Abstract
BackgroundCorrelation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial.ResultsThe WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings.ConclusionThe WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
Highlights
Introduction to Robust Estimation and HypothesisTesting Academic Press; 1997.25
Overview of functions included in the Weighted correlation network analysis (WGCNA) package The WGCNA package contains a comprehensive set of functions for performing a correlation network analysis of large, high-dimensional data sets
Functions in the WGCNA package can be divided into the following categories: 1. network construction; 2. module detection; 3. module and gene selection; 4. calculations of topological properties; 5. data simulation; 6. visualization; 7. interfacing with external software packages
Summary
Introduction to Robust Estimation and HypothesisTesting Academic Press; 1997.25. Yip A, Horvath S: Gene network interconnectedness and the generalized topological overlap measure. Li A, Horvath S: Network Neighborhood Analysis With the Multi-node Topological Overlap Measure. Correlation networks are increasingly being used in bioinformatics applications. Weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. Correlation networks are constructed on the basis of correlations between quantitative measurements that can be described by an n × m matrix X = [xil] where the row indices correspond to network nodes
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