[BioC] GCRMA Fold Change

[Christos Hatzis]

The journal is BMC Bioinformatics for those interested.

-Christos 

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Stefano Calza
Sent: Tuesday, May 02, 2006 1:24 PM
To: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] GCRMA Fold Change



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
Fax: +390303717488

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