[BioC] FDR/adj.Pvalues

Stefano Calza stef at biostatistics.it
Mon Jan 28 10:50:00 CET 2008

BH adjustement (say p.adjust(,...method="BH")) doesn't account for pi0. Moreover, a lower pi0 would give you smaller FDR.

So you probably used some othe algorithm. Which one?


On Sun, Jan 27, 2008 at 09:30:57AM -0800, Lana Schaffer wrote:
<Lana>In the experiment where pi0 is probably smaller and the FDR
<Lana>is high, some of the genes are plausable for the experiment.
<Lana>In other words, the unadjusted pvalue is small but the FDR is
<Lana>high for those genes and those genes are relavent to the experiment.
<Lana>What do you say about those genes?
<Lana>From: Naomi Altman [mailto:naomi at stat.psu.edu]
<Lana>Sent: Sat 1/26/2008 6:50 AM
<Lana>To: Lana Schaffer; bioconductor at stat.math.ethz.ch
<Lana>Subject: Re: [BioC] FDR/adj.Pvalues
<Lana>Have a look at the estimate the pi0 - that can have a big effect.  To
<Lana>understand what is going on, look at the histograms of unadjusted
<Lana>p-values.  The stronger the peak at the low end, the smaller the
<Lana>FDR-adjusted p-values will be.
<Lana>At 03:54 AM 1/26/2008, Lana Schaffer wrote:
<Lana>>I have a set of samples for which the FDR/adj.Pvalues (BH)
<Lana>>are >.5 for the same unadjusted pvalue level and number of
<Lana>>testing genes as another set of samples.  The other set of
<Lana>>samples have more significant FDR/adj.Pvalues.  What would
<Lana>>be some of the reasons why these adjusted pvalues would
<Lana>>would be different between the 2 sets of samples?
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