Skip Navigation


Bioinformatics Advance Access originally published online on June 9, 2006
Bioinformatics 2006 22(16):1988-1997; doi:10.1093/bioinformatics/btl284
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
22/16/1988    most recent
btl284v1
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 ISI Web of Science
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 arrow Search for citing articles in:
ISI Web of Science (5)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Qin, Z. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Qin, Z. S.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Clustering microarray gene expression data using weighted Chinese restaurant process

Zhaohui S. Qin

Center for Statistical Genetics, Department of Biostatistics, School of Public Health, University of Michigan 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA


  Abstract

Motivation: Clustering microarray gene expression data is a powerful tool for elucidating co-regulatory relationships among genes. Many different clustering techniques have been successfully applied and the results are promising. However, substantial fluctuation contained in microarray data, lack of knowledge on the number of clusters and complex regulatory mechanisms underlying biological systems make the clustering problems tremendously challenging.

Results: We devised an improved model-based Bayesian approach to cluster microarray gene expression data. Cluster assignment is carried out by an iterative weighted Chinese restaurant seating scheme such that the optimal number of clusters can be determined simultaneously with cluster assignment. The predictive updating technique was applied to improve the efficiency of the Gibbs sampler. An additional step is added during reassignment to allow genes that display complex correlation relationships such as time-shifted and/or inverted to be clustered together. Analysis done on a real dataset showed that as much as 30% of significant genes clustered in the same group display complex relationships with the consensus pattern of the cluster. Other notable features including automatic handling of missing data, quantitative measures of cluster strength and assignment confidence. Synthetic and real microarray gene expression datasets were analyzed to demonstrate its performance.

Availability: A computer program named Chinese restaurant cluster (CRC) has been developed based on this algorithm. The program can be downloaded at http://www.sph.umich.edu/csg/qin/CRC/

Contact: qin{at}umich.edu

Supplementary information: http://www.sph.umich.edu/csg/qin/CRC/

Associate Editor: John Quackenbush

Received on February 16, 2006; revised on April 20, 2006; accepted on May 31, 2006

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
 EndocrinologyHome page
Y. Chen, G. Lin, J. S. Huo, D. Barney, Z. Wang, T. Livshiz, D. J. States, Z. S. Qin, and J. Schwartz
Computational and Functional Analysis of Growth Hormone (GH)-Regulated Genes Identifies the Transcriptional Repressor B-Cell Lymphoma 6 (Bc16) as a Participant in GH-Regulated Transcription
Endocrinology, August 1, 2009; 150(8): 3645 - 3654.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
F. Achcar, J.-M. Camadro, and D. Mestivier
AutoClass@IJM: a powerful tool for Bayesian classification of heterogeneous data in biology
Nucleic Acids Res., July 1, 2009; 37(suppl_2): W63 - W67.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
H. Wang, Q. Wang, X. Li, B. Shen, M. Ding, and Z. Shen
Towards patterns tree of gene coexpression in eukaryotic species
Bioinformatics, June 1, 2008; 24(11): 1367 - 1373.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Joshi, Y. Van de Peer, and T. Michoel
Analysis of a Gibbs sampler method for model-based clustering of gene expression data
Bioinformatics, January 15, 2008; 24(2): 176 - 183.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
S. Bandyopadhyay, A. Mukhopadhyay, and U. Maulik
An improved algorithm for clustering gene expression data
Bioinformatics, November 1, 2007; 23(21): 2859 - 2865.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Z. Xiang, Z. S. Qin, and Y. He
CRCView: a web server for analyzing and visualizing microarray gene expression data using model-based clustering
Bioinformatics, July 15, 2007; 23(14): 1843 - 1845.
[Abstract] [Full Text] [PDF]

Home page
 Nucleic Acids ResHome page
Y. Lu, X. He, and S. Zhong
Cross-species microarray analysis with the OSCAR system suggests an INSR->Pax6->NQO1 neuro-protective pathway in aging and Alzheimer's disease
Nucleic Acids Res., July 13, 2007; 35(suppl_2): W105 - W114.
[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.