WGCNA: an R package for weighted correlation network analysis

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.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 webcite.Correlation networks are increasingly being used in biology to analyze large, high-dimensional data sets. 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 (i = 1, . . ., n) and the column indices (l = 1, . . ., m) correspond to sample measurements:We refer to the i-th row xi as the i-th node profile across m sample measurements.Sometimes a quantitative measure (referred to as sample trait) is provided for the columns of X. For example, T = (T1, . . ., Tm) could measure survival time or it could be a binary indicator variable (disease status). Abstractly speaking, we define a sample trait T as a vector with m components that correspond to the columns of the data matrix X. A sample trait can be used to define a node significance measure. For example, a trait-based node significance measure can be defined as the absolute value of the correlation between the i-th node profile xi and the sample trait T:Alternatively, a correlation test p-value [1] or a