Bayesian testing of many hypotheses $\times$ many genes: A study of sleep apnea

TL;DR

This paper introduces a hierarchical Bayesian method for simultaneously testing multiple hypotheses across many genes, specifically applied to sleep apnea gene expression data, improving detection of differential expression in complex experimental designs.

Contribution

It develops a hierarchical Bayesian framework to handle multiple hypotheses within and across genes, addressing complex experimental designs in gene expression studies.

Findings

Effective identification of differentially expressed genes in sleep apnea.

Improved control of false discoveries in multi-hypothesis testing.

Demonstrates the method's applicability to real biological data.

Abstract

Substantial statistical research has recently been devoted to the analysis of large-scale microarray experiments which provide a measure of the simultaneous expression of thousands of genes in a particular condition. A typical goal is the comparison of gene expression between two conditions (e.g., diseased vs. nondiseased) to detect genes which show differential expression. Classical hypothesis testing procedures have been applied to this problem and more recent work has employed sophisticated models that allow for the sharing of information across genes. However, many recent gene expression studies have an experimental design with several conditions that requires an even more involved hypothesis testing approach. In this paper, we use a hierarchical Bayesian model to address the situation where there are many hypotheses that must be simultaneously tested for each gene. In addition to having many hypotheses within each gene, our analysis also addresses the more typical multiple comparison issue of testing many genes simultaneously. We illustrate our approach with an application to a study of genes involved in obstructive sleep apnea in humans.