Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm.

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We live in an era of abundant data.This has necessitated TV 15" TV SET+CTL BOX+BACK COVER 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 ERECTOL 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/.