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<h1 class="documentFirstHeading">lab2.R</h1>
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###################################################
<br />### chunk number 1:
<br />###################################################
<br />set.seed(1234)
<br />x <- rnorm(5)
<br />print(x)
<br />
<br />###################################################
<br />### chunk number 2:
<br />###################################################
<br />print(3:10)
<br />
<br />###################################################
<br />### chunk number 3:
<br />###################################################
<br />print(x[2:4])
<br />
<br />###################################################
<br />### chunk number 4:
<br />###################################################
<br />inds <- c(2,3,4)
<br />print(x[inds])
<br />
<br />###################################################
<br />### chunk number 5:
<br />###################################################
<br />inds <- c(low=2,mid=3,hi=4)
<br />print(inds)
<br />print(x[ inds[c("low","hi")] ])
<br />
<br />###################################################
<br />### chunk number 6:
<br />###################################################
<br />myList <- list(a=x, b=inds)
<br />print(myList[[1]])
<br />print(myList[["a"]])
<br />print(myList$b)
<br />
<br />
<br />###################################################
<br />### chunk number 7:
<br />###################################################
<br />library(cluster)
<br />library(Biobase)
<br />library(annotate)
<br />library(genefilter)
<br />library(AggPred)
<br />library(sma)
<br />
<br />
<br />###################################################
<br />### chunk number 8:
<br />###################################################
<br />library(golubEsets)
<br />data(golubTrain)
<br />data(golubTest)
<br />print(golubTrain)
<br />print(table(golubTrain$ALL.AML))
<br />
<br />###################################################
<br />### chunk number 9:
<br />###################################################
<br />LS <- exprs(golubTrain)
<br />cl <- golubTrain$ALL.AML
<br />TS <- exprs(golubTest)
<br />clts <- golubTest$ALL.AML
<br />
<br />###################################################
<br />### chunk number 10:
<br />###################################################
<br />LS[LS<100]<-100
<br />TS[TS<100]<-100
<br />LS[LS>16000]<-16000
<br />TS[TS>16000]<-16000
<br />
<br />
<br />###################################################
<br />### chunk number 11:
<br />###################################################
<br />mmfilt <- function(r=5, d=500, na.rm=TRUE) {
<br /> function(x) {
<br /> minval <- min(x, na.rm=na.rm)
<br /> maxval <- max(x, na.rm=na.rm)
<br /> (maxval/minval > 5) && (maxval-minval > 500)
<br /> }
<br /> }
<br />
<br />mmfun <- mmfilt()
<br />
<br />ffun <- filterfun(mmfun)
<br />
<br />good <- genefilter(cbind(LS, TS), ffun )
<br />
<br />sum(good) ##I get 3571 not 3051!
<br />
<br />LSsub <- log10(LS[good,])
<br />TSsub <- log10(TS[good,])
<br />print(dim(LSsub))
<br />
<br />
<br />###################################################
<br />### chunk number 12:
<br />###################################################
<br />cor.all<-cor(LSsub)
<br />plot.cor(cor.all[order(cl), order(cl)], labels=sort(cl),
<br /> labcols=unclass(sort(cl)),
<br /> title="Correlation matrix, all genes",zlim=c(-1,1))
<br />
<br />###################################################
<br />### chunk number 13:
<br />###################################################
<br />library(ellipse)
<br />plotcorr(cor.all[order(cl), order(cl)], labels=sort(cl),
<br /> labcols=unclass(sort(cl)),
<br /> title="Correlation matrix, all genes")
<br />
<br />###################################################
<br />### chunk number 14:
<br />###################################################
<br />gf <- gapFilter(250, 500, .1)
<br />ff <- filterfun(gf)
<br />good <- genefilter(10^LSsub, gf)
<br />sum(good) # should be 1091
<br />
<br />cor.gf <- cor(LS[good,])
<br />
<br />
<br />###################################################
<br />### chunk number 15:
<br />###################################################
<br />plot.cor(cor.gf[order(cl), order(cl)], labels=sort(cl),
<br /> labcols=unclass(sort(cl)),
<br /> title="Correlation matrix, filtered genes",zlim=c(-1,1))
<br />
<br />###################################################
<br />### chunk number 16:
<br />###################################################
<br />plotcorr(cor.gf[order(cl), order(cl)], labels=sort(cl),
<br /> labcols=unclass(sort(cl)),
<br /> title="Correlation matrix, filtered genes")
<br />
<br />###################################################
<br />### chunk number 17:
<br />###################################################
<br />library(edd)
<br />ALLtrDists <- edd.unsupervised( golubTrain[, golubTrain$ALL=="ALL"] )
<br />print(table(ALLtrDists))
<br />
<br />###################################################
<br />### chunk number 18:
<br />###################################################
<br />golURL <- url("http://www-genome.wi.mit.edu/mpr/data_set_ALL_AML_train.txt","r")
<br />
<br />###################################################
<br />### chunk number 19:
<br />###################################################
<br />golVec <- scan(golURL, sep="\t", what="")
<br />
<br />
<br />###################################################
<br />### chunk number 20:
<br />###################################################
<br />print(length(golVec))
<br />print(length(golVec)/79)
<br />
<br />###################################################
<br />### chunk number 21:
<br />###################################################
<br />golVec <- c(golVec[1:78],"",golVec[-(1:78)])
<br />print(length(golVec)/79)
<br />golMat <- t(matrix(golVec,nr=79))
<br />print(dim(golMat))
<br />print(golMat[1:5,1:5])
<br />
<br />###################################################
<br />### chunk number 22:
<br />###################################################
<br />cExprs <- golMat[-1,seq(3,78,2)]
<br />numExprs <- t(apply(cExprs,1,as.numeric))
<br />print(numExprs[1:4,1:4])
<br />
<br />###################################################
<br />### chunk number 23:
<br />###################################################
<br />accnos <- golMat[-1,2]
<br />dimnames(numExprs) <- list(accnos,NULL)
<br />
<br />###################################################
<br />### chunk number 24:
<br />###################################################
<br />exprSamps <- golMat[1,seq(3,78,2)]
<br />
<br />###################################################
<br />### chunk number 25:
<br />###################################################
<br />pheu <- url("http://www-genome.wi.mit.edu/mpr/table_ALL_AML_samples.txt","r")
<br />PH <- readLines(pheu)
<br />print(length(PH))
<br />print(PH[1:5])
<br />
<br />###################################################
<br />### chunk number 26:
<br />###################################################
<br />print(PH[10])
<br />print(PH[47])
<br />
<br />###################################################
<br />### chunk number 27:
<br />###################################################
<br />print(strsplit(PH[10],"\t"))
<br />
<br />###################################################
<br />### chunk number 28:
<br />###################################################
<br />samp <- rep(NA,38)
<br />dx <- rep(NA,38)
<br />cellType <- rep(NA,38)
<br />for (i in 10:47) {
<br /> tmp <- strsplit(PH[i],"\t")[[1]]
<br /> samp[i-9] <- tmp[1]
<br /> dx[i-9] <- tmp[3]
<br /> cellType[i-9] <- tmp[6]
<br />}
<br />dx <- substring(dx,1,3)
<br />samp <- c(substring(samp[1:9],1,1),substring(samp[10:38],1,2))
<br />phenoDF <- data.frame(samp=as.numeric(samp), dx=dx,
<br /> cellType=cellType)
<br />print(phenoDF[1:4,])
<br />
<br />###################################################
<br />### chunk number 29:
<br />###################################################
<br />library(Biobase)
<br />myPh <- new("phenoData", pData=phenoDF[as.numeric(exprSamps),],
<br /> varLabels=list(samp="sample code",
<br /> dx="diagnosis", cellType="cell type"))
<br />myES <- new("exprSet", exprs=numExprs,
<br /> phenoData=myPh, description=names(phenoDF),
<br /> annotation="affy", notes="from web")
<br />
<br />###################################################
<br />### chunk number 30:
<br />###################################################
<br />print(myES)
<br />print(myES[1:10,])
<br />print(myES$dx[c(1:5,37:38)])
<br />
<br />###################################################
<br />### chunk number 31:
<br />###################################################
<br />print(geneNames(myES)[c(1,1000,2000)])
<br />print(exprs(myES)[c(1,1000,2000),1:3])
<br />
<br />
<br />
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<a href="../../../../News/2009-10-28-Release-2.5"
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title="BioC 2.5 Released">2009-10-26</a>
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BioC 2.5, consisting of 352 packages and designed to work with R 2.10.z, was released today.
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<a href="../../../../News/RinNYT"
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title="Print Media Recognition">2009-01-07</a>
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R, the open source platform used by Bioconductor, featured in a series of articles in the New York Times.
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