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The traditional approaches to false discovery rate (FDR) control in multiplehypothesis testing are usually based on the null distribution of a teststatistic. However, all types of null distributions, including the theoretical,permutation-based and empirical ones, have some inherent drawbacks. Forexample, the theoretical null might fail because of improper assumptions on thesample distribution. Here, we propose a null distribution-free approach to FDRcontrol for multiple hypothesis testing. This approach, named target-decoyprocedure, simply builds on the ordering of tests by some statistic or score,the null distribution of which is not required to be known. Competitive decoytests are constructed from permutations of original samples and are used toestimate the false target discoveries. We prove that this approach controls theFDR when the statistics are independent between different tests. Simulationdemonstrates that it is more stable and powerful than two existing popularapproaches. Evaluation is also made on a real dataset.

Null-free False Discovery Rate Control Using Decoy Permutations