tools-only Multiomics GRN Inference - Usage Guide
Overview
Build enhancer-driven gene regulatory networks by integrating single-cell RNA-seq and ATAC-seq data. SCENIC+ extends the SCENIC framework to discover eRegulons: triplets linking transcription factors to their target enhancers and downstream regulated genes. This enables identification of cis-regulatory programs driving cell identity.
Prerequisites
# SCENIC+ and dependencies
pip install scenicplus pycisTopic
# Peak calling
pip install macs3
# Additional
pip install scanpy loompy matplotlib seaborn
# Download cisTarget databases for SCENIC+
# See: https://resources.aertslab.org/cistarget/
# FigR alternative (R)
devtools::install_github('buenrostrolab/FigR')
BiocManager::install(c('Signac', 'Seurat'))
Quick Start
Tell your AI agent what you want to do:
- "Infer gene regulatory networks from my 10x multiome data"
- "Run SCENIC+ on my paired scRNA+scATAC dataset"
- "Find eRegulons linking TFs to enhancers and target genes"
- "Build a multiomics GRN from my CellRanger ARC output"
Example Prompts
SCENIC+ Pipeline
"I have 10x multiome data processed with CellRanger ARC. Run SCENIC+ to identify eRegulons."
"Infer enhancer-driven regulatory networks from my paired scRNA-seq and scATAC-seq data."
Interpretation
"Which TFs have the most enhancer targets in my eRegulon results?"
"Show activating vs repressive eRegulons for each cell type."
FigR Alternative
"Run FigR on my Seurat multiome object to find TF-gene regulatory links."
What the Agent Will Do
- Load paired scRNA-seq and scATAC-seq data
- Call peaks from ATAC fragments with MACS3
- Run cisTopic for topic modeling on scATAC regions
- Link accessible regions to target genes (region-to-gene)
- Connect TFs to enhancers via motif enrichment (TF-to-region)
- Assemble eRegulons (TF-enhancer-gene triplets)
- Score eRegulon activity per cell and visualize
Tips
- Memory requirements - SCENIC+ needs significant RAM: 64 GB for 20K cells, 128 GB+ for 50K+ cells
- cisTopic topics - Test multiple topic numbers (10-50) and select by coherence metrics
- Peak calling - Use MACS3 with BEDPE format on the fragments file for the region universe
- Activating vs repressive - eRegulons with (+) suffix have positive TF-target correlation; (-) suffix indicates repressive regulation
- FigR is lighter weight - If SCENIC+ is too resource-intensive, FigR provides a simpler alternative for TF-gene regulatory inference from multiome data
Related Skills
- scenic-regulons - RNA-only regulon inference with pySCENIC
- single-cell/scatac-analysis - scATAC-seq preprocessing with Signac and ArchR
- atac-seq/atac-peak-calling - Peak calling for chromatin accessibility
- chip-seq/motif-analysis - Motif enrichment for TF identification