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Bioinformatics Advance Access published online on September 1, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti655
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received June 10, 2005
Revised July 27, 2005
Accepted August 30, 2005

Article

Network constrained clustering for gene microarray data

Dongxiao Zhu 1*, Alfred O. Hero 2, Hong Cheng 3, Ritu Khanna 3, and Anand Swaroop 3

1 Bioinformatics Program, University of Michigan, Ann Arbor, MI 48109; Department of EECS, Statistics and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
2 Department of EECS, Statistics and Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109
3 Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI 48109

* To whom correspondence should be addressed.
Dongxiao Zhu, E-mail: zhud{at}umich.edu


  Abstract

Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and nonlinear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism data set and a retinal gene expression data set. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway. The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.


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