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Network-based genomic discovery: application and comparison of Markov random-field models

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  • Peng Wei
  • Wei Pan

Abstract

As biological knowledge accumulates rapidly, gene networks encoding genomewide gene-gene interactions have been constructed. As an improvement over the standard mixture model that tests all the genes identically and independently distributed "a priori", Wei and co-workers have proposed modelling a gene network as a discrete or Gaussian Markov random field (MRF) in a mixture model to analyse genomic data. However, how these methods compare in practical applications is not well understood and this is the aim here. We also propose two novel constraints in prior specifications for the Gaussian MRF model and a fully Bayesian approach to the discrete MRF model. We assess the accuracy of estimating the false discovery rate by posterior probabilities in the context of MRF models. Applications to a chromatin immuno-precipitation-chip data set and simulated data show that the modified Gaussian MRF models have superior performance compared with other models, and both MRF-based mixture models, with reasonable robustness to misspecified gene networks, outperform the standard mixture model. Copyright (c) 2010 Royal Statistical Society.

Suggested Citation

  • Peng Wei & Wei Pan, 2010. "Network-based genomic discovery: application and comparison of Markov random-field models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 105-125.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:1:p:105-125
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9876.2009.00686.x
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    References listed on IDEAS

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    1. Smith, Michael & Fahrmeir, Ludwig, 2007. "Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 417-431, June.
    2. Rodrigues, Alexandre & Assunção, Renato, 2008. "Propriety of posterior in Bayesian space varying parameter models with normal data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2408-2411, October.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
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    Cited by:

    1. Long Qu & Dan Nettleton & Jack C. M. Dekkers, 2012. "A Hierarchical Semiparametric Model for Incorporating Intergene Information for Analysis of Genomic Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1168-1177, December.

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