Unsupervised empirical Bayesian multiple testing with external covariates

TL;DR

This paper introduces an unsupervised empirical Bayesian multiple testing method that leverages external covariates to improve detection power and accuracy in hypothesis testing.

Contribution

It presents a novel covariate-informed Bayesian testing approach with a fast approximation algorithm, enhancing hypothesis detection in complex data.

Findings

Increased power in detecting true positives.

Different hypothesis lists compared to traditional methods.

Effective application to eQTL data.

Abstract

In an empirical Bayesian setting, we provide a new multiple testing method, useful when an additional covariate is available, that influences the probability of each null hypothesis being true. We measure the posterior significance of each test conditionally on the covariate and the data, leading to greater power. Using covariate-based prior information in an unsupervised fashion, we produce a list of significant hypotheses which differs in length and order from the list obtained by methods not taking covariate-information into account. Covariate-modulated posterior probabilities of each null hypothesis are estimated using a fast approximate algorithm. The new method is applied to expression quantitative trait loci (eQTL) data.