Sample size for gene expression microarray experiments.

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

doi:10.1093/bioinformatics/bti162

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Sample size for gene expression microarray experiments.

Chen-An Tsai·Sue-Jane Wang·Dung-Tsa Chen·James J Chen

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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.

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Algorithms Computer Simulation DNA Gene Expression Profiling Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Sample Size Sequence Analysis, DNA Evaluation Study Journal Article

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BibTeX RIS

Chen-An Tsai, Sue-Jane Wang, Dung-Tsa Chen, James J Chen. 2004-11-25. Sample size for gene expression microarray experiments.. https://doi.org/10.1093/bioinformatics%2Fbti162

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