Correcting false discovery rates for their bias toward false positives

Abstract

The way false discovery rates (FDRs) are used in the analysis of genomics data leads to excessive false positive rates. In this sense, FDRs overcorrect for the excessive conservatism (bias toward false negatives) of methods of adjusting p values that control a family-wise error rate. Estimators of the local FDR (LFDR) are much less biased but have not been widely adopted due to their high variance and lack of availability in software. To address both issues, we propose estimating the LFDR by correcting an estimated FDR or the level at which an FDR is controlled.