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scenic-grn-inference

Infer TF→region→gene regulatory networks from multi-omics data using SCENIC+.

1|Updated Nov 20, 2025
Search Tags:#scRNA-seq#transcription-factors#scATAC-seq#multiome#grn#scenic-plus#eregulons
One-click install
npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill scenic-grn-inference
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scenic-grn-inference
Source: https://github.com/tony-zhelonkin/SciAgent-toolkit/tree/main/skills/scenic-grn-inference
Command: npx skills add https://github.com/tony-zhelonkin/SciAgent-toolkit --skill scenic-grn-inference

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the inference of TF→region→gene regulatory relationships from paired scRNA-seq and scATAC-seq data, enabling mechanistic insights into gene regulation.

Core Features & Use Cases

  • SCENIC+ based GRN inference from multiome data, producing eRegulons (TF→region→gene) with motif enrichment and GBM scoring.
  • Supports both paired multiome data and unpaired RNA/ATAC data using metacell sampling to build pseudo-multiome representations.
  • Flexible workflows via Snakemake and a Python API for interactive exploration, with options to customize cisTarget databases and metadata preparation.
  • Use cases include decoding tissue-specific regulatory programs, comparing conditions, and prioritizing TF-region-gene triplets for experimental validation.

Quick Start

Prepare scRNA-seq and scATAC-seq data, configure SCENIC+ with cisTarget databases, and run the Snakemake pipeline or Python API to generate eRegulons.

Frequently Asked Questions about scenic-grn-inference

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

FAQPage Schema
How do I infer enhancer-driven GRNs from multiome data?▼

You infer enhancer-driven GRNs from multiome data by applying the SCENIC+ pipeline to build eRegulons, mapping TF→region→gene relationships via motif enrichment and GBM scoring.

Can I build eRegulons from unpaired scRNA-seq and scATAC-seq data?▼

Yes, you can build eRegulons from unpaired scRNA-seq and scATAC-seq data by using metacell sampling approaches to construct pseudo-multiome representations for the SCENIC+ pipeline.

What is an eRegulon in transcription factor regulatory networks?▼

An eRegulon is an enhancer-driven regulatory network unit mapping TF→region→gene relationships, inferred from multi-omics data using motif enrichment and GBM scoring to provide mechanistic gene regulation insights.

Do I need pycisTopic and pycistarget to run SCENIC+ GRN inference?▼

Yes, you need pycisTopic and pycistarget configured with correct cisTarget databases and preprocessed scRNA-seq and scATAC-seq inputs to execute the SCENIC+ GRN inference pipeline.

What's the best way to customize cisTarget databases for eRegulon inference?▼

The best way to customize cisTarget databases for eRegulon inference is using the flexible Snakemake pipeline or standalone Python API, allowing interactive exploration and metadata preparation adjustments.

Does this SCENIC+ workflow support Snakemake for batch processing multiome data?▼

Yes, this SCENIC+ workflow supports Snakemake for batch processing multiome data, alongside a Python API for interactive exploration of eRegulons and TF→region→gene regulatory relationships.

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