Sample Size Determination With False Discovery Rate Adjustment for Experiments With High-Dimensional Data

In high-dimensional data analyses, such as in microarray experiments, the false discovery rate (FDR) has been widely used as an appropriate method to control false positive error rate, and some progress has been made on the issue of sample size calculation. However, there is still lack of a simple and practically useful method for routine use. This article investigates the power and the related problem of sample size determination methods for current FDR controlling procedures under a mixture model involving independent test statistics. An approach is proposed where one can use traditional sample size calculation for a single hypothesis with appropriately adjusted Type I error rate. This adjustment is based on a simple relationship between the desired FDR and power level and the individual Type I error rate. Simulation results show that our approach can be applied successfully under both an independence assumption and certain commonly used correlation structures.