# generate raw counts from NB, create list object
y <- matrix(rnbinom(80,size=1,mu=10),nrow=20)
d <- DGEList(counts=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
rownames(d$counts) <- paste("gene",1:nrow(d$counts),sep=".")
# estimate common dispersion and find differences in expression
# here we demonstrate the 'exact' methods, but the use of topTags is
# the same for a GLM analysis
d <- estimateCommonDisp(d)
de <- exactTest(d)
# look at top 10
topTags(de)
# Can specify how many genes to view
tp <- topTags(de, n=15)
# Here we view top 15
tp
# Or order by fold change instead
topTags(de,sort.by="logFC")
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