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ParticleMDI - Particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification

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Cunningham, Nathan, Griffin, Jim E. and Wild, David L. (2020) ParticleMDI - Particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification. Advances in Data Analysis and Classification, 14 . pp. 463-484. doi:10.1007/s11634-020-00401-y

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Official URL: https://doi.org/10.1007/s11634-020-00401-y

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Abstract

We present a novel nonparametric Bayesian approach for performing cluster analysis in a context where observational units have data arising from multiple sources. Our approach uses a particle Gibbs sampler for inference in which cluster allocations are jointly updated using a conditional particle filter within a Gibbs sampler, improving the mixing of the MCMC chain. We develop several approaches to improving the computational performance of our algorithm. These methods can achieve greater than an order-of-magnitude improvement in performance at no cost to accuracy and can be applied more broadly to Bayesian inference for mixture models with a single dataset. We apply our algorithm to the discovery of risk cohorts amongst 243 patients presenting with kidney renal clear cell carcinoma, using samples from the Cancer Genome Atlas, for which there are gene expression, copy number variation, DNA methylation, protein expression and microRNA data. We identify 4 distinct consensus subtypes and show they are prognostic for survival rate (p < 0.0001).

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Mathematics
Faculty of Science > Statistics
Faculty of Science > Centre for Systems Biology
Library of Congress Subject Headings (LCSH): Cluster analysis, Bayesian statistical decision theory, Monte Carlo method
Journal or Publication Title: Advances in Data Analysis and Classification
Publisher: Springer
ISSN: 1862-5347
Official Date: 2020
Dates:
DateEvent
2020Published
12 June 2020Available
14 May 2020Accepted
Date of first compliant deposit: 10 June 2020
Volume: 14
Page Range: pp. 463-484
DOI: 10.1007/s11634-020-00401-y
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: This is a post-peer-review, pre-copyedit version of an article published in Advances in Data Analysis and Classification. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11634-020-00401-y
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
1654596[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
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