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A Mixture Copula Bayesian Network Model for Multimodal Genomic Data

Qingyang Zhang, Xuan Shi
doi: https://doi.org/10.1101/110288
Qingyang Zhang
1Department of Mathematical Sciences, University of Arkansas, Fayetteville, AR 72701
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  • For correspondence: qz008@uark.edu
Xuan Shi
2Department of Geosciences University of Arkansas, Fayetteville, AR 72701
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Abstract

Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when normality assumption is moderately or severely violated, making it unsuitable to deal with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine Expectation-Maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling dataset. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.

  • Abbreviations

    TCGA
    The Cancer Genome Atlas
    BN
    Bayesian network
    GBN
    Gaussian Bayesian network
    CBN
    Copula Bayesian network
    MCBN
    Mixture copula Bayesian network
    EM
    Expectation-Maximization
    BIC
    Bayesian information criterion
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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    Posted February 22, 2017.
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    A Mixture Copula Bayesian Network Model for Multimodal Genomic Data
    Qingyang Zhang, Xuan Shi
    bioRxiv 110288; doi: https://doi.org/10.1101/110288
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    A Mixture Copula Bayesian Network Model for Multimodal Genomic Data
    Qingyang Zhang, Xuan Shi
    bioRxiv 110288; doi: https://doi.org/10.1101/110288

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