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Bioinformatics Advance Access published online on April 28, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti466
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received October 11, 2004
Revised April 23, 2005
Accepted April 23, 2005

Article

A variational Bayesian mixture modelling framework for cluster analysis of gene expression data

Andrew E. Teschendorff 1*, Yanzhong Wang 1, Nuno L. Barbosa-Morais 1, James D. Brenton 1, and Carlos Caldas 1

1 Department of Oncology, Cancer Genomics Program, Hutchison-MRC Research Centre, University of Cambridge, Hills Road, Cambridge CB2 2XZ, UK

* To whom correspondence should be addressed.
Andrew E. Teschendorff, E-mail: aet21{at}cam.ac.uk


  Abstract

Motivation: Accurate subcategorisation of tumour types through gene expression profiling requires analytical techniques that estimate the number of categories or clusters rigorously and reliably. Parametric mixture modelling provides a natural setting in which to address this problem.

Results: We compare a criterion for model selection that is derived from a variational Bayesian framework with a popular alternative based on the Bayesian information criterion (BIC). Using simulated data we show that the variational Bayesian method is more accurate in finding the true number of clusters in situations that are relevant to current and future microarray studies. We also compare the two criteria using publicly available tumour microarray data sets and show that the variational Bayesian method is moresensitive to capturing biologically relevant structure.

Availability: We have developed an R-package vabayelMix, available from www.cran.r-project.org, that implements the algorithm as described here.


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