[BioC] Influence of expression correlation on false positive ratio

[Wolfgang Huber]

January,

if you only require per-gene p-values and no multiple testing 
adjustment, then the dependency is never a problem. The validity of 
unadjusted per-gene p-values is unaffected by whether there is 
dependency between the genes.

For multiple testing, if you do FWER by the Westfall-Young method, any 
dependence is also no problem. If you do FDR by the Benjamini-Hochberg 
method, problems can in principle occur if there is pervasive 
dependence. Often this is caused by technical artifacts, which would be 
addressed (and removed) by the methods mentioned by Jeff. If it is 
biological, then a serial univariate analysis (gene-by-gene testing) 
does not seem the cleverest choice of approach, and a truly multivariate 
approach seems more advisable.

	Best wishes
	Wolfgang


Jeff Leek scripsit 07/09/2012 01:17 PM:
> Hi January,
>
> If the tests are only dependent in small groups, say because genes are
> grouped into small modules,  then most FDR methods in the p.adjust()
> function or the methods in the qvalue package will work. The Bonferroni
> correction controls a more conservative error rate, but also holds under
> dependence.
>
> If the sources of dependence are more pervasive, like if there are batch
> effects:
>
> http://www.nature.com/nrg/journal/v11/n10/full/nrg2825.html
>
> Then you can either use the batch correction methods in Limma if, say, you
> know the date the samples were processed. Or, if you don't know the sources
> of large scale dependence, you can use the sva package:
>
> http://www.bioconductor.org/packages/devel/bioc/html/sva.html
>
> which implements the methods described here:
>
> http://www.pnas.org/content/early/2008/11/24/0808709105.abstract
>
>
> Best,
>
>
> Jeff
>
>
>
> On Jul 9, 2012 7:08 AM, "January Weiner" <january.weiner at mpiib-berlin.mpg.de>
> wrote:
>
>> Hello,
>>
>> statistical methods for assessing significance of differences in
>> expression assume, correct me if I'm wrong, independence of the tests.
>> Does anyone have at hand any papers on the performance -- in terms of
>> type I error -- of methods such as limma / eBayes? I'm sure this issue
>> has been investigated in depth.
>>
>> Kind regards,
>>
>> January
>>
>> --
>> -------- Dr. January Weiner 3 --------------------------------------
>> Max Planck Institute for Infection Biology
>> Charitéplatz 1
>> D-10117 Berlin, Germany
>> Web   : www.mpiib-berlin.mpg.de
>> Tel     : +49-30-28460514
>> Fax    : +49-30-28450505
>>
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-- 
Best wishes
	Wolfgang

Wolfgang Huber
EMBL
http://www.embl.de/research/units/genome_biology/huber