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

[Gordon K Smyth]

I should have added, you can use the competitive gene set test wilcoxGST() 
from the limma package on the edgeR output, although it doesn't take 
account of inter-gene correlations.

Best wishes
Gordon

On Mon, 13 Jun 2011, 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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