[BioC] edgeR for entirely individual gene wise dispersion

[Gordon K Smyth]

Dear Gu,

Yes, you can set prior.df=0 to get genewise dispersion estimates without 
squeezing, although this not recommended except perhaps as a diagnostic.

In the formula G_0=20/df in the paper, df is the residual df and 
prior.df=20.  So

   prior.df = G_0 * df.residual

Best wishes
Gordon

---------------------------------------------
Professor Gordon K Smyth,
Bioinformatics Division,
Walter and Eliza Hall Institute of Medical Research,
1G Royal Parade, Parkville, Vic 3052, Australia.
http://www.statsci.org/smyth

> Date: Sat, 27 Apr 2013 23:46:20 -0700
> From: Gu Mi <neo.migu at gmail.com>
> To: bioconductor at r-project.org
> Subject: [BioC] edgeR for entirely individual gene wise dispersion
>
> Dear All:
>
> I am using edgeR for RNA-Seq data analysis. According to the paper 
> "Differential expression analysis of multifactor RNA-Seq experiments 
> with respect to biological variation" by McCarthy, DJ et al. (2012), the 
> tagwise dispersion model is recommended. It is a compromise between 
> entirely individual genewise dispersion and the common dispersion 
> models.
>
> My question is, is it possible to obtain a "pure genewise" model, i.e. 
> there is NO shrinkage applied towards the common/trended dispersion? I 
> think what I mean is to estimate \phi by maximizing APL_g (\phi) + G_0 * 
> APL_s (\phi) where G_0 is set to zero (no shared part). Which argument 
> in the estimateGLMTagwiseDisp function shall I change? Can I set 
> prior.df = 0 to get a purely genewise model without any shrinkage? In 
> the paper, G_0 = 20/df, but I am not sure if this "df" in the 
> denominator is the prior.df argument in R, or something else.
>
> Thank you very much!
>
> Best,
> Gu
>

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