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Bioinformatics Advance Access originally published online on June 15, 2009
Bioinformatics 2009 25(16):2035-2041; doi:10.1093/bioinformatics/btp363
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© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Comments on the analysis of unbalanced microarray data

Kathleen F. Kerr

Department of Biostatistics, Box 357232, University of Washington, Seattle, WA 98195, USA


  Abstract

Motivation: Permutation testing is very popular for analyzing microarray data to identify differentially expressed (DE) genes; estimating false discovery rates (FDRs) is a very popular way to address the inherent multiple testing problem. However, combining these approaches may be problematic when sample sizes are unequal.

Results: With unbalanced data, permutation tests may not be suitable because they do not test the hypothesis of interest. In addition, permutation tests can be biased. Using biased P-values to estimate the FDR can produce unacceptable bias in those estimates. Results also show that the approach of pooling permutation null distributions across genes can produce invalid P-values, since even non-DE genes can have different permutation null distributions. We encourage researchers to use statistics that have been shown to reliably discriminate DE genes, but caution that associated P-values may be either invalid, or a less-effective metric for discriminating DE genes.

Contact: katiek{at}u.washington.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Trey Ideker

Received on February 3, 2009; revised on May 20, 2009; accepted on June 9, 2009

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