Covariate Adaptive False Discovery Rate Control With Applications to Omics-Wide Multiple Testing

Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the patterns of signals and nulls are available. In this article, we introduce an FDR control procedure in la...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2022-01, Vol.117 (537), p.411-427
Main Authors: Zhang, Xianyang, Chen, Jun
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Asymptotic methods
Covariates
EM-algorithm
False discovery rate
Genomics
Likelihood ratio
Multiple testing
Phenotypes
Regression analysis
Statistical methods
Statistics
Test procedures
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Summary:Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the patterns of signals and nulls are available. In this article, we introduce an FDR control procedure in large-scale inference problem that can incorporate covariate information. We develop a fast algorithm to implement the proposed procedure and prove its asymptotic validity even when the underlying likelihood ratio model is misspecified and the p-values are weakly dependent (e.g., strong mixing). Extensive simulations are conducted to study the finite sample performance of the proposed method and we demonstrate that the new approach improves over the state-of-the-art approaches by being flexible, robust, powerful, and computationally efficient. We finally apply the method to several omics datasets arising from genomics studies with the aim to identify omics features associated with some clinical and biological phenotypes. We show that the method is overall the most powerful among competing methods, especially when the signal is sparse. The proposed covariate adaptive multiple testing procedure is implemented in the R package CAMT. Supplementary materials for this article are available online.
ISSN:0162-1459
1537-274X
DOI:10.1080/01621459.2020.1783273