Bulk Wgcna Analysis With Omicverse

• Create pyWGCNA_5xFAD = ov.bulk.pyWGCNA(name=..., species='mus musculus', geneExp=data.T, outputPath='', save=True).
• Confirm pyWGCNA_5xFAD.geneExpr looks correct before proceeding.

• Run pyWGCNA_5xFAD.preprocess() to drop low-expression genes and problematic samples.

• Evaluate soft-threshold power: pyWGCNA_5xFAD.calculate_soft_threshold().
• Build adjacency and TOM matrices via calculating_adjacency_matrix() and calculating_TOM_similarity_matrix().

• 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.

• 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(...).

• Load sample annotations updateSampleInfo(path='.../sampleInfo.csv', sep=',').
• Assign colour maps for metadata categories with setMetadataColor(...).

• Run analyseWGCNA() to compute module–trait statistics.
• Plot module eigengene heatmaps and bar charts with plotModuleEigenGene(module, metadata, show=True) and barplotModuleEigenGene(...).

• Identify top hubs per module using top_n_hub_genes(moduleName='lightgreen', n=10).

# Before WGCNA: verify enough genes remain after MAD filtering
assert data.shape[0] >= 1000, f"Only {data.shape[0]} genes after filtering — WGCNA needs >1000 for meaningful modules"
# Verify expression values are numeric and non-negative
assert data.dtypes.apply(lambda d: d.kind in 'iuf').all(), "Expression matrix contains non-numeric columns"
# Verify enough samples for network construction
assert data.shape[1] >= 6, f"Only {data.shape[1]} samples — WGCNA needs >=6 samples for reliable co-expression"

• 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.