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name: network-inference description: "Use pySCENIC and Arboreto to infer co-expression adjacencies, add TF-target correlations, and build TF-target modules before motif pruning." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Network Inference

Use this sub-skill when a task asks for pySCENIC Phase I work: GRNBoost2 or GENIE3 adjacency inference, Dask-free Arboreto multiprocessing, adding Pearson correlations, or converting adjacencies into TF-target modules.

Read First

• API reference: verified add_correlation() and modules_from_adjacencies() signatures, dataframe schemas, correlation behavior, and module-construction defaults.
• Workflows: CLI recipes for pyscenic grn, pyscenic add_cor, arboreto_with_multiprocessing.py, and Python recipes for module construction.
• Troubleshooting: TF/gene overlap, orientation, duplicate genes, sparse/loom handling, Dask client mode, multiprocessing fallback, missing genes, dropout masking, and min_genes filtering.
• GRN multiprocessing smoke helper: safe import check, tiny fixture writer, API smoke check, and command-template printer.

Route By Task

• Infer adjacencies: use pyscenic grn for Dask-backed GRNBoost2/GENIE3, or the bundled workflows for arboreto_with_multiprocessing.py when Dask is the failure point.
• Add correlations: use pyscenic add_cor or pyscenic.utils.add_correlation() to add rho and regulation columns before module construction or diagnostics.
• Build modules: use pyscenic.utils.modules_from_adjacencies() when a task needs unpruned activating or repressing modules from weighted adjacencies.
• Stay in bounds: route motif database pruning and final regulon creation to the motif-pruning workflow, AUCell scoring/binarization to the AUCell workflow, file conversion/export to the data I/O workflow, and container orchestration to the CLI/container workflow.

Safe Starting Point

Run the bundled helper before writing commands for a user environment:

python scripts/grn_multiprocessing_smoke.py --help
python scripts/grn_multiprocessing_smoke.py --make-fixtures ./pyscenic-grn-smoke --run-api-smoke

The helper checks imports and prints command templates. It creates only tiny local fixtures when requested and does not download data, run GRNBoost2/GENIE3, train models, or mutate existing files.