Open Access
September 2012 Integrative Model-based clustering of microarray methylation and expression data
Matthias Kormaksson, James G. Booth, Maria E. Figueroa, Ari Melnick
Ann. Appl. Stat. 6(3): 1327-1347 (September 2012). DOI: 10.1214/11-AOAS533


In many fields, researchers are interested in large and complex biological processes. Two important examples are gene expression and DNA methylation in genetics. One key problem is to identify aberrant patterns of these processes and discover biologically distinct groups. In this article we develop a model-based method for clustering such data. The basis of our method involves the construction of a likelihood for any given partition of the subjects. We introduce cluster specific latent indicators that, along with some standard assumptions, impose a specific mixture distribution on each cluster. Estimation is carried out using the EM algorithm. The methods extend naturally to multiple data types of a similar nature, which leads to an integrated analysis over multiple data platforms, resulting in higher discriminating power.


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Matthias Kormaksson. James G. Booth. Maria E. Figueroa. Ari Melnick. "Integrative Model-based clustering of microarray methylation and expression data." Ann. Appl. Stat. 6 (3) 1327 - 1347, September 2012.


Published: September 2012
First available in Project Euclid: 31 August 2012

zbMATH: 1254.62113
MathSciNet: MR3012532
Digital Object Identifier: 10.1214/11-AOAS533

Keywords: AML , EM algorithm , expression , Integrative model-based clustering , methylation , microarray data , Mixture models

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.6 • No. 3 • September 2012
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