Title

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

Campus Units

Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

12-2010

Journal or Book Title

Journal of the American Statistical Association

Volume

105

Issue

492

First Page

1444

Last Page

1445

DOI

10.1198/jasa.2010.tm10195

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.

Comments

This is a manuscript of an article published as Liang, Kun, and Dan Nettleton. "A hidden Markov model approach to testing multiple hypotheses on a tree-transformed gene ontology graph." Journal of the American Statistical Association 105, no. 492 (2010): 1444-1454. doi: 10.1198/jasa.2010.tm10195. Posted with permission.

Copyright Owner

American Statistical Association

Language

en

File Format

application/pdf

Published Version

Share

COinS