Skip Navigation



Bioinformatics Advance Access published online on February 24, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl065
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow All Versions of this Article:
22/10/1259    most recent
btl065v1
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 Huang, D.
Right arrow Articles by Pan, W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Huang, D.
Right arrow Articles by Pan, W.
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
Received November 28, 2005
Revised January 16, 2006
Accepted February 20, 2006

Article

Incorporating biological knowledge into distance-based clustering analysis of microarray gene expression data

Desheng Huang 1 and Wei Pan 2 *

1 Department of Mathematics, China Medical University, Shenyang, China; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
2 Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA

* To whom correspondence should be addressed.
Wei Pan, E-mail: weip{at}biostat.umn.edu


  Abstract

Motivation: Because co-expressed genes are likely to share the same biological function, cluster analysis of gene expression profiles has been applied for gene function discovery. Most existing clustering methods ignore known gene functions in the process of clustering.

Results: To take advantage of accumulating gene functional annotations, we propose incorporating known gene functions into a new distance metric, which shrinks a gene expression-based distance towards 0 if and only if the two genes share a common gene function. A two step procedure is used. First, the shrinkage distance metric is used in any distance-based clustering method, e.g. K-medoids or hierarchical clustering, to cluster the genes with known functions. Second, while keeping the clustering results from the first step for the genes with known functions, the expression-based distance metric is used to cluster the remaining genes of unknown function, assigning each of them to either one of the clusters obtained in the first step or some new clusters. A simulation study and an application to gene function prediction for the yeast demonstrate the advantage of our proposal over the standard method.

Associate Editor: Joaquin Dopazo
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
D. Dotan-Cohen, S. Kasif, and A. A. Melkman
Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering
Bioinformatics, July 15, 2009; 25(14): 1789 - 1795.
[Abstract] [Full Text] [PDF]

Home page
 Brief BioinformHome page
B. Andreopoulos, A. An, X. Wang, and M. Schroeder
A roadmap of clustering algorithms: finding a match for a biomedical application
Brief Bioinform, May 1, 2009; 10(3): 297 - 314.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
I. Takigawa and H. Mamitsuka
Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis
Bioinformatics, January 15, 2008; 24(2): 250 - 257.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
D. Dotan-Cohen, A. A. Melkman, and S. Kasif
Hierarchical tree snipping: clustering guided by prior knowledge
Bioinformatics, December 15, 2007; 23(24): 3335 - 3342.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
F. Tai and W. Pan
Incorporating prior knowledge of gene functional groups into regularized discriminant analysis of microarray data
Bioinformatics, December 1, 2007; 23(23): 3170 - 3177.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
G. C. Tseng
Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data
Bioinformatics, September 1, 2007; 23(17): 2247 - 2255.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
F. Tai and W. Pan
Incorporating prior knowledge of predictors into penalized classifiers with multiple penalty terms
Bioinformatics, July 15, 2007; 23(14): 1775 - 1782.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
Y. Shi, M. Klustein, I. Simon, T. Mitchell, and Z. Bar-Joseph
Continuous hidden process model for time series expression experiments
Bioinformatics, July 1, 2007; 23(13): i459 - i467.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
M. Shiga, I. Takigawa, and H. Mamitsuka
Annotating gene function by combining expression data with a modular gene network
Bioinformatics, July 1, 2007; 23(13): i468 - i478.
[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.