PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

R/BHC:Fast Bayesian Hierarchical Clustering for Microarray Data
R.S. Savage, Katherine Heller, Yang Xu, Zoubin Ghahramani, W.M. Truman, M. Grant, K.J. Denby and David Wild
BMC Bioinformatics Volume 10, Number 242, 2009.

Abstract

Background Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained. Results We present an R/Bioconductor port of a fast novel algorithm for Bayesian agglomerative hierarchical clustering and demonstrate its use in clustering gene expression microarray data. The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. Conclusion Biologically plausible results are presented from a well studied data set: expression profiles of A. thaliana subjected to a variety of biotic and abiotic stresses. Our method avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:6741
Deposited By:Katherine Heller
Deposited On:08 March 2010