Significance analysis by minimizing false discovery rate

Abstract

False discovery rate (FDR) control is widely practiced to correct for multiple comparisons in selecting statistically significant features from genome-wide datasets. In this paper, we present an advanced significance analysis method called miFDR that minimizes FDR when the number of the required significant features is fixed. We compared our approach with other well-known significance analysis approaches such as Significance Analysis of Microarrays [1-3], the Benjamini-Hochberg approach [4] and the Storey approach [5]. The results of using both simulated data sets and public microarray data sets demonstrated that miFDR is more powerful.