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Bioinformatics Advance Access originally published online on January 19, 2007
Bioinformatics 2007 23(6):739-746; doi:10.1093/bioinformatics/btl664
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Quick calculation for sample size while controlling false discovery rate with application to microarray analysis

Peng Liu 1,2,* and J. T. Gene Hwang 3,4

1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA, 2Department of Statistics, Iowa State University, Ames, IA 50011, USA, 3Department of Mathematics and Department of Statistical Science, Cornell University, Ithaca, NY 14853, USA and 4Department of Statistics, National Cheng Kung University, Tainan, Taiwan

*To whom correspondence should be addressed.


  Abstract

Motivation: Sample size calculation is important in experimental design and is even more so in microarray or proteomic experiments since only a few repetitions can be afforded. In the multiple testing problems involving these experiments, it is more powerful and more reasonable to control false discovery rate (FDR) or positive FDR (pFDR) instead of type I error, e.g. family-wise error rate (FWER). When controlling FDR, the traditional approach of estimating sample size by controlling type I error is no longer applicable.

Results: Our proposed method applies to controlling FDR. The sample size calculation is straightforward and requires minimal computation, as illustrated with two sample t-tests and F-tests. Based on simulation with the resultant sample size, the power is shown to be achievable by the q-value procedure.

Availability: A Matlab code implementing the described methods is available upon request.

Contact: pliu{at}iastate.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

Received on October 12, 2005; revised on December 11, 2006; accepted on December 26, 2006

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