga39871/iteremoval/issues PackageStatus: Deprecated Package: MACPET Version: 1.18.0 Depends: R (>= 3.6.1), InteractionSet (>= 1.13.0), bigmemory (>= 4.5.33), BH (>= 1.66.0.1), Rcpp (>= 1.0.1) Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 2.1.3), stats (>= 3.6.1), utils (>= 3.6.1), methods (>= 3.6.1), GenomicRanges (>= 1.37.14), S4Vectors (>= 0.23.17), IRanges (>= 2.19.10), GenomeInfoDb (>= 1.21.1), gtools (>= 3.8.1), GenomicAlignments (>= 1.21.4), knitr (>= 1.23), rtracklayer (>= 1.45.1), BiocParallel (>= 1.19.0), Rbowtie (>= 1.25.0), GEOquery (>= 2.53.0), Biostrings (>= 2.53.2), ShortRead (>= 1.43.0), futile.logger (>= 1.4.3) LinkingTo: Rcpp, bigmemory, BH Suggests: ggplot2 (>= 3.2.0), igraph (>= 1.2.4.1), rmarkdown (>= 1.14), reshape2 (>= 1.4.3), BiocStyle (>= 2.13.2) License: GPL-3 Title: Model based analysis for paired-end data Description: The MACPET package can be used for complete interaction analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM or SAM format and separates the data into Self-ligated, Intra- and Inter-chromosomal PETs. Furthermore, MACPET breaks the genome into regions and applies 2D mixture models for identifying candidate peaks/binding sites using skewed generalized students-t distributions (SGT). It then uses a local poisson model for finding significant binding sites. Finally it runs an additive interaction-analysis model for calling for significant interactions between those peaks. MACPET is mainly written in C++, and it also supports the BiocParallel package. biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod, Clustering, Classification, HiC Author: Ioannis Vardaxis Maintainer: Ioannis Vardaxis SystemRequirements: C++11 VignetteBuilder: knitr PackageStatus: Deprecated Package: gpart Version: 1.16.0 Depends: R (>= 3.5.0), grid, Homo.sapiens, TxDb.Hsapiens.UCSC.hg38.knownGene, Imports: igraph, biomaRt, Rcpp, data.table, OrganismDbi, AnnotationDbi, grDevices, stats, utils, GenomicRanges, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE Title: Human genome partitioning of dense sequencing data by identifying haplotype blocks Description: we provide a new SNP sequence partitioning method which partitions the whole SNP sequence based on not only LD block structures but also gene location information. The LD block construction for GPART is performed using Big-LD algorithm, with additional improvement from previous version reported in Kim et al.(2017). We also add a visualization tool to show the LD heatmap with the information of LD block boundaries and gene locations in the package. biocViews: Software, Clustering Author: Sun Ah Kim [aut, cre, cph], Yun Joo Yoo [aut, cph] Maintainer: Sun Ah Kim VignetteBuilder: knitr PackageStatus: Deprecated Package: TimeSeriesExperiment Version: 1.16.0 Depends: R (>= 4.1), S4Vectors (>= 0.19.23), SummarizedExperiment (>= 1.11.6) Imports: dynamicTreeCut, dplyr, edgeR, DESeq2, ggplot2 (>= 3.0.0), graphics, Hmisc, limma, methods, magrittr, proxy, stats, tibble, tidyr, vegan, viridis, utils Suggests: Biobase, BiocFileCache (>= 1.5.8), circlize, ComplexHeatmap, GO.db, grDevices, grid, knitr, org.Mm.eg.db, org.Hs.eg.db, MASS, RColorBrewer, rmarkdown, UpSetR, License: MIT + file LICENSE Title: Analysis for short time-series data Description: TimeSeriesExperiment is a visualization and analysis toolbox for short time course data. The package includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. Additionally, it also provides methods for retrieving enriched pathways. biocViews: TimeCourse, Sequencing, RNASeq, Microbiome, GeneExpression, ImmunoOncology, Transcription, Normalization, DifferentialExpression, PrincipalComponent, Clustering, Visualization, Pathways Author: Lan Huong Nguyen [cre, aut] () Maintainer: Lan Huong Nguyen URL: https://github.com/nlhuong/TimeSeriesExperiment VignetteBuilder: knitr BugReports: https://github.com/nlhuong/TimeSeriesExperiment/issues PackageStatus: Deprecated Package: PrecisionTrialDrawer Version: 1.14.0 Depends: R (>= 3.6) Imports: graphics, grDevices, stats, utils, methods, cBioPortalData, parallel, stringr, reshape2, data.table, RColorBrewer, BiocParallel, magrittr, biomaRt, XML, httr, jsonlite, ggplot2, ggrepel, grid, S4Vectors, IRanges, GenomicRanges, LowMACAAnnotation, googleVis, shiny, shinyBS, DT, brglm, matrixStats Suggests: BiocStyle, knitr, rmarkdown, dplyr License: GPL-3 Title: A Tool to Analyze and Design NGS Based Custom Gene Panels Description: A suite of methods to design umbrella and basket trials for precision oncology. biocViews: SomaticMutation, WholeGenome, Sequencing, DataImport, GeneExpression Author: Giorgio Melloni, Alessandro Guida, Luca Mazzarella Maintainer: Giorgio Melloni VignetteBuilder: knitr Package: GAPGOM Version: 1.14.0 Depends: R (>= 4.0) Imports: stats, utils, methods, Matrix, fastmatch, plyr, dplyr, magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db, org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase, BiocFileCache, matrixStats Suggests: org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db, org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db, org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db, org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc, ggplot2, kableExtra, profvis, reshape2 License: MIT + file LICENSE Title: GAPGOM (novel Gene Annotation Prediction and other GO Metrics) Description: Collection of various measures and tools for lncRNA annotation prediction put inside a redistributable R package. The package contains two main algorithms; lncRNA2GOA and TopoICSim. lncRNA2GOA tries to annotate novel genes (in this specific case lncRNAs) by using various correlation/geometric scoring methods on correlated expression data. After correlating/scoring, the results are annotated and enriched. TopoICSim is a topologically based method, that compares gene similarity based on the topology of the GO DAG by information content (IC) between GO terms. biocViews: GO, GeneExpression, GenePrediction Author: Rezvan Ehsani [aut, cre], Casper van Mourik [aut], Finn Drabløs [aut] Maintainer: Rezvan Ehsani URL: https://github.com/Berghopper/GAPGOM/ VignetteBuilder: knitr BugReports: https://github.com/Berghopper/GAPGOM/issues/ Package: XCIR Version: 1.12.0 Depends: methods Imports: stats, utils, tools, data.table, Biostrings, IRanges, VariantAnnotation, seqminer, ggplot2, biomaRt, readxl, S4Vectors Suggests: knitr, rmarkdown License: GPL-2 Title: XCI-inference Description: Models and tools for subject level analysis of X chromosome inactivation (XCI) and XCI-escape inference. biocViews: StatisticalMethod, RNASeq, Sequencing, Coverage Author: Renan Sauteraud, Dajiang Liu Maintainer: Renan Sauteraud URL: https://github.com/SRenan/XCIR VignetteBuilder: knitr BugReports: https://github.com/SRenan/XCIR/issues PackageStatus: Deprecated Package: pulsedSilac Version: 1.12.0 Depends: R (>= 3.6.0) Imports: robustbase, methods, R.utils, taRifx, S4Vectors, SummarizedExperiment, ggplot2, ggridges, stats, utils, UpSetR, cowplot, grid, MuMIn Suggests: testthat (>= 2.1.0), knitr, rmarkdown, gridExtra License: GPL-3 Title: Analysis of pulsed-SILAC quantitative proteomics data Description: This package provides several tools for pulsed-SILAC data analysis. Functions are provided to organize the data, calculate isotope ratios, isotope fractions, model protein turnover, compare turnover models, estimate cell growth and estimate isotope recycling. Several visualization tools are also included to do basic data exploration, quality control, condition comparison, individual model inspection and model comparison. biocViews: Proteomics Author: Marc Pagès-Gallego, Tobias B. Dansen Maintainer: Marc Pagès-Gallego VignetteBuilder: knitr Package: ctgGEM Version: 1.10.0 Depends: monocle, SummarizedExperiment, Imports: Biobase, BiocGenerics, graphics, grDevices, igraph, Matrix, methods, utils, sincell, TSCAN Suggests: BiocStyle, biomaRt, HSMMSingleCell, irlba, knitr, rmarkdown, VGAM License: GPL(>=2) Title: Generating Tree Hierarchy Visualizations from Gene Expression Data Description: Cell Tree Generator for Gene Expression Matrices (ctgGEM) streamlines the building of cell-state hierarchies from single-cell gene expression data across multiple existing tools for improved comparability and reproducibility. It supports pseudotemporal ordering algorithms and visualization tools from monocle, cellTree, TSCAN, sincell, and destiny, and provides a unified output format for integration with downstream data analysis workflows and Cytoscape. biocViews: GeneExpression, Visualization, Sequencing, SingleCell, Clustering, RNASeq, ImmunoOncology, DifferentialExpression, MultipleComparison, QualityControl, DataImport Author: Mark Block [aut], Carrie Minette [aut], Evgeni Radichev [aut], Etienne Gnimpieba [aut], Mariah Hoffman [aut], USD Biomedical Engineering [aut, cre] Maintainer: USD Biomedical Engineering VignetteBuilder: knitr PackageStatus: Deprecated Package: CytoTree Version: 1.8.0 Depends: R (>= 4.0), igraph Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase, Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc, RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster, pheatmap, scatterpie, umap, scatterplot3d, limma, stringr, grDevices, grid, stats LinkingTo: Rcpp Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat, BiocStyle License: GPL-3 Title: A Toolkit for Flow And Mass Cytometry Data Description: A trajectory inference toolkit for flow and mass cytometry data. CytoTree is a valuable tool to build a tree-shaped trajectory using flow and mass cytometry data. The application of CytoTree ranges from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation. It offers complete analyzing workflow for flow and mass cytometry data. biocViews: CellBiology, Clustering, Visualization, Software, CellBasedAssays, FlowCytometry, NetworkInference, Network Author: Yuting Dai [aut, cre] Maintainer: Yuting Dai URL: http://www.r-project.org, https://github.com/JhuangLab/CytoTree VignetteBuilder: knitr BugReports: https://github.com/JhuangLab/CytoTree/issues PackageStatus: Deprecated git_url: https://github.com/JhuangLab/CytoTree