title: Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.
abstract: We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/.
authors.affiliation: Systems Biology Centre, University of Warwick, Coventry, United Kingdom.
authors.forename: Robert
authors.initials: R
authors.lastname: Darkins
authors.forename: Emma J
authors.initials: EJ
authors.lastname: Cooke
authors.forename: Zoubin
authors.initials: Z
authors.lastname: Ghahramani
authors.forename: Paul D W
authors.initials: PD
authors.lastname: Kirk
authors.forename: David L
authors.initials: DL
authors.lastname: Wild
authors.forename: Richard S
authors.initials: RS
authors.lastname: Savage
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D020407:Internet; D046228:Microarray Analysis; D015233:Models, Statistical; D013997:Time Factors
references.citation: Bioinformatics. 2010 Jun 15;26(12):i158-67
references.pmid: 20529901
references.citation: J Comput Biol. 2003;10(3-4):341-56
references.pmid: 12935332
references.citation: Nucleic Acids Res. 2006 Mar 01;34(4):1261-9
references.pmid: 16510852
references.citation: Bioinformatics. 2009 May 15;25(10):1300-6
references.pmid: 19289448
references.citation: BMC Bioinformatics. 2009 Aug 06;10:242
references.pmid: 19660130
references.citation: Proc Natl Acad Sci U S A. 1998 Dec 8;95(25):14863-8
references.pmid: 9843981
references.citation: Mol Cell. 1998 Jul;2(1):65-73
references.pmid: 9702192
references.citation: Proc Natl Acad Sci U S A. 2005 Nov 22;102(47):16939-44
references.pmid: 16287981
references.citation: Bioinformatics. 2004 Nov 1;20(16):2493-503
references.pmid: 15130923
references.citation: IEEE/ACM Trans Comput Biol Bioinform. 2005 Jul-Sep;2(3):179-93
references.pmid: 17044182
references.citation: Bioinformatics. 2010 Mar 15;26(6):770-6
references.pmid: 20147305
references.citation: BMC Bioinformatics. 2011 Oct 13;12:399
references.pmid: 21995452
references.citation: BMC Bioinformatics. 2006 Aug 31;7:397
references.pmid: 16945146
references.citation: Bioinformatics. 2005 Aug 15;21(16):3385-93
references.pmid: 15937031
references.citation: J Comput Biol. 2010 Mar;17(3):355-67
references.pmid: 20377450