Welcome to the Weighted Gene Co-Expression Network Page
Weighted Gene Co-expression Network Analysis ( WGCNA )
University of California, Los Angeles
Gene Network Team Members (Pictures)
Steve Horvath, Jun Dong, Tova Fuller, Peter Langfelder, Wen Lin, Michael Mason, Mike Oldham, Anja Presson
Former Members
Jason Aten, Marc Carlson, Sud Doss, Anatole Ghazalpour, Chi-ying Lee, Ai Li, Chris Plaisier, Moira Regelson, Lin Wang, Andy Yip, Bin Zhang, Wei Zhao
Correspondence:
shorvath@mednet.ucla.edu
http://www.biostat.ucla.edu/people/horvath.htm
CONTENTS
Keywords:
Gene Coexpression Network, Gene Co-expression Network, Module.
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Overview of WGCNA
Link to talk: PowerPoint PDF
User-friendly WGCNA software package: WGCNA
Theory Papers
Bin Zhang and Steve Horvath (2005) "A General Framework for Weighted Gene Co-Expression Network Analysis", Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17
Description: How to construct a gene co-expression network using the scale free topology criterion? Robustness of network results. Relating a gene significance measure and the clustering coefficient to intramodular connectivity.
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/GeneralFramework
Link to paper: Statistical Applications in Genetics and Molecular Biology
Link to talk: PowerPoint PDF
"Connectivity, Module-Conformity, and Significance: Understanding Gene Co-Expression Network Methods" by Jun Dong and Steve Horvath
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/
Dong J, Horvath S (2007) Understanding Network Concepts in Modules, BMC Systems Biology 2007, 1:24
Description: Theory of module networks (both co-expression and protein-protein interaction modules).
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks/
Link to paper: BMC Systems Biology
Link to talk: PowerPoint PDF
Horvath S, Dong J (2008) Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol 4(8): e1000117
Description: Theory of module networks specifically in gene co-expression network analysis.
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/GeometricInterpretation/
Link to paper: PLoS Computational Biology
Link to talk: PowerPoint PDF
Yip A, Horvath S (2007) Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 2007, 8:22
Description: What is the topological overlap measure? Empirical studies of the robustness of the topological overlap measure.
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/GTOM/
Link to paper: BMC Bioinformatics
Link to talk: PowerPoint PDF
Li A, Horvath S (2006) Network Neighborhood Analysis with the multi-node topological overlap measure. Bioinformatics. doi:10.1093/bioinformatics/btl581
Description: Software for carrying out neighborhood analysis based on topological overlap. The paper shows that an initial seed neighborhood comprised of 2 or more highly interconnected genes (high TOM, high connectivity) yields superior results. It also shows that topological overlap is superior to correlation when dealing with expression data.
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/MTOM/
Link to paper: Bioinformatics
Link to talk: PowerPoint PDF
Langfelder P, Zhang B, Horvath S (2007) Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut library for R. Bioinformatics. November/btm563
Description: This article describes our default method for defining modules as branches of a hierarchical cluster tree.
Link to R packages and examples: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/BranchCutting/
Link to paper: Bioinformatics (PDF)
Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology 2007, 1:54
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/EigengeneNetwork
Link to paper: BMC Systems Biology
Link to talk: Powerpoint PDF
Aten JE, Fuller TF, Lusis AJ, Horvath S (2008) Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Systems Biology 2008, 2:34
Link to report and code: http://www.genetics.ucla.edu/labs/horvath/aten/NEO/
Link to paper: BMC Systems Biology
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559
Link to webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/
Link to paper: BMC Bioinformatics
Applied Papers
Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006) "Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target", PNAS | November 14, 2006 | vol. 103 | no. 46 | 17402-17407
Description: Gene screening based on intramodular connectivity identifies brain cancer genes that validate. This paper shows that WGCNA greatly alleviates the multiple comparison problem and leads to reproducible findings.
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ASPMgene/
Link to paper: PNAS
Yeast Network Application. "Gene Connectivity, Function, and Sequence Conservation: Predictions from Modular Yeast Co-Expression Networks" (2006) by Carlson MRJ, Zhang B, Fang Z, Mischel PS, Horvath S, and Nelson SF, BMC Genomics 2006, 7:40
Description: The relationship between connectivity and knock-out essentiality is dependent on the module under consideration. Hub genes in some modules may be non-essential. This study shows that intramodular connectivity is much more meaningful than whole network connectivity.
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/MarcCarlson/
Link to paper: BMC Genomics
Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE, Drake TA, Lusis AJ, Horvath S (2006) "Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight". PLoS Genetics. Volume 2 | Issue 8 | AUGUST 2006
General description: How to integrate SNP markers into weighted gene co-expression network analysis? These 2 papers (with Fuller etc, 2007) outline how SNP markers and co-expression networks can be used to screen for gene expressions underlying a complex trait. They also illustrate the use of the module eigengene based connectivity measure kME.
Description: Single network analysis
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/MouseWeight/
Link to paper: PLoS Genetics
Link to talk: PowerPoint PDF
Fuller TF, Ghazalpour A, Aten JE, Drake TA, Lusis AJ, Horvath S (2007) "Weighted Gene Co-expression Network Analysis Strategies Applied to Mouse Weight", Mamm Genome. 18(6):463-472
Description: Differential network analysis (also see Ghazalpour etc. 2006)
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/DifferentialNetworkAnalysis
Link to paper: Mammalian Genome
Link to talk: PowerPoint PDF
"Identification of inflammatory gene modules based on variations of human endothelial cell responses to oxidized lipids." (2006) by Peter S. Gargalovic, Minori Imura, Bin Zhang, Nima M. Gharavi, Michael J. Clark, Joanne Pagnon, Wen-Pin Yang, Aiqing He, Amy Truong, Shilpa Patel , Stanley F. Nelson , Steve Horvath, Judith A. Berliner, Todd G. Kirchgessner, and Aldons J. Lusis
Description: This application presents a 'supervised' gene co-expression network analysis. In general, we prefer to construct a co-expression network and associated modules without regard to an external microarray sample trait (unsupervised WGCNA). But if thousands of genes are differentially expressed, one can construct a network on the basis of differentially expressed genes (supervised WGCNA)
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/InflammatoryModule
Link to paper: PNAS Webpage PNAS PDF
MC Oldham, S Horvath, DH Geschwind (2006) Conservation and evolution of gene co-expression networks in human and chimpanzee brain. PNAS.
Description: This paper presents a differential co-expression network analysis. It studies module preservation between two networks. By screening for genes with differential topological overlap, we identify biologically interesting genes. The paper also shows the value of summarizing a module by its module eigengene.
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/HumanChimp/
Link to paper: PNAS
Link to talk: PDF
Gong KW, Zhao W, Li N, Barajas B, Kleinman M, Sioutas C, Horvath S, Lusis AJ, Nel A, Arauj JA (2007) Air-pollutant chemicals and oxidized lipids exhibit genome-wide synergistic effects on endothelial cells. Genome Biology 2007, 8:R149doi
Link to paper: Genome Biology
Jeremy A. Miller, Michael C. Oldham, and Daniel H. Geschwind (2008) A Systems Level Analysis of Transcriptional Changes in Alzheimer's Disease and Normal Aging. J. Neurosci. 28: 1410-1420
Link to paper: Journal of Neuroscience
Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, Leonardson A, Castellini LW, Wang S, Champy MF, Zhang B, Emilsson V, Doss S, Ghazalpour A, Horvath S, Drake TA, Lusis AJ, Schadt EE. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008 Mar 27;452(7186):429-35.
Link to paper: Nature
Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH (2008) Functional organization of the transcriptome in human brain. Nature Neuroscience. October 12
Description: This is the first comprehensive analysis of gene coexpression relationships in human cerebral cortex, caudate nucleus and cerebellum. The results demonstrate that the transcriptomes of human brain regions are robustly organized into modules of coexpressed genes that reflect the underlying cellular composition of brain tissue. This article highlights the value of WGCNA for annotating genes with regard to coexpression module membership. Toward this end, it makes use of fuzzy module membership measures which are highly related to intramodular connectivity (Dong and Horvath 2008). The fuzzy module membership measures (and intramodular connectivity) can be used i) to determine whether a gene is close to one or more modules, ii) to determine whether a module is preserved across data, iii) and to find differentially connected genes. The paper also demonstrates the use of of dynamic tree cutting for module detection and the use of eigengene networks to describe relationships between coexpression modules.
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/HumanBrainTranscriptome/
Link to paper: Nature Neuroscience
Link to talk: PowerPoint PDF
Presson AP , Sobel EM , Papp JC , Suarez CJ , Whistler T, Rajeevan MS, Vernon SD, Horvath S (2008) Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome. BMC Systems Biology 2008, 2:95
Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/CFS/
Link to paper: BMC Systems Biology
van Nas A, Guhathakurta D, Wang SS, Yehya N, Horvath S, Zhang B, Ingram-Drake L, Chaudhuri G, Schadt EE, Drake TA, Arnold AP, Lusis AJ (2008) Elucidating the Role of Gonadal Hormones in Sexually Dimorphic Gene Co-Expression Networks. Endocrinology. 2008 Oct 30
Hu S, Zhou M, Jiang J, Wang J, Elashoff D, Gorr S, Michie SA, Spijkervet FK, Bootsma H, Kallenberg CG, Vissink A, Horvath S, Wong DT (2008) Systems biology analysis of sjögren's syndrome and mucosa-associated lymphoid tissue lymphoma in parotid glands. Arthritis Rheum. 2008 Dec 30; 60(1):81-92
Wiki Dictionary of terms and reading lists
2009-01-16
Please send your suggestions and
comments to: shorvath@mednet.ucla.edu