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scenic-gene-regulatory-network

Infer transcription factor regulatory networks and score regulon activities from single-cell RNA-seq data.

32|5|Updated Mar 12, 2026
Search Tags:#scrna-seq#aucell#scenic#regdiffusion#grn-inference#regulatory-network#cistarget
One-click install
npx skills add https://github.com/omicverse/omicclaw --skill scenic-gene-regulatory-network-omicverse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scenic-gene-regulatory-network
Source: https://github.com/omicverse/omicclaw/tree/main/src/omicverse_skills/skills/single-scenic-grn
Command: npx skills add https://github.com/omicverse/omicclaw --skill scenic-gene-regulatory-network-omicverse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Reconstructing transcription factor (TF) regulatory networks and quantifying regulon activity from single-cell RNA-seq is complex, resource-intensive, and sensitive to input format and species-specific databases; this Skill codifies the SCENIC pipeline to automate GRN inference, motif-based pruning, and per-cell regulon scoring while surfacing common failure modes and validation checks.

Core Features & Use Cases

  • Three-stage SCENIC pipeline: fast GRN inference with RegDiffusion, cisTarget-based regulon pruning, and AUCell per-cell activity scoring.
  • Downstream analytics: regulon specificity scores (RSS) to identify master regulators, binary activity matrices, and visualization helpers for embedding and GRN graphs.
  • Operational safeguards: checks for raw counts vs log-normalized data, species-matching of gene names, and verification of large cisTarget ranking and motif files to avoid common failures.
  • Use Cases: discovering cell-type-specific TFs in mouse or human scRNA-seq, comparing regulon activity across conditions, and exporting regulon/aucell results for further analysis.

Quick Start

Run SCENIC on your AnnData using raw count layer, point to species-matched cisTarget .feather rankings and motif .tbl files, and return the top regulons per cell type.

Frequently Asked Questions about scenic-gene-regulatory-network

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I infer transcription factor regulatory networks from scRNA-seq data?▼

Scoring regulon activity in scRNA-seq requires the AUCell algorithm, which operates on raw count-level AnnData matrices to calculate per-cell transcription factor activity scores after cisTarget regulon pruning.

Do I need raw counts or normalized data for SCENIC regulon inference?▼

SCENIC regulon inference requires raw count-level matrices as input. The pipeline includes operational safeguards to check for raw counts versus log-normalized data to prevent common processing failures.

What files are required for cisTarget regulon pruning in mouse or human scRNA-seq?▼

cisTarget regulon pruning requires species-matched cisTarget ranking .feather files and motif .tbl annotations. The pipeline verifies these large database files to ensure species-matching with your gene names.

How does RegDiffusion compare to standard GRN inference for single-cell data?▼

RegDiffusion provides fast GRN inference as the first stage of the SCENIC pipeline, followed by cisTarget pruning and AUCell scoring. It requires parallel workers and is designed specifically for scRNA-seq AnnData inputs.

Why does my SCENIC pipeline fail during species matching for scRNA-seq regulatory networks?▼

SCENIC pipeline failures during species matching often occur when input gene names do not align with the species-matched cisTarget ranking .feather files and motif .tbl annotations required for the workflow.

Can I identify cell-type-specific master regulators from scRNA-seq regulon activity?▼

You can identify cell-type-specific master regulators by calculating regulon specificity scores (RSS) from AUCell per-cell activity matrices, allowing comparison of regulon activity across conditions and cell types.

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