[BioC] edgeR multiple contrasts Vs. One test

Naomi Altman naomi at stat.psu.edu
Fri Jun 28 03:22:01 CEST 2013

What method is used for FDR control in exactTest?


At 07:40 PM 6/27/2013, you wrote:
>Dear Michael,
>If I understand you correctly, you are asking about adjusting the 
>p-values for multiple testing.
>The default in edgeR is to adjust the p-value in order to control 
>the false discovery rate (FDR).  If you control the FDR at a given 
>level for each of 5 contrasts separately, then you have 
>automatically controlled the FDR at the same level for all 5 
>contrasts together.  The FDR is a scalable quantity in this sense.
>The situation would be different if you used adjust.method="holm". 
>Holm's method controls the family-wise type I error rate, and the 
>type I error rate does not scale over multiple contrasts.
>Best wishes
>>Date: Wed, 26 Jun 2013 12:44:14 -0700
>>From: Michael Breen <breenbioinformatics at gmail.com>
>>To: bioconductor at r-project.org
>>Subject: [BioC] edgeR multiple contrasts Vs. One test
>>Hi All,
>>If we have an design for which we have 4 groups, lets call:
>>1.Control Untreated
>>2. Control Treated
>>3. Cases Untreated
>>4. Cases Treated.
>>and we were interested in differences between:
>>-treated and untreated for Control
>>-treated and untreated for Cases
>>-treated differences between cases and controls
>>-untreated differences between cases and controls.
>>-differences between treated and untreated.
>>5 tests in total. We can then use edgeR contrast function as 
>>something like this...
>>contrasts <- makeContrasts(
>>Case.TreatedvsUntreated = Case.Treated-Case.Untreated,
>>Control.TreatedvsUntreated = Control.Treated-Control.Untreated,
>>CasevsControl.Untreated = Case.Untreated-Control.Untreated,
>>etc..... levels=design)
>>This produces an appropriate rank order of significance for each 
>>contrast. However, what is the cost of having no correction for the 
>>fact that I just performed 5 tests on each gene instead of just 1 test??
>>Any insight?
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