Open Access
June 2014 A Bayesian nonparametric mixture model for selecting genes and gene subnetworks
Yize Zhao, Jian Kang, Tianwei Yu
Ann. Appl. Stat. 8(2): 999-1021 (June 2014). DOI: 10.1214/14-AOAS719


It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.


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Yize Zhao. Jian Kang. Tianwei Yu. "A Bayesian nonparametric mixture model for selecting genes and gene subnetworks." Ann. Appl. Stat. 8 (2) 999 - 1021, June 2014.


Published: June 2014
First available in Project Euclid: 1 July 2014

zbMATH: 06333785
MathSciNet: MR3262543
Digital Object Identifier: 10.1214/14-AOAS719

Keywords: Density estimation , Dirichlet process mixture , Feature selection , ising priors , microarray data

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 2 • June 2014
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