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Nucleic Acids Research Advance Access published online on August 15, 2007
Nucleic Acids Research, doi:10.1093/nar/gkm537
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

Filtering genes to improve sensitivity in oligonucleotide microarray data analysis

Stefano Calza1,2, Wolfgang Raffelsberger3, Alexander Ploner1, Jose Sahel4, Thierry Leveillard4 and Yudi Pawitan1,*

1Department of Medical Epidemiology and Biostatistics - Karolinska Institute, Stockholm, Sweden, 2Section of Medical Statistics and Biometry, Department of Biomedical Sciences and Biotechnology - University of Brescia, Italy, 3Laboratoire de Bioinformatique et Génomique Intégratives, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch Strasbourg, France and 4Laboratoire de Physiopathologie Cellulaire et Moléculaire de la Retine - Faculté de Médecine, Université Pierre et Marie Curie, Paris, France

*To whom correspondence should be addressed. Tel: +46-8-5248 3983; Fax: +46-8-314 975; Email: yudi.pawitan{at}ki.se

Received March 27, 2007. Revised June 29, 2007. Accepted July 3, 2007.

Many recent microarrays hold an enormous number of probe sets, thus raising many practical and theoretical problems in controlling the false discovery rate (FDR). Biologically, it is likely that most probe sets are associated with un-expressed genes, so the measured values are simply noise due to non-specific binding; also many probe sets are associated with non-differentially-expressed (non-DE) genes. In an analysis to find DE genes, these probe sets contribute to the false discoveries, so it is desirable to filter out these probe sets prior to analysis. In the methodology proposed here, we first fit a robust linear model for probe-level Affymetrix data that accounts for probe and array effects. We then develop a novel procedure called FLUSH (Filtering Likely Uninformative Sets of Hybridizations), which excludes probe sets that have statistically small array-effects or large residual variance. This filtering procedure was evaluated on a publicly available data set from a controlled spiked-in experiment, as well as on a real experimental data set of a mouse model for retinal degeneration. In both cases, FLUSH filtering improves the sensitivity in the detection of DE genes compared to analyses using unfiltered, presence-filtered, intensity-filtered and variance-filtered data. A freely-available package called FLUSH implements the procedures and graphical displays described in the article.


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