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Network‐based genomic discovery: application and comparison of Markov random‐field models
Author(s) -
Wei Peng,
Pan Wei
Publication year - 2010
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2009.00686.x
Subject(s) - computer science , markov random field , mixture model , robustness (evolution) , independent and identically distributed random variables , bayesian network , context (archaeology) , artificial intelligence , gaussian , pattern recognition (psychology) , bayesian probability , gaussian process , data mining , mathematics , statistics , random variable , biology , gene , genetics , paleontology , segmentation , image segmentation , physics , quantum mechanics
Summary. 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.