Geometric Interpretation of Gene Coexpression Network Analysis

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...

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Autores principales: Horvath, Steve, Dong, Jun
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2446438/
https://www.ncbi.nlm.nih.gov/pubmed/18704157
http://dx.doi.org/10.1371/journal.pcbi.1000117
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author Horvath, Steve
Dong, Jun
author_facet Horvath, Steve
Dong, Jun
author_sort Horvath, Steve
collection PubMed
description 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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spelling pubmed-24464382008-08-15 Geometric Interpretation of Gene Coexpression Network Analysis Horvath, Steve Dong, Jun PLoS Comput Biol Research Article 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. Public Library of Science 2008-08-15 /pmc/articles/PMC2446438/ /pubmed/18704157 http://dx.doi.org/10.1371/journal.pcbi.1000117 Text en Horvath, Dong. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Horvath, Steve
Dong, Jun
Geometric Interpretation of Gene Coexpression Network Analysis
title Geometric Interpretation of Gene Coexpression Network Analysis
title_full Geometric Interpretation of Gene Coexpression Network Analysis
title_fullStr Geometric Interpretation of Gene Coexpression Network Analysis
title_full_unstemmed Geometric Interpretation of Gene Coexpression Network Analysis
title_short Geometric Interpretation of Gene Coexpression Network Analysis
title_sort geometric interpretation of gene coexpression network analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2446438/
https://www.ncbi.nlm.nih.gov/pubmed/18704157
http://dx.doi.org/10.1371/journal.pcbi.1000117
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