bulkrna-coexpression

Research

Load when discovering gene co-expression modules and hub genes in a bulk RNA-seq cohort via WGCNA-style soft-thresholded networks. Skip for direct DE comparison (use bulkrna-de) or PPI lookup of an existing gene list (use bulkrna-ppi-network); single-cell co-expression uses sc-grn instead.

QUICK START

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bulkrna-coexpression

When to use

Run on a bulk RNA-seq cohort (≥15 samples recommended; works on smaller sets but module structure is unstable below that) when you want to find groups of co-regulated genes ("modules") and the hub genes within each. Soft-thresholded correlation network in the WGCNA style; outputs module assignments, hub genes, and module-trait correlations.

Inputs & Outputs

Input	Format	Required
Count matrix	`.csv` (gene × sample)	yes (or `--demo`)
Sample traits	`--traits` CSV	optional, for module-trait correlation

Output	Path	Notes
Module assignments	`tables/module_assignments.csv`	gene → module colour
Hub genes	`tables/hub_genes.csv`	per-module top-connectivity genes
Soft-threshold diagnostic	`tables/threshold_fit.csv` + `figures/scale_free_fit.png`	per-power scale-free fit + connectivity
Module sizes	`figures/module_sizes.png`	bar chart of gene count per module
Module assignments	`figures/module_dendrogram.png`	colour-strip of per-gene module label
Report	`report.md` + `result.json`	summary keys: `n_genes_used`, `n_samples`, `soft_power`, `n_modules`, `module_sizes`, `method_used`

Flow

Load count matrix; validate --input (bulkrna_coexpression.py:721,724 parser-error / FileNotFoundError). Demo path uses :54's built-in fixture.
Validate sample count: :377 raises ValueError("WGCNA requires >= 8 samples ...") below 8; :382 warns "Low sample count" between 8 and 15 but proceeds.
Try the R WGCNA bridge (_run_wgcna_r via subprocess); :390 raises RuntimeError("R WGCNA failed: ...") if R or the WGCNA package is unavailable.
The Python helper _select_soft_threshold (:70) is a sanity-check / diagnostic that scores candidate powers by scale-free R² — used as a fallback / exploratory aid, not the production estimator. R WGCNA's own pickSoftThreshold drives the real run.
Build modules in R; collect assignments + hub genes; emit module_assignments.csv, hub_genes.csv, threshold_fit.csv.

Gotchas

WGCNA hard-fails below 8 samples. bulkrna_coexpression.py:377 raises ValueError. Between 8 and 15 the run proceeds but :382 warns "Low sample count (N). WGCNA recommends >= 15 samples for reliable module detection." — treat any modules from <15-sample cohorts as exploratory.
R WGCNA is required for the production path. :390 raises RuntimeError with installation instructions if R or the WGCNA package isn't importable. There is no Python-only fallback that produces module assignments — installing R+WGCNA is mandatory for non-demo runs.
Per-power scale-free R² is in tables/threshold_fit.csv, not result.json. The summary dict (:455-464) carries soft_power (the chosen power) but no R² value; inspect the threshold-fit table to assess scale-free quality. Below R² ≈ 0.8 the network is not scale-free and modules become noise.
No biological-replicate filter. Unlike PyDESeq2, this skill makes no distinction between technical and biological replicates. Modules built on a cohort with hidden batch structure will reflect the batch, not biology — run bulkrna-batch-correction upstream if PCA shows batch separation.
Gene IDs must match between counts and traits. No automatic mapping — feed counts and traits with consistent identifier system, or run bulkrna-geneid-mapping first.
Hub genes are connectivity-based, not necessarily biology-load-bearing. A hub in WGCNA means "highest intramodular correlation" — useful as a starting hypothesis but not proof of regulatory primacy. Validate with knockdown / knockout data or eQTL evidence.

Key CLI

python omicsclaw.py run bulkrna-coexpression --demo
python omicsclaw.py run bulkrna-coexpression \
  --input counts.csv --output results/
python omicsclaw.py run bulkrna-coexpression \
  --input counts.csv --traits clinical.csv --output results/

See also

references/parameters.md — every CLI flag and tuning hint
references/methodology.md — soft-thresholding, module detection, hub-gene definition
references/output_contract.md — exact output directory layout
Adjacent skills: bulkrna-batch-correction (run upstream if batches suspected), bulkrna-de (parallel: differential expression), bulkrna-ppi-network (parallel: STRING PPI on a gene list), sc-grn (single-cell sibling using GRNBoost2 / pySCENIC)