Statistics Papers

Document Type

Journal Article

Date of this Version

2009

Publication Source

The Annals of Applied Statistics

Volume

3

Issue

3

Start Page

1080

Last Page

1101

DOI

10.1214/09-AOAS241

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.

Keywords

Bayesian hypothesis testing, FDR control, hierarchical models, multiple comparisons

Share

COinS
 

Date Posted: 27 November 2017

This document has been peer reviewed.