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

TL;DR

This paper introduces a covariate-adaptive FDR control method for large-scale inference, improving power and robustness in omics data analysis by incorporating covariate information, with proven asymptotic validity and efficient implementation.

Contribution

It develops a novel covariate-adaptive FDR procedure with a fast algorithm, validated theoretically and demonstrated to outperform existing methods in simulations and real omics data.

Findings

Improved power over existing methods in simulations

Robustness to model misspecification and dependence

Most powerful method in sparse signal scenarios

Abstract

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 paper, 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 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.