200    ◾    Bioinformatics

significant genes are toward the top of each side. We can create a volcano plot of the RNA-

Seq results using the data of the top differentially expressed genes as shown in Figure 5.26.

#############

#volcano plot

jpeg(‘volcano.jpg’)

fitq <- glmQLFit(yNorm, design)

qlfq<- glmTreat(fitq, contrast=my.contrasts, lfc=2)

resFilt<- topTags(qlfq, n=100, adjust.method=”BH”, sort.

by=”PValue”, p.value=1)

volcanoData <- cbind(resFilt$table$logFC,

-log2(resFilt$table$PValue))

colnames(volcanoData) <- c(“logFC”, “negLogPval”)

plot(volcanoData, pch=19)

dev.off()

Once again, when all chromosomes are studied, the point distribution on the volcano plot

will be very dense. Moreover, some programs are able to color the upregulated and down-

regulated genes for better visualization.

The heatmap has been known as the most popular graphical representation for visual-

izing complex and multidimensional data. We can use the heatmap to visualize differential

expression of the genes. First, we need to use “cpm” function to convert read abundance

into log2 CPM values. The heatmap can be created for the top differentially expressed

genes. The following script creates a heatmap for the top 20 differentially expressed genes:

FIGURE 5.26  Volcano plot showing regulated genes.