Hi Cei,
It is definitely on our to-do list but, no, we don't yet have any means to
do gene set analyses within the edgeR framework.
At this stage, I think the best bet is simply to analyse the counts as
approximately normal and use limma. For example, compute
log-counts-per-million,
y <- log2( 1e6* (counts+0.5) / (lib.size+0.5) )
then quantile normalize, then analyse as usual in limma. Note the use of
an offset of half-a-count to avoid infinite values.
Alternatively, use the effective library sizes estimated by edgeR in place
of actual library sizes and skip the quantile normalization.
This normal-based approach will work well for high variability human data.
If your RNA-Seq data is low variability, close to Poisson, then the
normal-based approach is a bit further from being optimal, although
probably still servicable.
Best wishes
Gordon
---------------------------------------------
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
Tel: (03) 9345 2326, Fax (03) 9347 0852,
sm...@wehi.edu.au
http://www.wehi.edu.au
http://www.statsci.org/smyth
Date: Sat, 11 Jun 2011 10:38:45 -0500
From: Cei Abreu-Goodger <c...@ebi.ac.uk>
To: bioc-sig-sequencing@r-project.org
Subject: [Bioc-sig-seq] roast/romer for count data (edgeR)?
Hello Davis, Gordon, et al.,
Is it possible to perform focused or competitive gene-set analysis for
experiments with count data and linear models? Like what is available in
limma, with the roast and romer functions, but for edgeR?
Any tips or suggestions would be great!
Thanks,
Cei
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