Power and sample size estimation in microarray studies.

BMC Bioinformatics
Wei-Jiun LinJames J Chen

Abstract

Before conducting a microarray experiment, one important issue that needs to be determined is the number of arrays required in order to have adequate power to identify differentially expressed genes. This paper discusses some crucial issues in the problem formulation, parameter specifications, and approaches that are commonly proposed for sample size estimation in microarray experiments. Common methods for sample size estimation are formulated as the minimum sample size necessary to achieve a specified sensitivity (proportion of detected truly differentially expressed genes) on average at a specified false discovery rate (FDR) level and specified expected proportion (pi1) of the true differentially expression genes in the array. Unfortunately, the probability of detecting the specified sensitivity in such a formulation can be low. We formulate the sample size problem as the number of arrays needed to achieve a specified sensitivity with 95% probability at the specified significance level. A permutation method using a small pilot dataset to estimate sample size is proposed. This method accounts for correlation and effect size heterogeneity among genes. A sample size estimate based on the common formulation, to achieve the desire...Continue Reading

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Citations

Jul 23, 2011·Nature Reviews. Molecular Cell Biology·Rafael Garcia-MataKeith Burridge
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Jan 7, 2011·BMC Bioinformatics·Mohammed DaknaMark Girolami
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