Sample size for gene expression microarray experiments†The views presented in this paper are those of the authors and not necessarily representing those of the US Food and Drug Administration.

  • Authors:

  • Chen-An Tsai;Sue-Jane Wang;Dung-Tsa Chen;James J. Chen

  • Affiliations:

  • Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration Jefferson, AR 72079, USA;Division of Biometrics II, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration Rockville, MD 20857, USA;Biostatistics and Bioinformatics Unit, University of Alabama at Birmingham 153 Wallace Tumor Institute, Birmingham, AL 35294, USA;Division of Biometry and Risk Assessment, National Center for Toxicological Research, Food and Drug Administration Jefferson, AR 72079, USA

  • Venue:
  • Bioinformatics
  • Year:
  • 2005

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Abstract

Motivation: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are 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. The 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. Contact: jchen@nctr.fda.gov