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2016 Estimation of multiple networks in Gaussian mixture models
Chen Gao, Yunzhang Zhu, Xiaotong Shen, Wei Pan
Electron. J. Statist. 10(1): 1133-1154 (2016). DOI: 10.1214/16-EJS1135


We aim to estimate multiple networks in the presence of sample heterogeneity, where the independent samples (i.e. observations) may come from different and unknown populations or distributions. Specifically, we consider penalized estimation of multiple precision matrices in the framework of a Gaussian mixture model. A major innovation is to take advantage of the commonalities across the multiple precision matrices through possibly nonconvex fusion regularization, which for example makes it possible to achieve simultaneous discovery of unknown disease subtypes and detection of differential gene (dys)regulations in functional genomics. We embed in the EM algorithm one of two recently proposed methods for estimating multiple precision matrices in Gaussian graphical models. We demonstrate the feasibility and potential usefulness of the proposed methods in an application to glioblastoma subtype discovery and differential gene network analysis with a microarray gene expression data set. We also conduct realistic simulation studies to evaluate and compare the performance of various methods.


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Chen Gao. Yunzhang Zhu. Xiaotong Shen. Wei Pan. "Estimation of multiple networks in Gaussian mixture models." Electron. J. Statist. 10 (1) 1133 - 1154, 2016.


Received: 1 March 2015; Published: 2016
First available in Project Euclid: 2 May 2016

zbMATH: 1335.62098
MathSciNet: MR3499523
Digital Object Identifier: 10.1214/16-EJS1135

Keywords: Disease subtype discovery , Gaussian graphical model , gene expression , glioblastoma , Model-based clustering , non-convex penalty

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society


Vol.10 • No. 1 • 2016
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