[BioC] edgeR - R script - results compared to DESeq

Simon Anders anders at embl.de
Mon Nov 28 20:08:55 CET 2011

On 2011-11-28 16:58, Avinash S wrote:
> I'm just starting with R and wanted to analyze my data for differential
> expression using edgeR. Here is the code which is working for me but I want
> to check if I'm missing something as I get more number of differentially
> expressed genes compared to DESeq

As you do not tell us how you used DESeq, it is hard to compare.

However, why do you first (correctly) check how many genes have an 
adjusted p value below your chosen FDR but then (incorrectly) export 
genes according to their raw p value?

> sum(p.adjust(et$table$p.value,method="BH")<  0.1)
> good = sum(et$table$p.value<0.05)

> I compared the result with DESeq and I get about 2000 genes more in edgeR
> at pVal<  0.05, however, the the matched genes showed same log2foldchanges.
> Is it usual that edgeR gives more number of diff.expr genes?

If you cut your gene list at a raw p value of 5%, you will get many 
false positives. Please read up on multiple hypothesis testing and false 
dicovery rate.


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