title: Random walk models for bayesian clustering of gene expression profiles.
abstract: The analysis of gene expression temporal profiles is a topic of increasing interest in functional genomics. Model-based clustering methods are particularly interesting because they are able to capture the dynamic nature of these data and to identify the optimal number of clusters. We have defined a new Bayesian method that allows us to cope with some important issues that remain unsolved in the currently available approaches: the presence of time dislocations in gene expression, the non-stationarity of the processes generating the data, and the presence of data collected on an irregular temporal grid. Our method, which is based on random walk models, requires only mild a priori assumptions about the nature of the processes generating the data and explicitly models inter-gene variability within each cluster. It has first been validated on simulated datasets and then employed for the analysis of a dataset relative to serum-stimulated fibroblasts. In all cases, the results have been promising, showing that the method can be helpful in functional genomics research.
authors.affiliation: Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy.
authors.forename: Fulvia
authors.initials: F
authors.lastname: Ferrazzi
authors.forename: Paolo
authors.initials: P
authors.lastname: Magni
authors.forename: Riccardo
authors.initials: R
authors.lastname: Bellazzi