• FAQ
    • Deposit your work
    • Login
    View Item 
    •   Rice Scholarship Home
    • Faculty & Staff Research
    • Faculty Publications
    • View Item
    •   Rice Scholarship Home
    • Faculty & Staff Research
    • Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A hierarchical Bayesian model for inference of copy number variants and their association to gene expression

    Thumbnail
    Name:
    nihms558750.pdf
    Size:
    1.785Mb
    Format:
    PDF
    View/Open
    Author
    Cassese, Alberto
    Guindani, Michele
    Tadesse, Mahlet G.
    Falciani, Francesco
    Vannucci, Marina
    Date
    2014
    Citation
    Cassese, Alberto, Guindani, Michele, Tadesse, Mahlet G., et al.. "A hierarchical Bayesian model for inference of copy number variants and their association to gene expression." The Annals of Applied Statistics, 8, no. 1 (2014) 148-175. http://dx.doi.org/10.1214/13-AOAS705.
    Published Version
    http://dx.doi.org/10.1214/13-AOAS705
    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
    Bayesian hierarchical models; comparative genomic hybridization arrays; gene expression; hidden Markov models; measurement error; More... variable selection Less...
    Type
    Journal article
    Citable link to this page
    http://hdl.handle.net/1911/79352
    Metadata
    Show full item record
    Collections
    • Faculty Publications [2827]
    • Statistics Publications [81]

    Home | FAQ | Contact Us
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
     

     

    Browse

    Entire ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeThis CollectionBy Issue DateAuthorsTitlesSubjectsType

    My Account

    Login

    Statistics

    View Usage Statistics

    Home | FAQ | Contact Us
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892