[BioC] Is there any prejudice whether to use edgeR or DESeq for differential expression analysis for RNA Seq data

[Mark Robinson]

Hi Sakshi,

The two packages are indeed fairly similar.  They differ in their: 

i) look-and-feel -- overall the pipelines are quite similar, but things like specifying arbitrary contrasts, offsets, packaging the output statistics, etc. are, IMHO, easier in edgeR.

ii) standard normalization (edgeR - TMM; DESeq - what I call "RLE", which is also implemented in edgeR's calcNormFactors) … these are actually very similar anyways in the situations where I've tested it.

iii) dispersion estimation (edgeR default - moderate to trend; DESeq default - take maximum of individual or trend).  My impression is that this makes DESeq (slightly?) less powerful and edgeR (slightly?) sensitive to outliers.


> I am unsure as to if there is any particular condition that is the deciding factor between whether to use edgeR or DESeq packages for differential expression analysis for RNA Seq data.

I prefer edgeR, but there is some pretty strong prejudice behind that :)


> For example, does it depend upon how the counts were normalized?

I don't understand this question, since both packages expect un-normalized counts.


Best, Mark



On 07.11.2012, at 13:01, Sakshi Gulati wrote:

> Hi
> 
> I am unsure as to if there is any particular condition that is the deciding factor between whether to use edgeR or DESeq packages for differential expression analysis for RNA Seq data. For example, does it depend upon how the counts were normalized?
> 
> Thanks
> Sakshi
> 
> 
> Sakshi Gulati
> PhD Student
> Biomolecular Modelling Laboratory
> Cancer Research UK London Research Institute
> 44 Lincoln's Inn Fields
> London WC2A 3LY
> 
> 
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