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Bioinformatics Advance Access originally published online on June 26, 2009
Bioinformatics 2009 25(17):2216-2221; doi:10.1093/bioinformatics/btp385
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© 2009 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.

Error control variability in pathway-based microarray analysis

David L. Gold 1,2,*, Jeffrey C. Miecznikowski 1,2 and Song Liu 1,2

1 Department of Biostatistics, Roswell Park Cancer Institute and 2 Department of Biostatistics, University at Buffalo, The State University of New York, Buffalo, NY, USA

* To whom correspondence should be addressed.


  Abstract

Motivation: The decision to commit some or many false positives in practice rests with the investigator. Unfortunately, not all error control procedures perform the same. Our problem is to choose an error control procedure to determine a P-value threshold for identifying differentially expressed pathways in high-throughput gene expression studies. Pathway analysis involves fewer tests than differential gene expression analysis, on the order of a few hundred. We discuss and compare methods for error control for pathway analysis with gene expression data.

Results: In consideration of the variability in test results, we find that the widely used Benjamini and Hochberg's (BH) false discovery rate (FDR) analysis is less robust than alternative procedures. BH's error control requires a large number of hypothesis tests, a reasonable assumption for differential gene expression analysis, though not the case with pathway-based analysis. Therefore, we advocate through a series of simulations and applications to real gene expression data that researchers control the number of false positives rather than the FDR.

Availability: Our R package, EPath.omg is available at http://sphhp.buffalo.edu/biostat/research/software.

Contact: dlgold{at}buffalo.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Martin Bishop

Received on March 19, 2009; revised on May 22, 2009; accepted on June 11, 2009

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