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Model-Based clustering of microarray expression data via latent Gaussian mixture models
Date Issued
2010-11-01
Date Available
2011-03-10T11:51:55Z
Abstract
In recent years, work has been carried out on clustering gene expression microarray data. Some approaches are developed from an algorithmic viewpoint whereas others are developed via the application of mixture models. In this article, a family of eight mixture models which utilizes the factor analysis covariance structure is extended to 12 models and applied to gene expression microarray data. This modelling approach builds on previous work by introducing a modified factor analysis covariance structure, leading to a family of 12 mixture models, including parsimonious models. This family of models allows for the modelling of the correlation between gene expression levels even when the number of samples is small. Parameter estimation is carried out using a variant of the expectation–maximization algorithm and model selection is achieved using the Bayesian information criterion. This expanded family of Gaussian mixture models, known as the expanded parsimonious Gaussian mixture model (EPGMM) family, is then applied to two well-known gene expression data sets.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
Oxford University Press
Journal
Bioinformatics
Volume
26
Issue
21
Start Page
2705
End Page
2712
Copyright (Published Version)
Oxford University Press 2010.
Subject – LCSH
Cluster analysis
Gene expression
DNA microarrays
Mixture distributions (Probability theory)
Web versions
Language
English
Status of Item
Peer reviewed
ISSN
1460-2059 (online)
1367-4803 (print)
This item is made available under a Creative Commons License
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