[BioC] GCRMA Fold Change

[Stefano Calza]

On Tue, May 02, 2006 at 01:02:20PM -0400, Sean Davis wrote:
<Sean>> Instead on filtering on the expression values from GCRMA, I would suggest
<Sean>> to use Affymetrix's Present/Marginal/Absent calls. You can get these with
<Sean>> the mas5calls() function in the affy library. I use a very conservative
<Sean>> filter, and only throw out genes that are "absent" on all arrays. I would
<Sean>> suggest that you do this filtering before the statistical analysis for two
<Sean>> reasons: 1) the error variances of the filtered genes are affecting your
<Sean>> Bayesian statistics and 2) removing the genes will decrease the multiple
<Sean>> test correction penalty.
<Sean>> 
<Sean>> However, even after filtering out these genes, you may still have many
<Sean>> genes with low fold-changes that are "significant". One can argue all day
<Sean>> long on whether these low fold changers are "biologically" significant or
<Sean>> not, but if you prefer to follow up first on genes with higher fold
<Sean>> changes, then by all means pick these out of your significant gene list.
<Sean>> Just make sure to document everything clearly!
<Sean>
<Sean>Just to point out one detail--if you filter too stringently (which Jenny is
<Sean>careful not to do), you will potentially lose some of the most interesting
<Sean>genes, namely those that are expressed in one group and not in another, so
<Sean>there is potentially a fine line between too much filtering and not enough.
<Sean>


A recent paper (McClintick Bioinformatics 7:49 2006) talk about this. As already pointed out filtering, too much (e.g. 100% Presence) doesn't seem a good idea.

Stefano



<Sean>Sean
<Sean>
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-- 
Stefano Calza, PhD
Researcher - Biostatistician
Sezione di Statistica Medica e Biometria
Dipartimento di Scienze Biomediche e Biotecnologie
Università degli Studi di Brescia - Italy
Viale Europa, 11 25123 Brescia
email: calza at med.unibs.it
Phone: +390303717653
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