tiangzlab/omicsclaw/bulkrna-coexpression

bulkrna-coexpression

When to use

Load when discovering bulk gene co-expression modules and hub genes with R WGCNA. Skip direct expression contrasts (use bulkrna-de), existing-gene-list PPI lookup (use bulkrna-ppi-network), or single-cell networks (use sc-grn).

Use from a step

from skills._sdk.notebook import load_skill, write_output
library = load_skill("bulkrna-coexpression")
result = library.analyze(data, power=6, min_module_size=10)
write_output(result, "tables/result.csv")
write_output(library.module_sizes_figure(result), "figures/result.png")

Read expression and metadata with read_input before calling the library. examples/example_step.py constructs a small synthetic dataset and checks its results through the step runner and fresh-kernel replay.

API

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analyze(data: pd.DataFrame, *, power: int | None=None, min_module_size: int=10, random_state: int=54321) -> pd.DataFrame

Return R WGCNA gene-module assignments, leaving expression unchanged.

:param data: Nonnegative feature-by-sample expression; provide normalized data on the intended correlation scale. :param power: CLI default None selects the soft threshold; set a positive integer to override it. :param min_module_size: CLI default 10 genes per module. :param random_state: WGCNA blockwiseModules default seed 54321; fixes its preclustering. :returns: Gene/module DataFrame with diagnostics, hub genes and threshold fit in attrs. :raises ValueError: Input, sample count, power or module size is invalid. :raises ImportError: R WGCNA or Matrix is unavailable. :raises RuntimeError: R analysis fails.

run_info(data: pd.DataFrame, *, keep: bool=True) -> dict

Return WGCNA method diagnostics and summary.

:param data: Result from analyze. :param keep: Keep diagnostics by default; the CLI passes False. :returns: Independent diagnostics dictionary. :raises ValueError: No WGCNA diagnostics are present.

threshold_fit(data: pd.DataFrame) -> pd.DataFrame

Return the signed scale-free fit for each tested soft threshold.

:param data: Result from analyze. :returns: Table containing power, r_squared and mean_connectivity. :raises ValueError: No fit table is stored.

hub_genes(data: pd.DataFrame) -> pd.DataFrame

Return the highest absolute module-membership genes per non-grey module.

:param data: Result from analyze. :returns: Table with gene, module and kME columns. :raises ValueError: No hub table is stored.

module_sizes_figure(data: pd.DataFrame)

Plot assignment counts, including grey unassigned genes.

:param data: Gene/module table from analyze. :returns: Matplotlib Figure. :raises ValueError: The module column is missing.

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Methods and parameters

The function library returns DataFrames and Figures. The CLI loads the same library and owns reports and file writes. R runs in a temporary directory using Matrix Market, feature/sample identifiers and metadata. No R intermediate is a permanent CLI output.

Gotchas

• analyze requires R WGCNA and Matrix, at least eight samples, and finite nonnegative expression. Cohorts with fewer than 15 samples emit a warning.
• analyze(power=None) selects a soft threshold; an explicit power is honored. Integer matrices are converted to double for WGCNA.
• run_info()["filtered_genes"] lists genes removed by WGCNA quality checks. Grey is unassigned; module IDs are color strings.
• hub_genes ranks absolute module membership within each non-grey module. This is correlation evidence, not proof of regulation.
• threshold_fit reports signed scale-free R-squared and connectivity. The correlation analysis uses the supplied expression scale; normalize upstream as needed.
• analyze(random_state=54321) preserves the WGCNA default seed and uses one R thread. No Python module-detection fallback is used.

Inputs and outputs

Expression CSV with genes in the first column and samples in the remaining columns. The API takes that gene column as the DataFrame index.

CLI outputs:

• tables/module_assignments.csv
• tables/hub_genes.csv
• tables/threshold_fit.csv
• figures/scale_free_fit.png
• figures/module_sizes.png
• figures/module_dendrogram.png (assignment overview, not a dendrogram)
• report.md, result.json
• reproducibility/commands.sh

CLI

python skills/bulkrna/bulkrna-coexpression/bulkrna_coexpression.py --demo --output /tmp/bulkrna_coexpression_demo

Run the script with --help for real-input arguments.

See also

• references/parameters.md
• references/methodology.md
• references/output_contract.md

Dependencies

matplotlib, numpy, pandas, scipy, WGCNA, Matrix