Skip Navigation



Bioinformatics Advance Access published online on February 15, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti318
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
21/9/2118    most recent
bti318v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Ji, Y.
Right arrow Articles by Coombes, K. R.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Ji, Y.
Right arrow Articles by Coombes, K. R.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received January 5, 2005
Revised January 27, 2005
Accepted February 8, 2005

Applications note

Applications of beta-mixture models in bioinformatics

Yuan Ji 1*, Chunlei Wu 1, Ping Liu 1, Jing Wang 1, and Kevin R. Coombes 1

1 Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston TX 77030, USA

* To whom correspondence should be addressed.
Yuan Ji, E-mail: yuanji{at}mdanderson.org


  Abstract

We propose a beta-mixture model approach to solve a variety of problems related to correlations of gene expression levels. For example, in meta-analyses of microarray gene expression data sets, a threshold value of correlation coefficients for gene expression levels is used to decide whether gene expression levels are strongly correlated across studies. Ad hoc threshold values such as 0.5 are often used. In this paper, the proposed beta-mixture model approach provides an objective solution for this problem. It divides the correlation coefficients into several populations based on a mixture model, with which the gene population of large correlation coefficients can be identified. Another important application of the proposed method is in finding co-expressed genes. We illustrate the use of the proposed method for both applications with real examples. Through our analysis, we also discover that the popular model selection criteria BIC and AIC are not suitable for the beta-mixture model. To determine the number of components in the mixture model, we suggest using an alternative criterion, ICL-BIC, which is shown to perform better in selecting the correct mixture model.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
X. Dai, O. Yli-Harja, and A. S. Ribeiro
Determining noisy attractors of delayed stochastic gene regulatory networks from multiple data sources
Bioinformatics, September 15, 2009; 25(18): 2362 - 2368.
[Abstract] [Full Text] [PDF]

Home page
 CarcinogenesisHome page
C. J. Marsit, B. C. Christensen, E. A. Houseman, M. R. Karagas, M. R. Wrensch, R.-F. Yeh, H. H. Nelson, J. L. Wiemels, S. Zheng, M. R. Posner, et al.
Epigenetic profiling reveals etiologically distinct patterns of DNA methylation in head and neck squamous cell carcinoma
Carcinogenesis, March 1, 2009; 30(3): 416 - 422.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
J. Zhang, Y. Ji, and L. Zhang
Extracting three-way gene interactions from microarray data
Bioinformatics, November 1, 2007; 23(21): 2903 - 2909.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.