Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Carl Edward RasmussenDavid L Wild

Abstract

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data.

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Citations

Jun 10, 2010·Bioinformatics·Richard S SavageDavid L Wild
Aug 8, 2009·BMC Bioinformatics·Richard S SavageDavid L Wild
Oct 27, 2011·PLoS Computational Biology·Yinyin YuanFlorian Markowetz
Apr 9, 2015·Genome Biology·Dimitrios V VavoulisJulian Gough
May 29, 2015·Nature·Zoubin Ghahramani
Jan 14, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Marina Meila, Harr Chen
Aug 9, 2017·Journal of the Royal Statistical Society. Series C, Applied Statistics·Tianzhou MaGeorge Tseng
Jan 18, 2018·PLoS Computational Biology·Ian C McDowellBarbara E Engelhardt
Jul 10, 2018·Journal of the American Statistical Association·Jeffrey W Miller, Matthew T Harrison

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