Abstract
Based on the trajectories of individual genes, we address the problem of clustering time course gene expression data for embryonic stem cells (ESC) differentiation. We propose a class of functions determined by only two parameters but flexible enough to model realistic time courses. This serves as a basis for a mixed model clustering method. This method takes into account (1) genetic function profile induced or controlled by other regulators, (2) unobservable random effects producing heterogeneity within gene clusters, and (3) autoregressive components defining the stochastic and autocorrelation structures. We employ an EM algorithm to fit the mixture model and clustering follows monitoring via Bayesian posterior probabilities. Our method is applied to a mouse ESC line during the first 24 hours of differentiation period. We assess the biological credibility of the results by detecting significantly associated FatiGO Gene Ontology terms for each cluster.
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Li, S., Andrade-Navarro, M., Sankoff, D. (2008). A Customized Class of Functions for Modeling and Clustering Gene Expression Profiles in Embryonic Stem Cells. In: Bazzan, A.L.C., Craven, M., Martins, N.F. (eds) Advances in Bioinformatics and Computational Biology. BSB 2008. Lecture Notes in Computer Science(), vol 5167. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85557-6_9
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DOI: https://doi.org/10.1007/978-3-540-85557-6_9
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