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Weighted False Discovery Rate Control in Large-Scale Multiple Testing.

ABSTRACT: The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domain knowledge enhances the interpretability of results and precision of inference. Simulation studies show that the proposed method controls the error rate at the nominal level, and the gain in power over existing methods is substantial in many settings. An application to a genome-wide association study is discussed.

SUBMITTER: Basu P

PROVIDER: S-EPMC6474384 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Weighted False Discovery Rate Control in Large-Scale Multiple Testing.

Basu Pallavi P Cai T Tony TT Das Kiranmoy K Sun Wenguang W

Journal of the American Statistical Association 20180612 523

The use of weights provides an effective strategy to incorporate prior domain knowledge in large-scale inference. This paper studies weighted multiple testing in a decision-theoretic framework. We develop oracle and data-driven procedures that aim to maximize the expected number of true positives subject to a constraint on the weighted false discovery rate. The asymptotic validity and optimality of the proposed methods are established. The results demonstrate that incorporating informative domai ...[more]

PMID: 31011234

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