[BioC] RMA normalization and MAS5.0 detection calls

[Jenny Drnevich]

Hi all,

Actually, the problem of the statistical results not always making sense 
with the P/A calls also happens with MAS5 values, not RMA or GCRMA 
specifically. Many years ago I as using Affy's two sample comparison in the 
MAS 5.0 software, and I noticed that a probeset would be called "A" in 
sample 1 and "P" in sample 2, but supposedly sample 2 had higher expression 
than sample 1!!  The calls and the expression level comparisons sometimes 
don't correspond, but this is because they use different algorithms and 
values in their computation, as Jim explained. I tend to like and use the 
calls in a conservative matter, but they may only be about 85% accurate 
(Choe et al. Genome Biology 2005, 6:R16).

>Another way to approach filtering probesets is based on the variability
>of the probesets over all samples. If the variance is low (below some
>constant c), then you might assume that the gene is not differentially
>expressed in any samples (which is different than saying it is expressed
>or not). These genes are uninteresting by definition, and can be removed
>from the dataset.

I still haven't convinced myself that I like this approach. And wouldn't it 
be better to filter on CV, which takes into account expression level, 
rather than variance? I know there was a recent exchange on what sort of 
cutoff value to use... I really need to find the time to play around with 
filtering on some aspect of variability - unless something has been 
published on it?

Cheers,
Jenny




>HTH,
>
>Jim
>
>
> >
> >
> > Regards!
> >
> >
> > haiyan
> >
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>James W. MacDonald, M.S.
>Biostatistician
>Affymetrix and cDNA Microarray Core
>University of Michigan Cancer Center
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

330 ERML
1201 W. Gregory Dr.
Urbana, IL 61801
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e-mail: drnevich at uiuc.edu