Open Access
March 2014 A hierarchical Bayesian model for inference of copy number variants and their association to gene expression
Alberto Cassese, Michele Guindani, Mahlet G. Tadesse, Francesco Falciani, Marina Vannucci
Ann. Appl. Stat. 8(1): 148-175 (March 2014). DOI: 10.1214/13-AOAS705


A number of statistical models have been successfully developed for the analysis of high-throughput data from a single source, but few methods are available for integrating data from different sources. Here we focus on integrating gene expression levels with comparative genomic hybridization (CGH) array measurements collected on the same subjects. We specify a measurement error model that relates the gene expression levels to latent copy number states which, in turn, are related to the observed surrogate CGH measurements via a hidden Markov model. We employ selection priors that exploit the dependencies across adjacent copy number states and investigate MCMC stochastic search techniques for posterior inference. Our approach results in a unified modeling framework for simultaneously inferring copy number variants (CNV) and identifying their significant associations with mRNA transcripts abundance. We show performance on simulated data and illustrate an application to data from a genomic study on human cancer cell lines.


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Alberto Cassese. Michele Guindani. Mahlet G. Tadesse. Francesco Falciani. Marina Vannucci. "A hierarchical Bayesian model for inference of copy number variants and their association to gene expression." Ann. Appl. Stat. 8 (1) 148 - 175, March 2014.


Published: March 2014
First available in Project Euclid: 8 April 2014

zbMATH: 06302231
MathSciNet: MR3191986
Digital Object Identifier: 10.1214/13-AOAS705

Keywords: Bayesian hierarchical models , comparative genomic hybridization arrays , gene expression , Hidden Markov models , measurement error , Variable selection

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 1 • March 2014
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