PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Modeling and Visualizing uncertainty in gene expression clusters using dirichlet process mixtures
Carl Edward Rasmussen, B. J. de la Cruz, Zoubin Ghahramani and D. L. Wild
IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 6, Number 4, pp. 615-628, 2009. ISSN 2007.70269

Abstract

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:6268
Deposited By:Zoubin Ghahramani
Deposited On:08 March 2010