Savage, Richard S., Heller, K. (Katherine), Xu, Yang, Ghahramani, Zoubin, Truman, William M., Grant, Murray, Denby, Katherine J. and Wild, David L. (2009) R/BHC : fast Bayesian hierarchical clustering for microarray data. BMC Bioinformatics, 10 . 242. doi:10.1186/1471-2105-10-242

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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.

Item Type:	Journal Article
Subjects:	R Medicine > R Medicine (General) Q Science > QA Mathematics
Divisions:	Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) > Warwick HRI (2004-2010)
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Gene expression -- Statistical methods, Dirichlet series, Arabidopsis thaliana
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd.
ISSN:	1471-2105
Official Date:	6 August 2009
Dates:	Date Event 6 August 2009 Submitted
Volume:	10
Article Number:	242
DOI:	10.1186/1471-2105-10-242
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), Marie Curie Fellowship Association (MCFA)
Grant number:	EP/F027400/1 (EPSRC), BB/F005806/1 (BBSRC), 46444 (MCFA)

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