Random Walk Models for Bayesian Clustering of Gene Expression Profiles
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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.
This work was in part supported by the Progetto di Ricerca di Interesse Nazionale (PRIN) 2003 grant ‘Dynamic modelling of gene expression profiles’ from the Italian Ministry of Education.
The authors have no conflicts of interest that are directly relevant to the content of this article.
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