Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
Clustering short time series gene expression data
Bioinformatics
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The unsupervised clustering analysis of data from temporal or dose-response experiments is one of the most important and challenging tasks of microarray data anlysis. Here we present an extension of CAGED (Cluster Analysis of Gene Expression Dynamics, one of the most commonly used programs) to identify similar gene expression patterns measured in either short time-course or dose-response microarray experiments. Compared to the initial version of CAGED, in which gene expression temporal profiles are modeled by autoregressive equations, this new method uses polynomial models to incorporate time/dosage information into the model, and objective priors to include information about background noise in gene expression data. In its current formulation, CAGED results may change according to the parametrization. In this new formulation, we make the results invariant to reparametrization by using proper prior distributions on the model parameters. We compare the results obtained by our approach with those generated by STEM to show that our method can identify the correct number of clusters and allocate gene expression profiles to the correct clusters in simulated data, and produce more meaningful Gene Ontology enriched clusters in data from real microarray experiments.