Bulk WGCNA analysis with omicverse

Overview

Activate this skill for users who want to reproduce the WGCNA workflow from [twgcna.ipynb](../../omicverseguide/docs/Tutorials-bulk/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 genemad = data.apply(robust.mad). - Keep the top variable genes (e.g., data = data.T.loc[genemad.sort_values(ascending=False).index[:2000]]).

1. Initialise PyWGCNA

- Create pyWGCNA5xFAD = ov.bulk.pyWGCNA(name=..., species='mus musculus', geneExp=data.T, outputPath='', save=True). - Confirm pyWGCNA5xFAD.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: pyWGCNA5xFAD.calculatesoftthreshold(). - Build adjacency and TOM matrices via calculatingadjacencymatrix() and calculatingTOMsimilaritymatrix().

1. Detect gene modules

- Generate dendrograms and modules: calculategeneTree(), calculatedynamicMods(kwargsfunction={'cutreeHybrid': {...}}). - Derive module eigengenes with calculategenemodule(kwargsfunction={'moduleEigengenes': {'softPower': 8}}). - Visualise adjacency/TOM heatmaps using plot_matrix(save=False) if needed.

1. Inspect specific modules

- Extract genes from modules with getsubmodule([...], modtype='modulecolor'). - Build sub-networks using getsubnetwork(modlist=[...], modtype='modulecolor', correlationthreshold=0.2) and plot them via plotsubnetwork(...).

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 topnhub_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: [twgcna.ipynb](../../omicverseguide/docs/Tutorials-bulk/t_wgcna.ipynb)
• Tutorial dataset: [data/5xFADpaper/](../../omicverseguide/docs/Tutorials-bulk/data/5xFAD_paper/)
• Quick copy/paste commands: [reference.md](reference.md)