[BioC] DESeq variance question
Gordon K Smyth
smyth at wehi.EDU.AU
Sun Dec 4 23:57:56 CET 2011
Dear Steffen,
> Date: Fri, 02 Dec 2011 13:53:42 +0100
> From: "Steffen Priebe" <Steffen.Priebe at hki-jena.de>
> To: <bioconductor at r-project.org>
> Subject: [BioC] DESeq variance question
>
> I was using DESeq (and edgeR) for differentially expression analysis. In
> my current dataset I compare 3 biological replicates of control vs. 3
> biol. replicates from a mutant. The resulting 4 top genes according
> adjusted pvalue by DESeq and edgeR have a very high variance. (The
> reason for this is, that this are genes located on the chrY and only one
> replicate of the mutant was male)
Replicates should be representative of the same population, so I would
remove the male mutant from the experiment, or else remove all X and Y
chromosome genes from the analysis. In our in-house analyses, we have
tended to do the latter when faced with your situation.
More generally, this is exactly the issue that tagwise dispersion
estimation in edgeR is intended to combat. In our experience, filtering
so that genes are expressed in at least three libraries (for a 3 vs 3
study) and using a reasonably low prior.n to estimateTagwiseDisp() will
give a satisfying topTags gene list.
You don't say whether you used tagwise dispersion estimation.
> My question is now, how can genes with such a high variance of the
> counts result in this small pvalues? Is there any way to avoid this,
> because I think this are False Positives?
>
> Attached you can find the combined result table of DESeq and edgeR for
> the top 100 genes. The problem occurs for the first 4 genes. The raw
> counts are stated in columns P-U (P-R: Mutant, T-U Control).
Note that we have not seen your attachments, with are removed by the list
server. Nor do we know what version of software you are using. If you
post again, please give output of sessionInfo() and give code for your
edgeR analysis.
Best wishes
Gordon
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