bio-gene-regulatory-networks-scenic-regulons
PKU-YuanGroup/OpenAI4SInfer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and…
Scores out of 100 · grade A
2026-08-22Works
40% of the score100/100
- Loads cleanly: valid frontmatter, required fields present, no dangling references.
Maintained
25% of the score100/100
- no commits in the last 12 weeks
Adopted
20% of the score35/100
- 335 stars on the source repo.
Documented
15% of the score100/100
- 1,772 words with worked examples.
- Ships 3 bundled files.
Install
npx skills add PKU-YuanGroup/OpenAI4S/bio-gene-regulatory-networks-scenic-regulonsWhat it says it does
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
Also in PKU-YuanGroup/OpenAI4S
| Artifact | Score | What the check found | Type | Reach | Last commit |
|---|---|---|---|---|---|
| bio-atac-seq-allele-specific-accessibilityPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-atac-peak-callingPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-consensus-peaksetPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-deep-learning-atacPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-atac-seq-footprintingPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today | |
| bio-causal-genomics-effector-gene-prioritizationPKU-YuanGroup/OpenAI4S | clean | Skill | 335 stars | today |
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