Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures
Rasmussen, Carl Edward, de la Cruz, Bernard J., Ghahramani, Zoubin and Wild, David L.. (2009) Modeling and visualizing uncertainty in gene expression clusters using dirichlet process mixtures. IEEE - ACM Transactions on Computational Biology and Bioinformatics, Vol.6 (No.4). pp. 615-628. ISSN 1545-5963Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/TCBB.2007.70269
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.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QD Chemistry
Q Science > QA Mathematics
|Divisions:||Faculty of Science > Centre for Systems Biology|
|Journal or Publication Title:||IEEE - ACM Transactions on Computational Biology and Bioinformatics|
|Number of Pages:||14|
|Page Range:||pp. 615-628|
|Access rights to Published version:||Restricted or Subscription Access|
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