omicverse/omicclaw/bulk-wgcna-analysis

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().

1. 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]]).

1. 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.

1. Preprocess the dataset

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

1. 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().

1. 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.

1. 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(...).

1. Update sample metadata for downstream analyses

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

1. 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(...).

1. Find hub genes

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

1. Defensive validation

``python # 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" ``

1. 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: see the tutorial page for the linked 5xFAD example data: t_wgcna
• Quick copy/paste commands: `reference.md`