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bio-gene-regulatory-networks-multiomics-grn

Infer enhancer-driven gene regulatory networks from paired scRNA-seq and scATAC-seq data with SCENIC+.

Updated Mar 30, 2026
Search Tags:#multiome#scrna-seq#scatac-seq#scenic+#eregulon#cis-regulatory#cistopic
One-click install
npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-gene-regulatory-networks-multiomics-grn
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-gene-regulatory-networks-multiomics-grn
Source: https://github.com/ya-way/cytoclaw-skills/tree/main/workspace/skills/bio-gene-regulatory-networks-multiomics-grn
Command: npx skills add https://github.com/ya-way/cytoclaw-skills --skill bio-gene-regulatory-networks-multiomics-grn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Build cis-regulatory gene networks by integrating single-cell RNA-seq and ATAC-seq data to identify eRegulons with SCENIC+ and analyze enhancer-linked regulatory programs across cell types.

Core Features & Use Cases

  • Integrates paired scRNA-seq and scATAC-seq data to infer eRegulons (TF–enhancer–gene triplets) using SCENIC+.
  • Utilizes cisTopic for ATAC topic modeling and region-to-gene linking to reveal cis-regulatory programs.
  • Generates eRegulon activity scores and supports downstream visualization and interpretation across cell types.
  • Typical workflow: preprocess data, call peaks with MACS3, run SCENIC+ pipeline, and interpret TF–enhancer–gene links.

Quick Start

Run SCENIC+ on paired scRNA-seq and scATAC-seq data to identify eRegulons and explore TF–enhancer–gene links.

Frequently Asked Questions about bio-gene-regulatory-networks-multiomics-grn

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

FAQPage Schema
How do I infer enhancer-driven gene regulatory networks from paired scRNA-seq and scATAC-seq data?▼

To infer enhancer-driven gene regulatory networks, integrate paired scRNA-seq and scATAC-seq data using SCENIC+ to identify eRegulons linking transcription factors to enhancers and target genes across cell types.

What is the best way to link transcription factors to enhancers and target genes in single-cell multiome data?▼

The best way to link transcription factors to enhancers and target genes is running the SCENIC+ pipeline on 10x multiome or Cell Ranger ARC outputs to score regulatory activity per cell.

Do I need MACS3 and pycisTopic to run SCENIC+ for cis-regulatory network analysis?▼

Yes, you need MACS3 for peak calling and pycisTopic alongside SCENIC+ and cisTopic for ATAC topic modeling to effectively run cis-regulatory network analysis on multiome data.

How does cisTopic topic modeling help identify cis-regulatory programs in scATAC-seq data?▼

cisTopic topic modeling helps identify cis-regulatory programs by processing scATAC-seq data to perform region-to-gene linking, revealing the underlying regulatory mechanisms before running SCENIC+.

Can I use FigR as a lighter alternative to SCENIC+ for scRNA-seq and scATAC-seq integration?▼

Yes, you can use FigR as a lighter alternative to SCENIC+ to integrate scRNA-seq and scATAC-seq data when you need a less computationally intensive method for identifying regulatory programs.

What prerequisites are required to identify eRegulons from 10x multiome outputs?▼

Prerequisites to identify eRegulons from 10x multiome outputs include Python, SCENIC+ with pycisTopic, cisTopic, and MACS3 for peak calling to ensure proper data preprocessing.

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