6,555 research outputs found
Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures
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
Modeling dependent gene expression
In this paper we propose a Bayesian approach for inference about dependence
of high throughput gene expression. Our goals are to use prior knowledge about
pathways to anchor inference about dependence among genes; to account for this
dependence while making inferences about differences in mean expression across
phenotypes; and to explore differences in the dependence itself across
phenotypes. Useful features of the proposed approach are a model-based
parsimonious representation of expression as an ordinal outcome, a novel and
flexible representation of prior information on the nature of dependencies, and
the use of a coherent probability model over both the structure and strength of
the dependencies of interest. We evaluate our approach through simulations and
in the analysis of data on expression of genes in the Complement and
Coagulation Cascade pathway in ovarian cancer.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS525 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The Infinite Hierarchical Factor Regression Model
We propose a nonparametric Bayesian factor regression model that accounts for
uncertainty in the number of factors, and the relationship between factors. To
accomplish this, we propose a sparse variant of the Indian Buffet Process and
couple this with a hierarchical model over factors, based on Kingman's
coalescent. We apply this model to two problems (factor analysis and factor
regression) in gene-expression data analysis
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