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Biostatistics 2005 6(1):157-169; doi:10.1093/biostatistics/kxh026
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Biostatistics Vol. 6 No. 1 © Oxford University Press 2005; all rights reserved.

Sample size calculation for multiple testing in microarray data analysis

Sin-Ho Jung

Department of Biostatistics and Bioinformatics, Duke University, Box 2716, Durham, NC 27705, USA jung0005{at}mc.duke.edu

Heejung Bang

Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA

Stanley Young

National Institute of Statistical Sciences, Research Triangle Park, NC 27709, USA

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.

Keywords: Adjusted p-value; Bonferroni; Multi-step; Permutation; Simulation; Single-step


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