False discovery rate estimation for large-scale homogeneous discrete p-values.
Large-scale homogeneous discrete p-values are encountered frequently in high-throughput genomics studies, and the related multiple testing problems become challenging because most existing methods for the false discovery rate (FDR) assume continuous p-values. In this article, we study the estimation of the null proportion and FDR for discrete p-values with common support. In the finite sample setting, we propose a novel class of conservative FDR estimators. Furthermore, we show that a broad class of FDR estimators is simultaneously conservative over all support points under some weak dependence condition in the asymptotic setting. We further demonstrate the significant improvement of a newly proposed method over existing methods through simulation studies and a case study.
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Kun
K
Liang
D001172:Arthritis, Rheumatoid / Q000235:genetics / Q000473:pathology; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D015870:Gene Expression / Q000187:drug effects; D020869:Gene Expression Profiling; D006801:Humans; D015233:Models, Statistical; D014409:Tumor Necrosis Factor-alpha / Q000494:pharmacology
C439524:TNF protein, human; D014409:Tumor Necrosis Factor-alpha
Discrete p-values; Dynamic adaptive methods; Empirical processes; False discovery rate; Multiple testing; Simultaneous inference