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. 2008 Dec 29:9:559.
doi: 10.1186/1471-2105-9-559.

WGCNA: an R package for weighted correlation network analysis

Peter Langfelder  1 , Steve Horvath
Affiliations

WGCNA: an R package for weighted correlation network analysis

Peter Langfelder et al. BMC Bioinformatics. .

Abstract

Background: Correlation 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.

Results: The 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.

Conclusion: The 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 http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

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Figures

Figure 1
Figure 1
Overview of WGCNA methodology. This flowchart presents a brief overview of the main steps of Weighted Gene Co-expression Network Analysis.
Figure 2
Figure 2
Network visualization plots. A. Log-log plot of whole-network connectivity distribution. The x-axis shows the logarithm of whole network connectivity, y-axis the logarithm of the corresponding frequency distribution. On this plot the distribution approximately follows a straight line, which is referred to as approximately scale-free topology. B. Results of classical multidimensional scaling. Modules tend to form separate 'fingers' in this plot. Intramodular hub genes are located at the finger tips. C. Network heatmap plot. Branches in the hierarchical clustering dendrograms correspond to modules. Color-coded module membership is displayed in the color bars below and to the right of the dendrograms. In the heatmap, high co-expression interconnectedness is indicated by progressively more saturated yellow and red colors. Modules correspond to blocks of highly interconnected genes. Genes with high intramodular connectivity are located at the tip of the module branches since they display the highest interconnectedness with the rest of the genes in the module.
Figure 3
Figure 3
Module and eigengene network plots. A. Barplot of mean gene significance across modules. In this example we use a trait-based gene significance, Equation 2. The higher the mean gene significance in a module, the more significantly related the module is to the clinical trait of interest. B. Scatterplot of gene significance (y-axis) vs. module membership (x-axis) in the most significant module (green module, see panel A). In modules related to a trait of interest, genes with high module membership often also have high gene significance. C. Hierarchical clustering dendrogram of module eigengenes (labeled by their colors) and the microarray sample trait y. D. Heatmap plot of the adjacencies in the eigengene network including the trait y. Each row and column in the heatmap corresponds to one module eigengene (labeled by color) or the trait (labeled by y). In the heatmap, green color represents low adjacency (negative correlation), while red represents high adjacency (positive correlation).
Figure 4
Figure 4
Example WGCNA analysis of liver expression data in female mice. A. Gene dendrogram obtained by average linkage hierarchical clustering. The color row underneath the dendrogram shows the module assignment determined by the Dynamic Tree Cut. B. Heatmap plot of topological overlap in the gene network. In the heatmap, each row and column corresponds to a gene, light color denotes low topological overlap, and progressively darker red denotes higher topological overlap. Darker squares along the diagonal correspond to modules. The gene dendrogram and module assignment are shown along the left and top. C. Hierarchical clustering of module eigengenes that summarize the modules found in the clustering analysis. Branches of the dendrogram (the meta-modules) group together eigengenes that are positively correlated. D. Heatmap plot of the adjacencies in the eigengene network including the trait weight. Each row and column in the heatmap corresponds to one module eigengene (labeled by color) or weight. In the heatmap, green color represents low adjacency (negative correlation), while red represents high adjacency (positive correlation). Squares of red color along the diagonal are the meta-modules. E. A scatterplot of gene significance for weight (GS, Equation 2) versus module membership (MM, Equation 6) in the brown module. GS and MM exhibit a very significant correlation, implying that hub genes of the brown module also tend to be highly correlated with weight. F. The network of the 30 most highly connected genes in the brown module. In this network we only display a connection of the corresponding topological overlap is above a threshold of 0.08.
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