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)

     .
   • Keep the top variable genes (e.g.,

     data = data.T.loc[gene_mad.sort_values(ascending=False).index[:2000]]

     ).
3. Initialise PyWGCNA
   • Create

     pyWGCNA_5xFAD = ov.bulk.pyWGCNA(name=..., species='mus musculus', geneExp=data.T, outputPath='', save=True)

     .
   • Confirm

     pyWGCNA_5xFAD.geneExpr

     looks correct before proceeding.
4. Preprocess the dataset
   • Run

     pyWGCNA_5xFAD.preprocess()

     to drop low-expression genes and problematic samples.
5. Construct the co-expression network
   • Evaluate soft-threshold power:

     pyWGCNA_5xFAD.calculate_soft_threshold()

     .
   • Build adjacency and TOM matrices via

     calculating_adjacency_matrix()

     and

     calculating_TOM_similarity_matrix()

     .
6. Detect gene modules
   • Generate dendrograms and modules:

     calculate_geneTree()

     ,

     calculate_dynamicMods(kwargs_function={'cutreeHybrid': {...}})

     .
   • Derive module eigengenes with

     calculate_gene_module(kwargs_function={'moduleEigengenes': {'softPower': 8}})

     .
   • Visualise adjacency/TOM heatmaps using

     plot_matrix(save=False)

     if needed.
7. Inspect specific modules
   • Extract genes from modules with

     get_sub_module([...], mod_type='module_color')

     .
   • Build sub-networks using

     get_sub_network(mod_list=[...], mod_type='module_color', correlation_threshold=0.2)

     and plot them via

     plot_sub_network(...)

     .
8. Update sample metadata for downstream analyses
   • Load sample annotations

     updateSampleInfo(path='.../sampleInfo.csv', sep=',')

     .
   • Assign colour maps for metadata categories with

     setMetadataColor(...)

     .
9. Analyse module–trait relationships
   • Run

     analyseWGCNA()

     to compute module–trait statistics.
   • Plot module eigengene heatmaps and bar charts with

     plotModuleEigenGene(module, metadata, show=True)

     and

     barplotModuleEigenGene(...)

     .
10. Find hub genes
    • Identify top hubs per module using

      top_n_hub_genes(moduleName='lightgreen', n=10)

      .
11. Troubleshooting tips
    • Large datasets may require increasing

      save=False

      to avoid writing many intermediate files.
    • If module detection fails, confirm enough genes remain after MAD filtering and adjust

      deepSplit

      or

      softPower

      .
    • Ensure metadata categories have assigned colours before plotting eigengene heatmaps.

Examples

• "Build a WGCNA network on the 5xFAD dataset, visualise modules, and extract hub genes from the lightgreen module."
• "Load sample metadata, update colours for sex and genotype, and plot module eigengene heatmaps."
• "Create a sub-network plot for the gold module using a correlation threshold of 0.2."

References

• Tutorial notebook:

  t_wgcna.ipynb

• Tutorial dataset:

  data/5xFAD_paper/

• Quick copy/paste commands:

  reference.md