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Bioinformatics Advance Access published online on October 4, 2005
Bioinformatics, 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@oxfordjournals.org
Received July 14, 2005
Revised September 8, 2005
Accepted September 27, 2005

Article

Sample size determination for the false discovery rate

Stan Pounds 1* and Cheng Cheng 1

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

* To whom correspondence should be addressed.
Stan Pounds, E-mail: stanley.pounds{at}stjude.org


  Abstract

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 false discovery ratio (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 data set.

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


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