In the following, we focus on gene co-expression networks which represent a major application of correlation network methodology. Co-expression networks have been found useful for describing the pairwise relationships among gene transcripts [2-9]. In co-expression networks, we refer to nodes as 'genes', to the node profile xi as the gene expression profile, and to the node significance measure GSi as the gene significance measure. A glossary of important network-related terms can be found in Table 1. Here we introduce an R software package that summarizes and extends our earlier work on weighted gene co-expression network analysis (WGCNA) [5,10-12]. WGCNA has been used to analyze gene expression data from brain cancer [10], yeast cell cycle [13], mouse genetics [14-17], primate brain tissue [18-20], diabetes [21], chronic fatigue patients [22] and plants [23]. While these publications have made R software code available in various forms, there is a need for a comprehensive R package that summarizes and standardizes methods and functions. To address this need, we introduce the WGCNA R package which also includes enhanced and novel functions for co-expression network analysis.
