A mixture model approach to sample size estimation in two-sample comparative microarray experiments

Background: Choosing the appropriate sample size is an important step in the design of a microarray experiment, and recently methods have been proposed that estimate sample sizes for control of the False Discovery Rate (FDR). Many of these methods require knowledge of the distribution of effect sizes among the differentially expressed genes. If this distribution can be determined then accurate sample size requirements can be calculated. Results: We present a mixture model approach to estimating the distribution of effect sizes in data from two-sample comparative studies. Specifically, we present a novel, closed form, algorithm for estimating the noncentrality parameters in the test statistic distributions of differentially expressed genes. We then show how our model can be used to estimate sample sizes that control the FDR together with other statistical measures like average power or the false nondiscovery rate. Method performance is evaluated through a comparison with existing metho...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Tommy S. Jørstad, Herman Midelfart, Atle M. Bones
• Keywords: Appropriate Sample Size, BMCBI 2008, Expressed Genes, Sample Size