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Bioinformatics Advance Access originally published online on October 4, 2005
Bioinformatics 2005 21(23):4263-4271; doi:10.1093/bioinformatics/bti699
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Sample size determination for the false discovery rate

Stan Pounds * and Cheng Cheng

Department of Biostatistics, St Jude Children's Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA

*To whom correspondence should be addressed.

Motivation: There is not a widely applicable method to determine the sample size for experiments basing statistical significance on the false discovery rate (FDR).

Results: We propose and develop the anticipated FDR (aFDR) as a conceptual tool for determining sample size. We derive mathematical expressions for the aFDR and anticipated average statistical power. These expressions are used to develop a general algorithm to determine sample size. We provide specific details on how to implement the algorithm for a k-group (k ≥ 2) comparisons. The algorithm performs well for k-group comparisons in a series of traditional simulations and in a real-data simulation conducted by resampling from a large, publicly available dataset.

Availability: Documented S-plus and R code libraries are freely available from www.stjuderesearch.org/depts/biostats

Contact: stanley.pounds{at}stjude.org

Supplementary information: Supplementary data are available at Bioinformatics online.

Received on July 14, 2005; revised on September 8, 2005; accepted on September 27, 2005

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