A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Gene Ontology Graph

Thumbnail Image
Date
2009-08-01
Authors
Liang, Kun
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract

Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes. We developed a method based on a hidden Markov model that takes the whole graph into account and provides coherent decisions in this structured multiple hypothesis testing problem. The method is illustrated by testing Gene Ontology terms for evidence of differential expression.

Series Number
Journal Issue
Is Version Of
Versions
Series
Academic or Administrative Unit
Type
article
Comments

This preprint was published as Kun Liang & Dan Nettleton, "A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph", Journal of the American Statistical Association (2010): 1444-1454, doi: 10.1198/jasa.2010.tm10195.

Rights Statement
Copyright
Funding
Subject Categories
DOI
Supplemental Resources
Source
Collections