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:	Steve Horvath, Jun Dong
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2008
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/cdf1029cb2d04d29baa77d423e3ac32d
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spelling	oai:doaj.org-article:cdf1029cb2d04d29baa77d423e3ac32d2021-11-25T05:41:11ZGeometric interpretation of gene coexpression network analysis.1553-734X1553-735810.1371/journal.pcbi.1000117https://doaj.org/article/cdf1029cb2d04d29baa77d423e3ac32d2008-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/18704157/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358THE 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.Steve HorvathJun DongPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 4, Iss 8, p e1000117 (2008)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Steve Horvath Jun Dong Geometric interpretation of gene coexpression network analysis.
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.
format	article
author	Steve Horvath Jun Dong
author_facet	Steve Horvath Jun Dong
author_sort	Steve Horvath
title	Geometric interpretation of gene coexpression network analysis.
title_short	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_sort	geometric interpretation of gene coexpression network analysis.
publisher	Public Library of Science (PLoS)
publishDate	2008
url	https://doaj.org/article/cdf1029cb2d04d29baa77d423e3ac32d
work_keys_str_mv	AT stevehorvath geometricinterpretationofgenecoexpressionnetworkanalysis AT jundong geometricinterpretationofgenecoexpressionnetworkanalysis
_version_	1718414544697556992

Geometric interpretation of gene coexpression network analysis.

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