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Accelerating Bayesian Hierarchical Clustering of Time Series Data with a Randomised Algorithm

Author

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  • Robert Darkins
  • Emma J Cooke
  • Zoubin Ghahramani
  • Paul D W Kirk
  • David L Wild
  • Richard S Savage

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/.

Suggested Citation

  • Robert Darkins & Emma J Cooke & Zoubin Ghahramani & Paul D W Kirk & David L Wild & Richard S Savage, 2013. "Accelerating Bayesian Hierarchical Clustering of Time Series Data with a Randomised Algorithm," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-9, April.
    • Handle: RePEc:plo:pone00:0059795
    DOI: 10.1371/journal.pone.0059795
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    References listed on IDEAS

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    1. Guillaume Marrelec & Arnaud Messé & Pierre Bellec, 2015. "A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-26, September.
    2. Crook Oliver M. & Gatto Laurent & Kirk Paul D. W., 2019. "Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-20, December.

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