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<h1 class="documentFirstHeading">ComplexAnalysisLecture.R</h1>
<div class="documentDescription"></div>
<div class="plain">
<p>###################################################
### chunk number 1: setup
###################################################
options(width=60)
dataDir <- system.file("extdata", package="arrayQ")
workDir <- tempdir()</p>
<p>###################################################
### chunk number 2: expt-design-ours-setup
###################################################
library("limma")
description <- readTargets("ArrayDesc", path=dataDir)
factors <- description[["Cy3"]]
exptlDesign <-
data.frame(cellLine=factor(sub("PEC(<a href="#ref0-9">[0-9]</a>+)_.*", "\\1", factors)),
SFN=factor("Low", levels=c("Low", "High")),
HGF=factor("Low", levels=c("Low", "High")))
exptlDesign[grep("SFN", description[["Cy3"]]), "SFN"] <- "High"
exptlDesign[grep("HGF", description[["Cy3"]]), "HGF"] <- "High"</p>
<p>###################################################
### chunk number 3: expt-design-ours
###################################################
head(exptlDesign)
table(exptlDesign<a href="ComplexAnalysisLecture.R#ref,-1">[,-1]</a>)</p>
<p>###################################################
### chunk number 4: formula-a eval=FALSE
###################################################
## Response ~ Dependent</p>
<p>###################################################
### chunk number 5: formula-our eval=FALSE
###################################################
## ~1 + SFN + HGF + SFN:HGF
## ~SFN*HGF</p>
<p>###################################################
### chunk number 6: model-matrix-eg
###################################################
X <- model.matrix(~SFN*HGF, exptlDesign)
head(X)</p>
<p>###################################################
### chunk number 7: model-fit-setup
###################################################
RG <- read.maimages(description$FileName,
path=dataDir,
source="genepix",
wt.fun=wtflags())</p>
<p>MA <- normalizeBetweenArrays(RG,
"vsn",
strata=rep(1:2, each=nrow(RG)/2))</p>
<p>library(convert)
M <- as(MA, "ExpressionSet")
sampleNames(M) <- description[["Cy3"]]
featureNames(M) <- MA[["genes"]][["ID"]]</p>
<p>metaD <- read.csv(file.path(dataDir, "PEDB_ARRAY_annotations.csv"), as.is=TRUE)
dropRows <- metaD[["Description"]] %in% c("EMPTY", "Failed sequencing")
M <- M[!dropRows,]</p>
<p>dupCor <- duplicateCorrelation(exprs(M),
ndup=2,
spacing=nrow(M)/2)</p>
<p>###################################################
### chunk number 8: M-eg
###################################################
M</p>
<p>###################################################
### chunk number 9: M-half-eg
###################################################
Mh <- M[1:(nrow(M)/2),]</p>
<p>###################################################
### chunk number 10: limma-lmFit-eg
###################################################
fit <- lmFit(Mh, X)
efit <- eBayes(fit)</p>
<p>###################################################
### chunk number 11: limma-lmFit-eg-summary
###################################################
names(efit)
head(efit[["coefficients"]])</p>
<p>###################################################
### chunk number 12: expected-value
###################################################
head(cbind(X, X %*% efit[["coefficients"]]<a href="ComplexAnalysisLecture.R#ref1,">[1,]</a>))</p>
<p>###################################################
### chunk number 13: decideTests-eg
###################################################
decided <- decideTests(efit)
summary(decided)</p>
<p>###################################################
### chunk number 14: topTable-eg
###################################################
topTable(efit, coef=2, n=sum(decided<a href="ComplexAnalysisLecture.R#ref,2">[,2]</a>!=0))</p>
<p>###################################################
### chunk number 15: duplicate-correlations-eg
###################################################
dupCor <-
duplicateCorrelation(exprs(M), design=X, ndups=2, spacing=nrow(M)/2)</p>
<p>###################################################
### chunk number 16:
###################################################
summary(dupCor[["atanh.correlations"]])
dupCor[["consensus"]]</p>
<p>###################################################
### chunk number 17: duplicate-analysis
###################################################
dupFit <- lmFit(M, design=X,
ndups=2, spacing=nrow(M)/2,
correlation=dupCor[["consensus.correlation"]])
dupFitE <- eBayes(dupFit)
dupDecided <- decideTests(dupFitE)</p>
<p>###################################################
### chunk number 18:
###################################################
summary(dupDecided)</p>
<p>###################################################
### chunk number 19: cell-line-eg
###################################################
levels(exptlDesign[["cellLine"]])</p>
<p>###################################################
### chunk number 20: cell-line-design
###################################################
head(model.matrix(~cellLine, exptlDesign))</p>
<p>###################################################
### chunk number 21: full-model-eg
###################################################
X <- model.matrix(~cellLine+SFN*HGF, exptlDesign)
dupCor <-
duplicateCorrelation(exprs(M), design=X,
ndups=2, spacing=nrow(M)/2)
dupFit <- lmFit(M, design=X,
ndups=2, spacing=nrow(M)/2,
correlation=dupCor[["consensus.correlation"]])
dupFitE <- eBayes(dupFit)
dupDecided <- decideTests(dupFitE)</p>
<p>###################################################
### chunk number 22: full-model-highlights
###################################################
dupCor[["consensus.correlation"]]
summary(dupDecided)</p>
<p>###################################################
### chunk number 23: full-model-topTable
###################################################
topTable(dupFitE, coef=6)</p>
<p>###################################################
### chunk number 24: fitted
###################################################
fittedVals <- fitted(dupFitE)
dim(fittedVals)</p>
<p>###################################################
### chunk number 25: ave-exprs
###################################################
observedVals <- (exprs(M)[1:(nrow(M)/2),]+exprs(M)[nrow(M)/2+1:(nrow(M)/2),])/2
residualVals <- residuals(dupFitE, observedVals)</p>
<p>###################################################
### chunk number 26: as-data-frame
###################################################
df <-
data.frame(sampleId=rep(1:20, each=nrow(observedVals)),
SFN=rep(exptlDesign[["SFN"]], each=nrow(observedVals)),
HGF=rep(exptlDesign[["HGF"]], each=nrow(observedVals)),
observed=as.vector(observedVals),
fitted=as.vector(fittedVals),
residuals=as.vector(residualVals))</p>
<p>###################################################
### chunk number 27: residuals-by-sample
###################################################
library(lattice)
print(bwplot(sampleId~residuals, df))</p>
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