Journal Article

Error control variability in pathway-based microarray analysis

David L. Gold, Jeffrey C. Miecznikowski and Song Liu

in Bioinformatics

Volume 25, issue 17, pages 2216-2221
Published in print September 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI:

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology


Show Summary Details


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


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4299 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.