bulk-wgcna-analysis-with-omicverse

pywgcna within omicverse streamlines WGCNA by automating soft-threshold selection, adjacency and TOM construction, and module detection. It reduces manual parameter tuning so you can reproducibly identify co-expression modules and hub genes from bulk expression matrices.

For weighted gene co-expression network analysis, you must provide a numeric genes-by-samples expression matrix from bulk RNA-seq or microarray data, along with a sample metadata CSV containing trait information for module-trait correlation.

Yes, you can compute module-trait correlations and eigengene analysis using omicverse. The workflow constructs modules, calculates module eigengenes, visualises patterns across metadata, and identifies top hub genes for downstream validation.

This WGCNA pipeline supports both bulk RNA-seq and microarray datasets. It processes any numeric genes-by-samples expression matrix to perform module detection, eigengene analysis, and hub gene identification for exploratory bioinformatics workflows.

The best way to extract top hub genes is to use the omicverse bulk.pyWGCNA hub gene extraction functions after module detection. This identifies highly connected genes within specific modules for downstream biological validation and reporting.