False discovery rate estimation for large‐scale homogeneous discrete p‐values. Issue 2 (22nd October 2015)

• Record Type: Journal Article
• Authors: Liang, Kun
• Is Part Of: Biometrics. Volume 72:Issue 2(2016)
• Page Start: 639
• Page End: 648
• Publication Date: 2015-10-22
• Subjects: Discrete p‐values -- Dynamic adaptive methods -- Empirical processes -- False discovery rate -- Multiple testing -- Simultaneous inference; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.12429
• Languages: English
• ISSNs: 0006-341X

Abstract

Summary: 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.