Journal
ANNALS OF APPLIED STATISTICS
Volume 6, Issue 3, Pages 1327-1347Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/11-AOAS533
Keywords
Integrative model-based clustering; microarray data; mixture models; EM algorithm; methylation; expression; AML
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Funding
- NSF Grant [DMS-08-05865]
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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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