Model-Based clustering of microarray expression data via latent Gaussian mixture models
|Title:||Model-Based clustering of microarray expression data via latent Gaussian mixture models||Authors:||McNicholas, Paul D.
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/2836||Date:||1-Nov-2010||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.||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Oxford University Press||Copyright (published version):||Oxford University Press 2010.||Keywords:||Mixture models; Model-based clustering; Gene expression data||Subject LCSH:||Cluster analysis
Mixture distributions (Probability theory)
|DOI:||10.1093/bioinformatics/btq498||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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