Geometric Interpretation of Gene Co-Expression Network Analysis

 Steve Horvath^# * and Jun Dong^#
Department of Human Genetics and Department of Biostatistics, University of California, Los Angeles, CA 90095, USA
Email: Steve Horvath* - shorvath@mednet.ucla.edu; Jun Dong - jundong@ucla.edu;
*Corresponding author
# These authors contributed equally to this work.

• Citation

• Presentation

• Abstract

• Additional Files

• R Tutorial

• Acknowledgement

• Other Materials

Citation

Horvath S, Dong J (2008) Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol 4(8): e1000117

Link to paper: PLoS Computational Biology

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Presentation

Link to talks:    PowerPoint Version    PDF version

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Abstract

The merging of network theory and microarray data analysis techniques has spawned a new field: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods.

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Additional Files

1. Robustness Analysis of the Brain Cancer Gene Co-expression Network (Download)

2. Robustness Analysis of the Mouse Gene Co-expression Networks (Download)

3. Robustness Analysis of the Yeast Gene Co-expression Networks (Download)

4. Brain Cancer Network Comprised of the 500 Genes with Highest Absolute Correlation with Brain Cancer Survival Time (Download)

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R Tutorials

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Acknowledgement

We acknowledge grant support from 1U19AI063603-01, P50CA092131, 1U24NS043562-01, 5P30CA016042-28, and HL28481. We are grateful for discussions with Andy Yip, Lora Bagryanova, Dan Geschwind, Peter Langfelder, Tova Fuller, Jake Lusis, Tom Drake, Paul Mischel, Stan Nelson, Mike Oldham, Anja Presson, Lin Wang, and Nan Zhang.

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Other Materials Regarding Weighted Gene Co-expression Network Analysis

Weighted Gene Co-Expression Network Page

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2008-08-15

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