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bulk-wgcna-analysis-with-omicverse

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Assist Claude in running PyWGCNA through omicverse—preprocessing expression matrices, constructing co-expression modules, visualising eigengenes, and extracting hub genes.

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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