Coconut: covariate-assisted composite null hypothesis testing with applications to replicability analysis of high-throughput experimental data.

ABSTRACT:

Background

Multiple testing of composite null hypotheses is critical for identifying simultaneous signals across studies. While it is common to incorporate external information in simple null hypotheses, exploiting such auxiliary covariates to provide prior structural relationships among composite null hypotheses and boost the statistical power remains challenging.

Results

We propose a robust and powerful covariate-assisted composite null hypothesis testing (CoCoNuT) procedure based on a Bayesian framework to identify replicable signals in two studies while asymptotically controlling the false discovery rate. CoCoNuT innovatively adopts a three-dimensional mixture model to consider two primary studies and an integrative auxiliary covariate jointly. While accounting for heter

SUBMITTER: Li Y 

PROVIDER: S-EPMC12210505 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature