[BioC] combining GSA and lmFit

[Gordon K Smyth]

Dear Dick,

Anything in GSA which works with the SAM statistic should also work fine 
with limma moderated t-statistics.

However there are several issues that come to my mind which affect both 
statistics.  Firstly, both SAM and limma statistics depend on the whole 
ensemble of genes, i.e., they are not merely computed genewise.  This is 
unlike the floored mean statistics assumed in the GSA theory paper.  This 
has clear computational implications, but also could give rise to some 
theoretical issues.

Secondly, it's not too clear to me whether it makes sense to compute 
regularized or moderated statistics after the standardization steps that 
GSA does.

Thirdly, GSA computes p-values from permuation, and permutation does not 
perform well for linear models.

These are simply my thoughts, which you asked for.  You may have ways 
around all these issues.

Best wishes
Gordon

> Date: Sun, 26 Apr 2009 20:43:28 -0700 (PDT)
> From: Dick Beyer <dbeyer at u.washington.edu>
> Subject: [BioC] combining GSA and lmFit
> To: Bioconductor <bioconductor at stat.math.ethz.ch>
>
> Hi All,
>
> I have extended the GSA code (http://www-stat.stanford.edu/~tibs/GSA/) 
> to include lmFit() from the limma package so as to have linear model 
> capabilities with GSA.  Basically, I'm using the modified t-statistic 
> values from lmFit just like the SAM-like t-statistic values are used in 
> the GSA code.
>
> I was wondering if anyone had any thoughts on whether this was, in 
> principle, an OK thing to be doing.  I am worrying about whether there 
> is an underlying issue I'm not aware of in using the moderated 
> t-statistic values from limma as opposed to the SAM t-statistic values 
> that uses the s0 term in the denominator.
>
> My tests on some microarray data I have shows that in a qqplot of 
> t-statistic values from the two methods, they are in pretty close 
> agreement except for large values of the t-values.
>
> If anyone knows of reasons not to be doing this or could point me to 
> places with possible explanations, I'd be very grateful.
>
> Cheers,
> Dick
>
> *******************************************************************************
> Richard P. Beyer, Ph.D.	University of Washington
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