[BioC] Questions about DESeq2 (new version and filtering low counts)

[amandine.fournier at chu-lyon.fr]

Dear Mike and others,

Thank you Mike for your reply yesterday at my last question about PCA and transformed data.

I have two other questions for you today ;-)

The first question is about your new version of DESeq2 :
      - I found about 140 DEG when I used a previous version (v1.0.9)
      - Now I am using your new version v1.0.19 with exactly the same data and FDR threshold, and I find more DEG (about 360).
What does explain this difference ? I thought it is the new functionality of count outlier detection. But when I turn this filtering off by using  cooksCutoff=FALSE in nbinomWaldTest, I find ~ 370 DEG. What are the other differences between the two versions ? (only outlier detection is reported in the  NEWS file)

The second question is about filtering low counts : as I understand the vignette, the filtering is done after dispersion estimation. Then we just redo the Benjamini-Hochberg adjustement.
I would like to know why it is better to keep the previous estimates ? Naively I would first have filtered genes and then have estimated dispersion without the low counts. But my understanding of statistics is poorer as yours, so could you explain me the rationale of this order in a few words ?

Thanks a lot in advance !
Best regards,
Amandine

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Amandine Fournier
Lyon Neuroscience Research Center
& Lyon Civil Hospital (France)