[Bioc-sig-seq] roast/romer for count data (edgeR)?

[Cei Abreu-Goodger]

Thanks Gordon for the suggestions...

On 6/12/11 6:47 PM, Gordon K Smyth wrote:
> 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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