[Bioc-sig-seq] roast/romer for count data (edgeR)?
Gordon K Smyth
smyth at wehi.EDU.AU
Mon Jun 13 01:47:45 CEST 2011
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,
smyth at 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 <cei at ebi.ac.uk>
> To: bioc-sig-sequencing at 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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