THE GIBBS-PLAID BICLUSTERING MODEL

• 成果类型: Article
• 署名作者: Chekouo, Thierry; Murua, Alejandro; Raffelsberger, Wolfgang
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; Universite de Montreal; Universites de Strasbourg Etablissements Associes; Universite de Strasbourg; Institut National de la Sante et de la Recherche Medicale (Inserm); Centre National de la Recherche Scientifique (CNRS); Universite de Lorraine; Centre National de la Recherche Scientifique (CNRS); Institut National de la Sante et de la Recherche Medicale (Inserm); Universites de Strasbourg Etablissements Associes; Universite de Strasbourg
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/15-AOAS854
• 发表日期: 2015
• 页码: 1643-1670
• 关键词: gene-expression; microarray data; monte-carlo; INFORMATION; algorithm; network

摘要

We propose and develop a Bayesian plaid model for biclustering that accounts for the prior dependency between genes (and/or conditions) through a stochastic relational graph. This work is motivated by the need for improved understanding of the molecular mechanisms of human diseases for which effective drugs are lacking, and based on the extensive raw data available through gene expression profiling. We model the prior dependency information from biological knowledge gathered from gene ontologies. Our model, the Gibbs-plaid model, assumes that the relational graph is governed by a Gibbs random field. To estimate the posterior distribution of the bicluster membership labels, we develop a stochastic algorithm that is partly based on the Wang-Landau flat-histogram algorithm. We apply our method to a gene expression database created from the study of retinal detachment, with the aim of confirming known or finding novel subnetworks of proteins associated with this disorder.