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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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WRAP-Particle-Monte-Carlo-methods-cluster-analysis-Wild-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 12 June 2021. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (3807Kb) |
Official URL: https://doi.org/10.1007/s11634-020-00401-y
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 | ||||||||
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| Divisions: | Faculty of Science > Mathematics Faculty of Science > Statistics Faculty of Science > Centre for Systems Biology |
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| Journal or Publication Title: | Advances in Data Analysis and Classification | ||||||||
| Publisher: | Springer | ||||||||
| ISSN: | 1862-5347 | ||||||||
| Official Date: | 2020 | ||||||||
| Dates: |
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| 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: |
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