bio-gene-regulatory-networks-scenic-regulons
Infer gene regulatory networks and identify transcription factor regulons from single-cell RNA-seq data using pySCENIC. Discovers co-expression modules with GRNBoost2, prunes by cis-regulatory motif enrichment, and scores regulon activity per cell with AUCell. Use when identifying transcription factor regulons, scoring TF activity in single cells, or finding master regulators of cell identity.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scenic-regulons --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, seaborn 0.13+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # SCENIC Regulons **"Identify transcription factor regulons from my scRNA-seq data"** → Run the pySCENIC three-step pipeline: infer co-expression modules with GRNBoost2, prune by cis-regulatory motif enrichment with cisTarget, and score regulon activity per cell with AUCell. - CLI: `pyscenic grn` → `pyscenic ctx` → `pyscenic aucell` - Python: `arboreto_with_multiprocessing.py` for GRN step (workaround for dask>=2.0) Infer transcription factor regulons from single-cell RNA-seq with the pySCENIC three-step pipeline: GRN inference, motif enrichment, and regulon activity scoring. ## Pipeline Overview | Step | Tool | Description | |------|------|-------------| | 1. GRN inference | GRN
- Version Compatibility
- Pipeline Overview
- Known Issues
- Arboreto / Dask Compatibility
- Python Version
- Required Databases
- Step 1: GRN Inference with GRNBoost2
- Using arboretowithmultiprocessing.py (Recommended)
- Python API (if dask < 2.0)
- Step 2: Regulon Pruning with cisTarget
- CLI Alternative (Steps 1-2)
- Step 3: AUCell Activity Scoring
- CLI Alternative
- Interpreting Results
conda create -n scenic python=3.10 conda activate scenic pip install pyscenic loompy Human hg38 ranking databases (large files, ~1.5 GB each) mc9nr = motif collection v9, nr = non-redundant Motif-to-TF annotations wget https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl Run from command line -- avoids dask compatibility issues entirely python arboreto_with_multiprocessing.py \ filtered.loom \
What does the bio-gene-regulatory-networks-scenic-regulons skill do?
Infer gene regulatory networks and identify transcription factor regulons from single-cell RNA-seq data using pySCENIC. Discovers co-expression modules with GRNBoost2, prunes by cis-regulatory motif enrichment, and scores regulon activity per cell with AUCell. Use when identifying transcription factor regulons, scoring TF activity in single cells, or finding master regulators of cell identity.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scenic-regulons --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.