Journal
ANNALS OF APPLIED STATISTICS
Volume 8, Issue 1, Pages 148-175Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/13-AOAS705
Keywords
Bayesian hierarchical models; comparative genomic hybridization arrays; gene expression; hidden Markov models; measurement error; variable selection
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Funding
- NCI NIH HHS [P30 CA016672, T32 CA096520] Funding Source: Medline
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1007871] Funding Source: National Science Foundation
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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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