Computer applications in the biosciences · 2004 · 70 citations · 13 references
Life SciencesBioinformaticsBiostatisticsGene ExpressionGene Expression AnalysisGenomic Data AnalysisDifferential Expression AnalysisDifferential Gene Expression AnalysisGene Expression ProfilingPower AnalysisSample SizeSample Size PlanningAccuracyDifferential Gene ExpressionEffect SizeGene Expression CorrelationMicroarray AnalysisMicroarray Differential Expression AnalysisSample Size CalculationSample Size Estimation
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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Uri Alon, Naama Barkai, Daniel A. Notterman et al. · Proceedings of the National Academy of Sciences · 1999 · 4.2K citations