)

g

g

(the statistical significance) as its y-coordinate. Each gene is

n this two-dimensional space as a point. Using this technique, the

on of discovered DEGs can be visualised and analysed for both

l significance and statistical significance.

imma package has a function named as volcanoplot. Below

e of drawing a volcano plot for the prostate cancer data using this

In this code, coef=2 is consistent with the aforementioned

t ߚ^መଶ or the second column of $coefficients corresponds to

ma fold change or the modified fold change. Moreover,

ight=10 instructs the function to highlight top ten genes with

est p values in the resulting volcano plot. Figure 6.8 shows such

o plot,

anoplotplot(sam.model,coef=2,highlight=10)

volcano plot generated by the volcanoplotplot function of the limma

r the prostate cancer data.

output of the volcanoplot function shown in Figure 6.8

needs more information. This is because the displayed genes in

ano plot were only the top ten genes with the smallest p values,

e biological significance information was missing. Importantly,

played genes may or may not be statistically significant or may

f all discovered DEGs. In other words, it is not sure whether their

are less or greater than a critical p value.