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

Nathan Cunningham (), Jim E. Griffin and David L. Wild
Additional contact information
Nathan Cunningham: University of Warwick Coventry
Jim E. Griffin: University College London
David L. Wild: University of Warwick Coventry

Advances in Data Analysis and Classification, 2020, vol. 14, issue 2, No 10, 463-484

Abstract: 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

Keywords: Cluster analysis; Mixture models; Bayesian inference; Particle Monte Carlo; 62H30; 62P10; 65C05 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s11634-020-00401-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advdac:v:14:y:2020:i:2:d:10.1007_s11634-020-00401-y

Ordering information: This journal article can be ordered from
http://www.springer. ... ds/journal/11634/PS2

DOI: 10.1007/s11634-020-00401-y

Access Statistics for this article

Advances in Data Analysis and Classification is currently edited by H.-H. Bock, W. Gaul, A. Okada, M. Vichi and C. Weihs

More articles in Advances in Data Analysis and Classification from Springer, German Classification Society - Gesellschaft für Klassifikation (GfKl), Japanese Classification Society (JCS), Classification and Data Analysis Group of the Italian Statistical Society (CLADAG), International Federation of Classification Societies (IFCS)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().