[BioC] edgeR multiple contrasts Vs. One test

[Gordon K Smyth]

Hi Naomi,

exactTest just computes p-values.  Just as in limma, adjustment for 
multiple testing is done at the collation stage, rather than at the time 
of constructing the test and the p-value.

To see the default for topTags:

   > args(topTags)
   function (object, n = 10, adjust.method = "BH", sort.by = "PValue")
   NULL

Regards
Gordon

On Thu, 27 Jun 2013, Naomi Altman wrote:

> What method is used for FDR control in exactTest?
>
> Thanks,
> Naomi
>
>
>
> 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
>> Gordon
>> 
>>> 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?
>>> 
>>> Yours,
>>> 
>>> Michael
>> 
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