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@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
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