scRBP

scRBP is a command-line toolkit that integrates GRN inference, motif enrichment, Regulong-level polygenic scoring, and regulon activity quantification into a unified 10-step pipeline — from raw scRNA-seq data to disease-relevant RBP-regulon rankings.

RBP Regulon Inference

Construct high-confidence RBP–gene and RBP–isoform regulatory networks using GRNBoost2/GENIE3 with motif-binding evidence pruning via ctxcore. Supports gene-level (--mode gene) and isoform-level (--mode isoform) inference.

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Regulon Activity Scoring

Quantify RBP regulon activity per cell or per cell type using the AUCell algorithm (RAS). Statistical significance is assessed via Monte Carlo sampling with matched null regulons.

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Disease-Associated Regulons

Link GWAS genetic signals to RBP regulons (RGS),then integrate with RAS to compute unified Trait Relevance Scores (TRS).

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What scRBP Does

scRBP (Single-cell RNA-Binding Protein Regulon Inference) is a command-line toolkit for comprehensive analysis of RNA-binding proteins (RBPs) in single-cell RNA-seq data. It provides a systematic, scalable and integrative framework to infer RBP-mediated gene and isoform regulatory networks (“regulons”) from single-cell transcriptomes and prioritize critical regulons underlying complex traits and diseases.

scRBP is comprised of six main modules:

• RBP compendium — A comprehensive database of RBPs and associated motif clusters from diverse public resources
• Target inference — Motif-guided transcriptome-wide inference of RBP targets at gene- and isoform-level resolution
• Network construction — RBP–gene and RBP–isoform co-expression networks from short- or long-read scRNA-seq data
• Regulon scoring — High-fidelity regulon definition and cell type-specific Regulon Activity Scores (RAS)
• GWAS integration — GWAS enrichment to compute Regulon-level Genetic association Scores (RGS)
• Trait relevance — Unified Trait Relevance Score (TRS) combining RAS and RGS per regulon per cellular context

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Pipeline at a Glance

Raw single-cell data (.h5ad / .feather)
          │
          ▼
[Step 1]  scRBP getSketch        ── Stratified GeoSketch cell downsampling (Optional)
          │
          ▼
[Step 2]  scRBP getGRN           ── GRNBoost2/GENIE3 RBP→Gene/Isoform inference
          │                          (run N seeds for robustness, default 30 times)
          ▼
[Step 3]  scRBP getMerge_GRN     ── Merge N-seed GRNs → consensus network
          │
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[Step 4]  scRBP getModule        ── Extract regulon candidates (Top-N / percentile)
          │
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[Step 5]  scRBP getPrune         ── Motif-enrichment pruning via ctxcore
          │
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[Step 6]  scRBP getRegulon       ── Export pruned regulons to GMT format
          │
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[Step 7]  scRBP mergeRegulons    ── Merge region-specific GMT files
          │                          (3'UTR / 5'UTR / CDS / Introns)
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[Step 8]  scRBP ras              ── Regulon Activity Score (RAS) per cell / cell type
          │                          (--mode sc | --mode ct)
          ▼
[Step 9]  scRBP rgs              ── Regulon-level Genetic association Score (RGS)
          │                          (--mode sc | --mode ct)
          ▼
[Step 10] scRBP trs              ── Trait Relevance Score (TRS = RAS + RGS)
                                     (--mode sc | --mode ct)

Step	Command	Key Input	Key Output
1	getSketch	.h5ad / .feather	Downsampled cells
2	getGRN	Expression matrix, RBP list	*_scRBP_gene_GRNs.tsv
3	getMerge_GRN	Multiple GRN .tsv files	Consensus GRN .tsv
4	getModule	Consensus GRN .tsv	Modules .tsv
5	getPrune	Modules, motif databases	Pruned scores (Parquet)
6	getRegulon	Pruned scores	Regulons .gmt (symbol + Entrez)
7	mergeRegulons	Multiple .gmt files	Merged .gmt
8	ras	Expression matrix, .gmt	RAS matrix .csv
9	rgs	MAGMA .genes.raw, .gmt	RGS scores .csv
10	trs	RAS .csv, RGS .csv	TRS scores .csv

View Full Pipeline API Reference

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Quick Start

# Install
pip install scRBP

# Downsample large datasets (recommended for >300,000 cells)
scRBP getSketch --input data.h5ad --output sketch.feather --n_cells 50000

# Infer GRN (run 30 seeds for robustness)
for seed in {1..30}; do
  scRBP getGRN --input sketch.feather --output grn_seed${seed}.tsv --seed $seed
done

# Build regulons and score activity
scRBP getMerge_GRN --input_dir grn_seeds/ --output merged_grn.tsv
scRBP getModule    --input merged_grn.tsv  --output modules/
scRBP getPrune     --input modules/ --output pruned/ \
                   --motif_db motif_db.feather --rankings_db rankings.feather
scRBP getRegulon   --input pruned/ --output regulons.gmt
scRBP ras          --input data.h5ad --regulons regulons.gmt --output ras.csv

Full Quick Start Guide Installation Guide

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Citation

If you use scRBP in your research, please cite:

Ma Y. et al. Decoding cell-specific RNA-binding protein regulatory networks across development and disease. (2026)

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Links

• GitHub: https://github.com/mayunlong89/scRBP
• PyPI: https://pypi.org/project/scRBP
• Issues: https://github.com/mayunlong89/scRBP/issues

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License

scRBP is released under the MIT License.