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

Content Provider	PubMed Central
Author	Darkins, Robert Cooke, Emma J. Ghahramani, Zoubin Kirk, Paul D. W. Wild, David L. Savage, Richard S.
Editor	Rattray, Magnus
Copyright Year	2013
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/.
Related Links	http://dx.doi.org/10.1371/journal.pone.0059795
Starting Page	59795
File Format	PDF
ISSN	19326203
e-ISSN	19326203
Journal	PLoS ONE
Issue Number	4
Volume Number	8
Language	English
Publisher	Public Library of Science
Publisher Date	2013-04-01
Access Restriction	Open
Rights Holder	Public Library of Science
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Multidisciplinary

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Bayesian Hierarchical Clustering for Studying Cancer Gene Expression Data with Unknown Statistics

Estimating the Under-Five Mortality Rate Using a Bayesian Hierarchical Time Series Model

Dynamic Networks from Hierarchical Bayesian Graph Clustering

A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables

Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists

Accounting for Sampling Error When Inferring Population Synchrony from Time-Series Data: A Bayesian State-Space Modelling Approach with Applications

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering

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

Similar Documents

Bayesian Hierarchical Clustering for Studying Cancer Gene Expression Data with Unknown Statistics

Estimating the Under-Five Mortality Rate Using a Bayesian Hierarchical Time Series Model

Dynamic Networks from Hierarchical Bayesian Graph Clustering

A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables

Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

A Hierarchical Bayesian Approach to Ecological Count Data: A Flexible Tool for Ecologists

Accounting for Sampling Error When Inferring Population Synchrony from Time-Series Data: A Bayesian State-Space Modelling Approach with Applications

Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering

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