[BioC] edgeR multiple contrasts Vs. One test

[Gordon K Smyth]

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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