Null-free False Discovery Rate Control Using Decoy Permutations  

作　　者：Kun He Mengjie Li Yan Fu Fuzhou Gong Xiaoming Sun 

机构地区：[1]Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China [2]CEMS,NCMIS,RCSDS,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100190,China [3]University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《Acta Mathematicae Applicatae Sinica》2022年第2期235-253,共19页应用数学学报（英文版）

基　　金：supported by the National Key R&D Program of China(No.2018YFB0704304);the National Natural Science Foundation of China(Nos.32070668,62002231,61832003,61433014);the K.C.Wong Education Foundation。

摘　　要：The traditional approaches to false discovery rate(FDR)control in multiple hypothesis testing are usually based on the null distribution of a test statistic.However,all types of null distributions,including the theoretical,permutation-based and empirical ones,have some inherent drawbacks.For example,the theoretical null might fail because of improper assumptions on the sample distribution.Here,we propose a null distributionfree approach to FDR control for multiple hypothesis testing in the case-control study.This approach,named target-decoy procedure,simply builds on the ordering of tests by some statistic or score,the null distribution of which is not required to be known.Competitive decoy tests are constructed from permutations of original samples and are used to estimate the false target discoveries.We prove that this approach controls the FDR when the score function is symmetric and the scores are independent between different tests.Simulation demonstrates that it is more stable and powerful than two popular traditional approaches,even in the existence of dependency.Evaluation is also made on two real datasets,including an arabidopsis genomics dataset and a COVID-19 proteomics dataset.

关 键 词：multiple testing false discovery rate null distribution-free p-value-free decoy permutations knockoff filter 

分 类 号：O231[理学—运筹学与控制论]