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.

Best regards,
Lucia


On Wed, Jun 5, 2013 at 10:42 AM, Davide Cittaro <cittaro.davide@hsr.it>wrote:

>
> On Jun 5, 2013, at 4:25 PM, Steve Lianoglou <lianoglou.steve@gene.com>
> wrote:
> >
> >
> > It's not clear to me what you are saying. Are you suggesting that edgeR
> is
> > returning a large number of significant genes at a low FDR, or are the
> > number of significant genes roughly the same at the same FDR's between
> the
> > two packages?
> >
>
> I don't know if this is the case, but I've seen edgeR giving smaller
> p-values (and FDR) than DESeq (and voom) on the same dataset...
>
> >
> >> How can I solve this
> >> problem?
> >
> >
> > Sequence more replicates ;-)
> >
>
> ROTFL!
> >
> >
> > In DESeq you must be using the "blind" method to estimate over
> dispersion,
> > right? You can imagine how that makes it harder to detect all but the
> most
> > extreme fold changes.
>
> A thing I've not checked yet: is the dispersion estimated by DESeq and
> edgeR similar?
>
> d
>
>
>
> /*
> Davide Cittaro, PhD
>
> Coordinator of Bioinformatics Core
> Center for Translational Genomics and Bioinformatics
> Ospedale San Raffaele
> Via Olgettina 58
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>
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>
>


-- 
Best regards,
Lucia

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