Tutorials for the WGCNA package for R: WGCNA Background and glossary

WGCNA begins with the understanding that the information captured by microarray experiments is far richer than a list of differentially expressed genes. Rather, microarray data are more completely represented by considering the relationships between measured transcripts, which can be assessed by pair-wise correlations between gene expression profiles. In most microarray data analyses, however, these relationships go essentially unexplored. WGCNA starts from the level of thousands of genes, identifies clinically interesting gene modules, and finally uses intramodular connectivity, gene significance (e.g. based on the correlation of a gene expression profile with a sample trait) to identify key genes in the disease pathways for further validation. WGCNA alleviates the multiple testing problem inherent in microarray data analysis. Instead of relating thousands of genes to a microarray sample trait, it focuses on the relationship between a few (typically less than 10) modules and the sample trait. Toward this end, it calculates the eigengene significance (correlation between sample trait and eigengene) and the corresponding p-value for each module. The module definition does not make use of a priori defined gene sets. Instead, modules are constructed from the expression data by using hierarchical clustering. Although it is advisable to relate the resulting modules to gene ontology information to assess their biological plausibility, it is not required. Because the modules may correspond to biological pathways, focusing the analysis on intramodular hub genes (or the module eigengenes) amounts to a biologically motivated data reduction scheme. Because the expression profiles of intramodular hub genes are highly correlated, typically dozens of candidate biomarkers result. Although these candidates are statistically equivalent, they may differ in terms of biological plausibility or clinical utility. Gene ontology information can be useful for further prioritizing intramodular hub genes. Examples of biological studies that show the importance of intramodular hub genes can be found reported in [4, 1, 2, 3, 5]. A flow chart of a typical network analysis is shown in Fig. 1. Below we present a short glossary of important network-related terms.