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Bioinformatics Advance Access originally published online on July 23, 2009
Bioinformatics 2009 25(20):2692-2699; doi:10.1093/bioinformatics/btp444
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Moderated effect size and P-value combinations for microarray meta-analyses

Guillemette Marot 1,*, Jean-Louis Foulley 1, Claus-Dieter Mayer 2 and Florence Jaffrézic 1

1 INRA, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, F-78350, France and 2 Biomathematics and Statistics, Rowett Institute of Nutrition and Health, University of Aberdeen, Scotland, UK

* To whom correspondence should be addressed.


  Abstract

Motivation: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results.

Results: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative.

Availability: An R package metaMA is available on the CRAN.

Contact: guillemette.marot{at}jouy.inra.fr

Associate Editor: David Rocke

Received on March 6, 2009; revised on June 25, 2009; accepted on July 7, 2009

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