A hierarchical Bayesian model for inference of copy number variants and their association to gene expression
Author
Cassese, Alberto
Guindani, Michele
Tadesse, Mahlet G.
Falciani, Francesco
Vannucci, Marina
Date
2014Citation
Published Version
Abstract
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.
Keyword
Type
Journal article
Citable link to this page
http://hdl.handle.net/1911/79352Metadata
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