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Gene Expression Clustering with Functional Mixture Models

Chudova, Darya and Hart, Christopher and Mjolsness, Eric and Smyth, Padhraic (2004) Gene Expression Clustering with Functional Mixture Models. In: Advances in Neural Information Processing Systems 16 (NIPS 2003). Advances in Neural Information Processing Systems. No.16. MIT Press , Cambridge, MA, pp. 683-690. ISBN 0-262-20152-6.

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We propose a functional mixture model for simultaneous clustering and alignment of sets of curves measured on a discrete time grid. The model is specifically tailored to gene expression time course data. Each functional cluster center is a nonlinear combination of solutions of a simple linear differential equation that describes the change of individual mRNA levels when the synthesis and decay rates are constant. The mixture of continuous time parametric functional forms allows one to (a) account for the heterogeneity in the observed profiles, (b) align the profiles in time by estimating real-valued time shifts, (c) capture the synthesis and decay of mRNA in the course of an experiment, and (d) regularize noisy profiles by enforcing smoothness in the mean curves. We derive an EM algorithm for estimating the parameters of the model, and apply the proposed approach to the set of cycling genes in yeast. The experiments show consistent improvement in predictive power and within cluster variance compared to regular Gaussian mixtures.

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Additional Information:© 2004 Massachusetts Institute of Technology.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:16
Record Number:CaltechAUTHORS:20160309-105810912
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:65241
Deposited On:14 Mar 2016 23:54
Last Modified:03 Oct 2019 09:45

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