NDLI: Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures

Content Provider	IEEE Xplore Digital Library
Author	Rasmussen, C.E. de la Cruz, B.J. Ghahramani, Z. Wild, D.L.
Copyright Year	2004
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.
Sponsorship	IEEE Computer Society
Page Count	14
File Size	4526827
Starting Page	615
Ending Page	628
File Format	PDF
ISSN	15455963
Volume Number	6
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Gene expression Clustering methods Bayesian methods Data visualization Biological system modeling Measurement standards Couplings Clustering algorithms Data analysis Monte Carlo. Clustering classification and association rules biology and genetics bioinformatics (genome or protein) databases statistical computing stochastic processes Bioinformatics (genome or protein) databases
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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