In this R software tutorial we review key concepts of weighted gene co-expression network analysis (WGCNA). The tutorial also serves as a small introduction to clustering procedures in R.

Oct 2, 2025 · Weighted gene co-expression network analysis (WGCNA) is a powerful all-in-one analysis method that allows biologists to understand the transcriptome-wide relationships of all …

Weighted correlation network analysis, also known as weighted gene co-expression network analysis (WGCNA), is a widely used data mining method especially for studying biological …

The WGCNA package requires the following packages to be installed: stats, fields, impute, grDevices, dynamicTreeCut (1.20 or higher), qvalue, utils, and flashClust.

Aug 2, 2025 · WGCNA has been widely applied across various fields of biological research, providing insights that traditional gene analysis methods might miss. In cancer research, it …

There are many gene correlation network builders but we shall provide an example of the WGCNA R Package. The WGCNA R package builds “weighted gene correlation networks for …

Sep 18, 2024 · Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to …

In this video I demonstrate how to perform Weighted Gene Co-expression Network Analysis (WGCNA) using a RNA-Seq dataset. I go over data manipulation, methods to detect outlier …

Weighted Gene Co-expression Network Analysis (WGCNA) is a powerful method for identifying gene modules, relating them to traits, and discovering hub genes in large-scale omics datasets.

This R script is to demonstrate Weighted Correlation Network Analysis (WGCNA) using R. This is the repository of the files and R script needed for the tutorial in the Youtube Channel (Liquid …