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R/BHC : fast Bayesian hierarchical clustering for microarray data

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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. ISSN 1471-2105

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Official URL: http://dx.doi.org/10.1186/1471-2105-10-242

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 > Centre for Systems Biology
Faculty of 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:

Article Number:

242

Identification Number:

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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