Weighted Gene Co-expression Network Analysis

WGCNA | Section 1 - Data curation, filtering and sft selection

WGCNA alleviates the multiple testing problem inherent in gene expression data analysis.
Instead of relating thousands of genes to a sample trait, it focuses on the relationship between a few (typically less than 10) modules and the sample trait.

Detailed usage information regarding WGCNA analysis can be found through THIS DOCUMENT.

Species used to curate WGCNA expression matrix
Expression level format

Data transformation method

Expression level filter threshold

Sample proportion filter threshold

SFT power cutoff

Show Examples

Sample Accession List (200 Max)
Gene List (4000 Max)

WGCNA | Section 2 - Co-expression network construction

Project ID
Selected soft power (sft)

Minimum module size

Merged tree cut height threshold

WGCNA | Section 3 - Module-trait correlation analysis

Instructions for preparing the sample-trait table

WGCNA currently only support quality trait analysis, that is, using number 0 or 1 to represent the presence and absence of a certain trait across all samples.
The table must be converted to or written in csv format for the server backend to recognize.

If you want to go through an example analysis, please use the example data from Coix lacryma-jobi var.lacryma-jobi (GCA_009763385.1)
and upload an example sample-trait table provided HERE.

WGCNA Result viewer

WGCNA Module Weighted Gene Co-expression Network Analysis

Instructions for preparing the sample-trait table