GRN Pipeline - Usage Guide

GRN Pipeline - Usage Guide

Overview

Complete gene regulatory network inference workflow from processed single-cell data to regulon discovery and perturbation simulation. Supports RNA-only analysis with pySCENIC (GRNBoost2 + RcisTarget + AUCell) and multiome analysis with SCENIC+ for enhancer-driven GRNs. Includes CellOracle for in silico perturbation simulation.

Prerequisites

# pySCENIC (RNA-only path)
pip install pyscenic arboreto ctxcore

# SCENIC+ (multiome path)
pip install scenicplus pycisTopic pycistarget

# CellOracle (perturbation simulation)
pip install celloracle

# Common dependencies
pip install scanpy pandas numpy scipy matplotlib

# Download cisTarget databases (once, ~10 GB each)
# Human: hg38_500bp_up_100bp_down and hg38_10kbp_up_10kbp_down
# Mouse: mm10 equivalents
# From: https://resources.aertslab.org/cistarget/databases/

Input data:

• Processed AnnData (QC'd, normalized, clustered) with .raw containing counts
• TF list for species (from cisTarget resources)
• For SCENIC+: paired scRNA-seq and scATAC-seq (multiome)

Quick Start

Tell your AI agent what you want to do:

• "Build gene regulatory networks from my single-cell RNA-seq data"
• "Run pySCENIC to find regulons in my scRNA-seq dataset"
• "Infer enhancer-driven GRNs from my multiome data with SCENIC+"
• "Simulate a TF knockout using CellOracle"

Example Prompts

RNA-Only GRN

"I have a processed scRNA-seq AnnData with cell type annotations. Run pySCENIC to discover regulons and score their activity."

"Find the key transcription factors driving cell identity in my single-cell data."

Multiome GRN

"I have paired RNA and ATAC from a 10x multiome experiment. Build enhancer-driven regulatory networks with SCENIC+."

"Use SCENIC+ to identify which enhancers are driving TF regulons in my data."

Perturbation Simulation

"Simulate what happens if I knock out MYC in my scRNA-seq data using CellOracle."

"Predict cell state transitions after perturbing key transcription factors."

What the Agent Will Do

1. Load processed AnnData and extract expression matrix
2. Run GRN inference (GRNBoost2 for RNA-only, cisTopic + pycistarget for multiome)
3. Prune TF-target links using motif enrichment (RcisTarget)
4. Score regulon activity per cell (AUCell)
5. Validate: check regulon count (50-500), known TFs present, cell type separation
6. Optionally simulate TF perturbations with CellOracle
7. Export regulon activity matrix and annotated AnnData

Tips

• Start with pySCENIC for RNA-only data; upgrade to SCENIC+ only with multiome
• Use raw counts for GRNBoost2 (not normalized values)
• Download both 500bp and 10kbp cisTarget databases for comprehensive motif scanning
• Subsample to ~50k cells if memory is an issue during GRNBoost2
• Expect 50-500 regulons; outside this range suggests parameter tuning needed
• Verify known lineage TFs appear in your regulon list as a sanity check
• CellOracle perturbation results are predictions; validate experimentally

Related Skills

• gene-regulatory-networks/scenic-regulons - pySCENIC implementation details
• gene-regulatory-networks/multiomics-grn - SCENIC+ enhancer-driven GRNs
• gene-regulatory-networks/perturbation-simulation - CellOracle details
• single-cell/clustering - Upstream cell type annotation
• single-cell/preprocessing - QC and normalization before GRN inference