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Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm
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Darkins, Robert, Cooke, Emma J., Ghahramani, Zoubin, Kirk, P. (Paul), Wild, David L. and Savage, Richard S. (2013) Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm. PLoS One, 8 (4). e59795. doi:10.1371/journal.pone.0059795 ISSN 1932-6203.
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WRAP_Kirk_pone.0059795.pdf - Published Version Available under License Creative Commons Attribution. Download (1252Kb) | Preview |
Official URL: http://dx.doi.org/10.1371/journal.pone.0059795
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/.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry Faculty of Science, Engineering and Medicine > Engineering > Engineering Faculty of Science, Engineering and Medicine > Research Centres > Warwick Systems Biology Centre |
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Library of Congress Subject Headings (LCSH): | Bayesian statistical decision theory, Time-series analysis, Cluster analysis, Algorithms | ||||
Journal or Publication Title: | PLoS One | ||||
Publisher: | Public Library of Science | ||||
ISSN: | 1932-6203 | ||||
Official Date: | April 2013 | ||||
Dates: |
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Volume: | 8 | ||||
Number: | 4 | ||||
Article Number: | e59795 | ||||
DOI: | 10.1371/journal.pone.0059795 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 24 December 2015 | ||||
Date of first compliant Open Access: | 24 December 2015 | ||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC), Medical Research Council (Great Britain) (MRC), University of Warwick. Molecular Organisation and Assembly in Cells | ||||
Grant number: | EP/F027400/1 (EPSRC), EP/I036575/1 (EPSRC) |
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