English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures

Rasmussen, C., de la Cruz, B., Ghahramani, Z., & Wild, D. (2009). Modeling and Visualizing Uncertainty in Gene Expression Clusters using Dirichlet Process Mixtures. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6(4), 615-628. doi:10.1109/TCBB.2007.70269.

Item is

Files

show Files

Locators

show
hide
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Rasmussen, CE1, 2, Author           
de la Cruz , BJ, Author
Ghahramani, Z, Author
Wild, DL, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

Content

show
hide
Free keywords: -
 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 models provide a non-parametric 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 non-parametric Bayesian clustering methods to the clustering of high-dimensional non-time series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a Dirichlet process mixture 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.

Details

show
hide
Language(s):
 Dates: 2009-10
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/TCBB.2007.70269
BibTex Citekey: 4799
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 6 (4) Sequence Number: - Start / End Page: 615 - 628 Identifier: -