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Clustering Microarray Data Via a Bayesian Infinite Mixture Model

Clustering microarray data is a helpful way of identifying genes which are biologically related. Unfortunately, when attempting to cluster microarray data, certain issues must be considered including: the uncertainty in the number of true clusters; the expression of a given gene is often a ected by the expression of other genes; and microarray data is usually high dimensional. This thesis outlines a Bayesian in nite
Gaussian mixture model which addresses the issues outlined above by: not requiring the researcher to specify the number of clusters expected, applying a non-diagonal covariance structure, and using mixtures of factor analyzers and extensions thereof to structure the covariance matrix such that it is based on a few latent variables. This
approach will be illustrated on real and simulated data.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/5210
Date04 January 2013
CreatorsGivari, Dena
ContributorsMcNicholas, Paul
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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