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Bioinformatics Advance Access published online on November 25, 2004
Bioinformatics, doi:10.1093/bioinformatics/bti162
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received October 28, 2004
Revised November 16, 2004
Accepted November 17, 2004

Article

Sample size for gene expression microarray experiments*

Chen-An Tsai 1, Sue-Jane Wang 2, Dung-Tsa Chen 3, and James J. Chen 1*

1 Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079
2 Division of Biometrics II, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Rockville, Maryland 20857
3 Biostatistics and Bioinformatics Unit, University of Alabama at Birmingham, 153 Wallace Tumor Institute, Birmingham, Alabama 35294

* To whom correspondence should be addressed.
James J. Chen, E-mail: jchen{at}nctr.fda.gov


  Abstract

Motivation: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes is expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery, and accuracy rates. Sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size.

Results: We present a general approach for estimating sample size under independent and equally-correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays.

* The views presented in this paper are those of authors and not necessarily representing those of the U.S. Food and Drug Administration


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