[BioC] Understanding limma, fdr and topTable

aaron.j.mackey at gsk.com aaron.j.mackey at gsk.com
Wed Jul 9 15:24:27 CEST 2008

Kevin, thanks for the clarification, that was, in fact, exactly what I 


> James MacDonald wrote:
> > aaron.j.mackey at gsk.com wrote:
> >> This doesn't make sense.  How can I choose to filter out "unchanged" 
> >> probesets without fitting a model of some sort, and making a 
> >> probabilistic decision for each probeset about whether it is 
> >> "unchanged" or not.  Every probeset (save those below the detection 
> >> limit) will exhibit variance (though the variance may be below the 
> >> precision of the instrument to measure), right?  You're not 
> >> that there are some probesets with zero variance?
> > 
> > I don't really understand your point here. First, I never suggested 
> > fitting a model of any kind to select unchanged probesets, unless 
> > computing the variance is some kind of newfangled model fitting that I 

> > don't understand.
> When you compute the variance and decide to eliminate probes from 
> consideration if the variance is below some value, you are performing a 
> statistical test. Implicitly, you are assuming a vague sort of model 
> that suggests that "if the variance is small enough, then the gene 
> cannot be differentially expressed".  This does not mean that this 
> particular statistical test is either efficient or powerful. But it is, 
> nevertheless, a test of differential expression, and so should not 
> really be ignored when accounting for multiple testing.
>    -- Kevin

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