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Bioinformatics Advance Access published online on April 26, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm149
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Meta-Analysis of Gene Expression Data: A Predictor-Based Approach

Irit Fishel 1, Alon Kaufman 2 and Eytan Ruppin 1,3,*

1 School of Medicine, Tel-Aviv University, Tel-Aviv 69978, Israel.
2 Interdisciplinary Center for Neural Computation, Hebrew University, Jerusalem 91904, Israel.
3 School of Computer Science, Tel-Aviv University, Tel-Aviv 69978, Israel.

*To whom correspondence should be addressed. Eytan Ruppin, E-mail: ruppin{at}post.tau.ac.il


  Abstract

Motivation: With the increasing availability of cancer microarray datasets there is a growing need for integrative computational methods that evaluate multiple independent microarray datasets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately.

Results: We propose a new meta-analysis technique that aims at finding a set of classifying genes whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray datasets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer dataset, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers.

Associate Editor: Dr. Trey Ideker

Received on November 2, 2006; revised on March 18, 2007; accepted on April 14, 2007

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