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particleMDI : a Julia Package for the Integrative Cluster Analysis of Multiple Datasets
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Cunningham, Nathan, Griffin, Jim E., Wild, David L. and Lee, Anthony (2019) particleMDI : a Julia Package for the Integrative Cluster Analysis of Multiple Datasets. In: Argiento, R. and Durante, D. and Wade, S., (eds.) Bayesian Statistics and New Generations. BAYSM 2018. Springer Proceedings in Mathematics & Statistics, 296 . Cham: Springer, pp. 65-74. ISBN 9783030306106
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Official URL: http://dx.doi.org/10.1007/978-3-030-30611-3_7
Abstract
We present particleMDI, a Julia package for performing integrative cluster analysis on multiple heterogeneous data sets, built within the framework of multiple data integration (MDI). particleMDI updates cluster allocations using a particle Gibbs approach which offers better mixing of the MCMC chain---but at greater computational cost---than the original MDI algorithm. We outline approaches for improving computational performance, finding the potential for greater than an order-of-magnitude improvement. We demonstrate the capability of particleMDI to uncovering the ground truth in simulated and real datasets. All files are available at https://github.com/nathancunn/particleMDI.jl
Item Type: | Book Item | ||||||
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Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||
Series Name: | Springer Proceedings in Mathematics & Statistics | ||||||
Journal or Publication Title: | Bayesian Statistics: New Challenges and New Generations - BAYSM 2018 | ||||||
Publisher: | Springer | ||||||
Place of Publication: | Cham | ||||||
ISBN: | 9783030306106 | ||||||
Book Title: | Bayesian Statistics and New Generations. BAYSM 2018 | ||||||
Editor: | Argiento, R. and Durante, D. and Wade, S. | ||||||
Official Date: | 22 November 2019 | ||||||
Dates: |
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Volume: | 296 | ||||||
Page Range: | pp. 65-74 | ||||||
DOI: | 10.1007/978-3-030-30611-3_7 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 14 February 2019 | ||||||
Date of first compliant Open Access: | 1 March 2020 | ||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC), Alan Turing Institute | ||||||
Grant number: | EPSRC 1654596, Alan Turing Institute TU/D/000013, EP/R014337/1 | ||||||
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