Journal Article

Comments on the analysis of unbalanced microarray data

Kathleen F. Kerr

in Bioinformatics

Volume 25, issue 16, pages 2035-2041
Published in print August 2009 | ISSN: 1367-4803
Published online June 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp363
Comments on the analysis of unbalanced microarray data

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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@u.washington.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  4591 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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