Bulk Wgcna Analysis With Omicverse

Overview

Activate this skill for users who want to reproduce the WGCNA workflow from t_wgcna.ipynb. It guides you through loading expression data, configuring PyWGCNA, constructing weighted gene co-expression networks, and inspecting modules of interest.

Instructions

1. Prepare the environment
   • Import omicverse as ov, scanpy as sc, matplotlib.pyplot as plt, and pandas as pd.
   • Set plotting defaults via ov.plot_set().
2. Load and filter expression data
   • Read expression matrices (e.g., from expressionList.csv).
   • Calculate median absolute deviation with from statsmodels import robust and gene_mad = data.apply(robust.mad).