Darkins, R and Cooke, EJ and Ghahramani, Z and Kirk, PD and Wild, DL and Savage, RS (2013) Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm. PLoS One, 8. e59795-.Full text not available from this repository.
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/.
|Uncontrolled Keywords:||Algorithms Bayes Theorem Cluster Analysis Computational Biology Internet Microarray Analysis Models, Statistical Time Factors|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||15 Dec 2015 12:50|
|Last Modified:||05 May 2016 03:56|