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.

Keywords:

• Bayesian false discovery rate
• Empirical Bayes
• Local false discovery rate
• Principle of maximum entropy
• Multiple comparison procedure
• Multiple testing

Acknowledgments

We thank the anonymous referee for many comments that led to improvements in clarity.

Additional information

Funding

This research was partially supported by the Natural Sciences and Engineering Research Council of Canada (RGPIN/356018-2009), by Agriculture and Agri-Food Canada (ABIP 000159B), and by the Faculty of Medicine of the University of Ottawa.