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A weighted FDR procedure under discrete and heterogeneous null distributions
Author(s) -
Chen Xiongzhi,
Doerge R. W.,
Sarkar Sanat K.
Publication year - 2020
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201900216
Subject(s) - false discovery rate , multiple comparisons problem , mathematics , inference , independence (probability theory) , null hypothesis , statistical hypothesis testing , nominal level , statistics , p value , negative binomial distribution , algorithm , computer science , poisson distribution , artificial intelligence , confidence interval , biochemistry , chemistry , gene
Multiple testing (MT) with false discovery rate (FDR) control has been widely conducted in the “discrete paradigm” where p ‐values have discrete and heterogeneous null distributions. However, in this scenario existing FDR procedures often lose some power and may yield unreliable inference, and for this scenario there does not seem to be an FDR procedure that partitions hypotheses into groups, employs data‐adaptive weights and is nonasymptotically conservative. We propose a weighted p ‐value‐based FDR procedure, “weighted FDR (wFDR) procedure” for short, for MT in the discrete paradigm that efficiently adapts to both heterogeneity and discreteness of p ‐value distributions. We theoretically justify the nonasymptotic conservativeness of the wFDR procedure under independence, and show via simulation studies that, for MT based on p ‐values of binomial test or Fisher's exact test, it is more powerful than six other procedures. The wFDR procedure is applied to two examples based on discrete data, a drug safety study, and a differential methylation study, where it makes more discoveries than two existing methods.