Dr. Paul McNicholas

Department of Mathematics and Statistics

University of Guelph

Title: " Analysis of Gene Expression Time Course Data via Model-Based Clustering "

Abstract:
A new family of mixture models for the model-based clustering of gene
expression time course data is introduced. The covariance structures of
eight members of this new family of models are given and the associated
maximum likelihood estimates for the parameters are estimated using
expectation-maximization (EM) algorithms. The Bayesian information
criterion is used for model selection and Aitken's acceleration is used
to determine convergence of these EM algorithms. This new family of
models is applied to the famous yeast sporulation time course data of
Chu et al, where the models display good clustering performance. Finally,
further constraints are imposed on the decomposition to allow a deeper
investigation of correlation structure of these yeast sporulation data.