RNA-Seq Data Analysis    ◾    201

jpeg(‘heatmap2.jpg’)

my.contrasts

<- makeContrasts(conditiontumo-conditionnorm,levels=design)

fitq<-glmQLFit(yNorm, design)

qlfq<-glmQLFTest(fitq, contrast=my.contrasts)

DEGenes<-decideTestsDGE(qlfq,

adjust.method=”BH”, p.value=0.05, lfc=2)

logCPM <- cpm(yNorm, prior.count=2, log=TRUE)

rownames(logCPM) <- yNorm$genes$SYMBOL

colnames(logCPM) <- paste(yNorm$samples$group, 1:3, sep=”-”)

o <- order(qlfq$table$PValue)

logCPM <- logCPM[o[1:20],]

logCPM <- t(scale(t(logCPM)))

col.pan <- colorpanel(100, “blue”, “white”, “red”)

heatmap.2(logCPM, col=col.pan, Rowv=TRUE, scale=”none”,

trace=”none”, dendrogram=”both”, cexRow=1, cexCol=1.4,

margin=c(10,9), lhei=c(2,10), lwid=c(2,6))

dev.off()

FIGURE 5.27  Heatmap for the top 20 most differentially expressed genes across the samples.