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