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Bioinformatics Advance Access published online on January 19, 2007
Bioinformatics, 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 a,b,* and J. T. Gene Hwang c

aDepartment of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA
bDepartment of Statistics, Iowa State University, Ames, IA 50011, USA
cDepartment of Mathematics and Department of Statistical Science, Cornell University, Ithaca, NY 14853, USA

* to whom correspondence should be addressed. Peng Liu, E-mail: pliu{at}iastate.edu


  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) (Storey and Tibshirani, 2003). When controlling FDR, the traditional approach of estimating sample size by controlling type I error is no longer applicable.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 of Storey, Taylor and Siegmund (2004).

Availability: MotifRank is available from http://bio.dlg.cn

Associate Editor: Joaquin Dopazo

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

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