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

• 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() .

• 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]] ).

• 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.

• Preprocess the dataset

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

• 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() .

• 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.

• 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(...) .

• Update sample metadata for downstream analyses

• Load sample annotations updateSampleInfo(path='.../sampleInfo.csv', sep=',') .

• Assign colour maps for metadata categories with setMetadataColor(...) .

• 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(...) .

• Find hub genes

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

• Defensive validation

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"

• 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