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Bioinformatics Advance Access originally published online on September 1, 2005
Bioinformatics 2005 21(21):4014-4020; 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{at}oxfordjournals.org

Network constrained clustering for gene microarray data

Dongxiao Zhu 1,3,*, Alfred O Hero 2,3,4, Hong Cheng 5,6, Ritu Khanna 5,6 and Anand Swaroop 5,6

1Bioinformatics Program, University of Michigan Ann Arbor, MI 48109, USA
2Department of Electrical Engineering and Computer Science (EECS), University of Michigan Ann Arbor, MI 48109, USA
3Department of Statistics, University of Michigan Ann Arbor, MI 48109, USA
4Department of Biomedical Engineering, University of Michigan Ann Arbor, MI 48109, USA
5Department of Ophthalmology, University of Michigan Ann Arbor, MI 48109, USA
6Department of Visual Science, University of Michigan Ann Arbor, MI 48109, USA

*To whom correspondence should be addressed.

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 non-linear 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 dataset and a retinal gene expression dataset. 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.

Availability: The clustering method has been implemented in an R package ‘GeneNT’ that is freely available from: http://www.cran.org.

Contact: zhud{at}umich.edu

Received on June 10, 2005; revised on July 27, 2005; accepted on August 30, 2005

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