Probabilistic Model Incorporating Auxiliary Covariates to Control FDR

TL;DR

This paper introduces NeurT-FDR, a deep learning-based method that incorporates auxiliary covariates to improve FDR control and increase discoveries in multiple hypothesis testing.

Contribution

It proposes a novel neural network framework that effectively integrates auxiliary covariates, enhancing power and FDR control in large-scale testing scenarios.

Findings

NeurT-FDR outperforms existing methods in real datasets.

The method effectively handles high-dimensional features.

It achieves more discoveries while controlling FDR.

Abstract

Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods rely on the test-level covariates while ignoring metrics about test-level covariates. This strategy may not be optimal for complex large-scale problems, where indirect relations often exist among test-level covariates and auxiliary metrics or covariates. We incorporate auxiliary covariates among test-level covariates in a deep Black-Box framework controlling FDR (named as NeurT-FDR) which boosts statistical power and controls FDR for multiple-hypothesis testing. Our method parametrizes the test-level covariates as a neural network and adjusts the auxiliary covariates through a regression framework, which enables flexible handling of high-dimensional features as well as efficient end-to-end optimization. We show that NeurT-FDR makes substantially more discoveries in three real datasets compared to competitive baselines.