Bioinformnatics Workbook: Network analysis with WGCNA

The meticulous signature of Bioinformatics Workbook tutorials makes building weighted gene correlation networks a breeze.

Bioinformnatics Workbook: Network analysis with WGCNA

Skill Level


Best For

Learning how to navigate WGCNA at the command line


WGCNA is an R package for building weighted gene correlation networks for analysis (thus the name) from expression data. Simply put, gene co-expression networks identify genes with similar expression patterns across different conditions. Weighted correlation networks extend to this to identify clusters, or modules, of highly correlated genes and integrate complimentary genomic datasets. With a basic understanding of weighted gene correlation networks you are set to work through the Bioinformatics Workbook tutorial, “Network Analysis with WGCNA”. In addition to the frequently internal links to helpful related content throughout this tutorial, we love the Bioinformatics Workbook resource for their dedication to providing meticulously guided steps, external resources, and conceptual graphics of core concepts.


If you’re looking to learn how to use the WGCNA R package, we reccomend skipping the tutorials provided by the package developper and opting for the “Network analysis with WGCNA” tutorial by Bioknformatics Workbook instead!