[BioC] [DESeq] p-value vs adjusted p-value

Davide Cittaro cittaro.davide at hsr.it
Thu Jun 6 13:44:27 CEST 2013

On Jun 5, 2013, at 8:56 PM, Simon Anders <anders at embl.de> wrote:

> On 05/06/13 17:06, lucia kwak wrote:
>> Hi all,
>> Thank you for your answer. I've used method=blind to estimate the
>> dispersions in DESeq. For the comparison of tools, I am using different
>> p-value cutoff for two packages making the similar subset size of
>> significant genes. The adjusted p-value in edgeR is much lower than the
>> p-value used in DESeq.  But if I use the adjusted p-value in DESeq also, it
>> is hard to find the differentially expressed genes, while the edgeR shows
>> many significant genes.
> I am getting confused here. I sounds as if at some point you are 
> comparing raw p values from one tool with adjusted p value from the 
> other tool. This would make no sense at all.

I think Lucia is doing like this:
use DESeq -> not many significant genes under FDR < 0.05 -> use nominal p-value to get some more genes. Use edgeR -> more genes under FDR <0.05 -> keep edgeR genes. Well, at least I've seen people doing this.
> I do remember that edgeR can somehow be switched to a "Poisson 
> mode" in case of no replicates, where the dispersion is set to zero. 
> Long ago, this was the default, but this is (reasonably) no longer the 
> case, I think.

Actually there's a new approach but the manual says that if you do not have replicates it would be better to stop after you plotted some MDS to describe relationships among samples.


> Maybe post the code you used.
>   Simon
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Davide Cittaro, PhD

Coordinator of Bioinformatics Core
Center for Translational Genomics and Bioinformatics
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