Integrative model-based clustering of microarray methylation and expression data

Matthias Kormaksson, James G. Booth, Maria E. Figueroa, Ari Melnick

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1327-1347
Number of pages21
JournalAnnals of Applied Statistics
Issue number3
StatePublished - Sep 2012
Externally publishedYes


  • Aml
  • Em algorithm
  • Expression
  • Integrative model-based clustering
  • Methylation
  • Microarray data
  • Mixture models

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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