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Bioinformatics Advance Access published online on December 19, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn650
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Matching Methods for Observational Microarray Studies

Ruth Heller 1,*, Elisabetta Manduchi 2 and Dylan Small 1,*

1Department of Statistics, Wharton School, University of Pennsylvania, Philadelphia, PA 19104-6340
2Computational Biology and Informatics Laboratory, Center for Bioinformatics, University of Pennsylvania, Philadelphia, PA 19104-6021

*To whom correspondence should be addressed. Dr. Ruth Heller, E-mail: ruheller{at}wharton.upenn.edu


  Abstract

Motivation: We address the problem of identifying differentially expressed genes between two conditions in the scenario where the data arise from an observational study, in which confounding factors are likely to be present.

Results: We suggest to use matching methods to balance two groups of observed cases on measured covariates, and to identify differentially expressed genes using a test suited to matched data. We illustrate this approach on 2 microarray studies: the first study consists of data from patients with two cancer subtypes, and the second study consists of data from AMKL patients with and without Down syndrome.

Availability: R code (www.r-project.org) for implementing our approach is included as supplementary material.

Contact: ruheller{at}wharton.upenn.edu

Associate Editor: Dr. Joaquin Dopazo

Received on July 9, 2008; revised on December 8, 2008; accepted on December 17, 2008

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