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Submitted to the Annals of Applied Statistics BAYESIAN CLUSTERING OF REPLICATED TIME-COURSE GENE EXPRESSION DATA WITH WEAK SIGNALS

By Simon Tavaré

Abstract

To identify novel dynamic patterns of gene expression, we develop a statistical method to cluster noisy measurements of gene expression collected from multiple replicates at multiple time points, with an unknown number of clusters. We propose a random-effects mixture model coupled with a Dirichlet-process prior for clustering. The mixture model formulation allows for probabilistic cluster assignments. The random-effects formulation allows for attributing the total variability in the data to the sources that are consistent with the experimental design, particularly when the noise level is high and the temporal dependence is not strong. The Dirichlet-process prior induces a prior distribution on partitions and helps to estimate the number of clusters (or mixture components) from the data. We further tackle two challenges associated with Dirichlet-process prior-based methods. One is efficient sampling. We develop a novel Metropolis-Hastings Markov Chain Monte Carlo (MCMC) procedur

Topics: multivariate analysis, time series, microarray gene expression
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.1378
Provided by: CiteSeerX
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