[BioC] question about lmFit model: double filtering not such a good idea?

[Christos Hatzis]

Hi Marien,

You should be safe with Limma.  That study concluded that shrinkage based
methods that use regularized versions of the standard t statistic should be
more efficient than the double filtering method (standard t-test & fold
change).  Limma uses a regularized or moderated t-statistic for individual
gene testing.  Selecting genes based on FDR would probably be the most
sensible approach for getting to the differential genes.  Further filtering
by FC would be justifiable if interested in genes differentially expressed
in one direction or have a minimum effect magnitude.

-Christos

Christos Hatzis, Ph.D.
Nuvera Biosciences, Inc.
400 West Cummings Park, Suite 5350
Woburn, MA 01801
781-938-3844



-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Koen Marien
Sent: Monday, February 01, 2010 8:10 AM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] question about lmFit model: double filtering not such a good
idea?

Hi
 
 
I'm currently using RMA-preprocessed microarray data to look for
differentially expressed genes. I used Limma (lmFit, contrast.fit, eBayes)
and topTable (adjust='FDR' = Benjamini&Hochberg I think?) and retained the
genes with logFC >2 and adjusted P-value <0.05 (=double filtering I think?),
but this appears not to be such a good idea (cfr. article)? Should I redo
the analysis now? Is it possible to use the methods explained in the article
in R?
 
 
Thanks for helping
 
 
Koen Marien
Master Student Bioscience Engineering: Cell & Gene Biotechnology
Univerity of Ghent (Belgium)
http://bene.vub.ac.be/Personal%20Pages/KM.htm
 
----------------------------------------------------------------------------
--------------------
 
This strategy is bound to be less efficient, though.
See a recent article on this subject.
 <http://www.biomedcentral.com/1471-2105/10/402>
http://www.biomedcentral.com/1471-2105/10/402
 
-Christos
 
 
Christos Hatzis, Ph.D.
Nuvera Biosciences, Inc.
400 West Cummings Park, Suite 5350
Woburn, MA 01801
781-938-3844

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