Expression analysis with R: package edgeR

Sample code for edgeR and DESeq for differential expressed gene

Best step-by-step code

http://www.nbic.nl/uploads/media/Practical_DiffExpr.pdf


Comparision of edgeR and DESeq

http://gettinggeneticsdone.blogspot.com/2012/09/deseq-vs-edger-comparison.html

# edgeR 

========


## Make design matrix

condition < relevel(factor(meta$condition), ref="untreated")
libType <  factor(meta$libType)


edesign  <  model.matrix(~libType+condition)





## Make new DGEList, normalize by library size, and estimate dispersion allowing

 possible trend with average count size
e <  DGEList(counts=counttable)


e <  calcNormFactors(e)


e <  estimateGLMCommonDisp(e, edesign)


e < estimateGLMTrendedDisp(e, edesign) 

e <  estimateGLMTagwiseDisp(e, edesign)



## MDS Plot
plotMDS(e, main="edgeR MDS Plot")


## Biological coefficient of variation plot

plotBCV(e, cex=0.4, main="edgeR: Biological coefficient of variation (BCV) vs abundance")

## Fit the model, testing the coefficient for the treated vs untreated comparison
efit <  glmFit(e, edesign)


efit <  glmLRT(efit, coef="conditiontreated")





## Make a table of results
etable  <  topTags(efit, n=nrow(e))$table


etable <  etable[order(etable$FDR), ]


head(etable)



## ~MA Plot
with(etable, plot(logCPM, logFC, pch=20, main="edgeR: Fold change vs abundance"))

with(subset(etable, FDR<0.05), points(logCPM, logFC, pch=20, col="red"))
abline(h=c(-1,1), col="blue")