Model-Based clustering of microarray expression data via latent Gaussian mixture models

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dc.contributor.author McNicholas, Paul D.
dc.contributor.author Murphy, Thomas Brendan
dc.date.accessioned 2011-03-10T11:51:55Z
dc.date.available 2011-03-10T11:51:55Z
dc.date.copyright Oxford University Press 2010. en
dc.date.issued 2010-11-01
dc.identifier.citation Bioinformatics en
dc.identifier.issn 1460-2059 (online)
dc.identifier.issn 1367-4803 (print)
dc.identifier.uri http://hdl.handle.net/10197/2836
dc.description.abstract In recent years, work has been carried out on clustering gene expression microarray data. Some approaches are developed from an algorithmic viewpoint whereas others are developed via the application of mixture models. In this article, a family of eight mixture models which utilizes the factor analysis covariance structure is extended to 12 models and applied to gene expression microarray data. This modelling approach builds on previous work by introducing a modified factor analysis covariance structure, leading to a family of 12 mixture models, including parsimonious models. This family of models allows for the modelling of the correlation between gene expression levels even when the number of samples is small. Parameter estimation is carried out using a variant of the expectation–maximization algorithm and model selection is achieved using the Bayesian information criterion. This expanded family of Gaussian mixture models, known as the expanded parsimonious Gaussian mixture model (EPGMM) family, is then applied to two well-known gene expression data sets. en
dc.description.sponsorship Science Foundation Ireland en
dc.format.extent 393840 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Oxford University Press en
dc.rights This is a post-peer-review, pre-copyedit version of an article published in Bioinformatics (2010) 26 (21): 2705-2712. The definitive publisher-authenticated version [insert complete citation information here] is available online at: http://dx.doi.org/10.1093/bioinformatics/btq498. en
dc.subject Mixture models en
dc.subject Model-based clustering en
dc.subject Gene expression data en
dc.subject.lcsh Cluster analysis en
dc.subject.lcsh Gene expression en
dc.subject.lcsh DNA microarrays en
dc.subject.lcsh Mixture distributions (Probability theory) en
dc.title Model-Based clustering of microarray expression data via latent Gaussian mixture models en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://dx.doi.org/10.1093/bioinformatics/btq498 en
dc.status Peer reviewed en
dc.identifier.volume 26 en
dc.identifier.issue 21 en
dc.identifier.startpage 2705 en
dc.identifier.endpage 2712 en
dc.identifier.doi 10.1093/bioinformatics/btq498
dc.neeo.contributor McNicholas|Paul D.|aut| en
dc.neeo.contributor Murphy|Thomas Brendan|aut| en
dc.description.admin Embargo until November 2011 - AV 24/2/2011 en


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