Hi Mike,

Thank you for the quick reply. So when I filtered using the padj < 0.1 criterion, DESeq did not give me any results but DESeq2 showed 53 genes. But when I used the pval < 0.05 criterion and filtered, I got 1008 genes in DESeq2 and 222 genes in DESeq, with no overlap between the 2 lists. Is it because we should be looking at padj rather than pval?

Thank you once again!

-Shobana

From: Michael Love <michaelisaiahlove@gmail.com<mailto:michaelisaiahlove@gmail.com>>
Date: Tuesday, February 18, 2014 2:49 PM
To: "Shobana Sekar [guest]" <guest@bioconductor.org<mailto:guest@bioconductor.org>>
Cc: "bioconductor@r-project.org<mailto:bioconductor@r-project.org>" <bioconductor@r-project.org<mailto:bioconductor@r-project.org>>, Shobana Sekar <ssekar@tgen.org<mailto:ssekar@tgen.org>>
Subject: Re: DESeq2

hi,

There is not really enough information here to give a concrete answer, but yes, typically DESeq2 will contain most of the DESeq genes found for a given adjusted p-value. If this were not the case, I would first check to see if the rows of the results tables are not lining up for some reason. How many genes are found by either software?

Mike


On Tue, Feb 18, 2014 at 4:36 PM, Shobana Sekar [guest] <guest@bioconductor.org<mailto:guest@bioconductor.org>> wrote:

Hi,

I've been using the DESeq as well as DESeq2 packages for my RNAseq analysis. For one particular study alone, I find some discrepancies between DESeq and DESeq2 results. I've read in forums that the gene list is longer when we use DESeq2, which is true in my case. But what I also notice for this study is that there is no overlap between the gene list obtained using DESeq and DESeq2. Could you please help in understanding this difference?

 -- output of sessionInfo():

R version 3.0.2 (2013-09-25)
Platform: x86_64-apple-darwin10.8.0 (64-bit)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] DESeq2_1.2.8          RcppArmadillo_0.4.000 Rcpp_0.10.6           GenomicRanges_1.14.4
[5] XVector_0.2.0         IRanges_1.20.6        BiocGenerics_0.8.0


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