scRBP 0.1.3

Single-cell RNA Binding Protein Regulon Inference

scRBP

A scalable framework for inferring RNA-binding protein regulons from single-cell transcriptomic data

scRBP — Single-cell RNA Binding Protein Regulon Inference

scRBP is a command-line toolkit for comprehensive analysis of RNA-binding proteins (RBPs) in single-cell RNA-seq data. scRBP provides a systematic, scalable and integrative framework to infer RBP-mediated gene and isoform regulatory networks ("regulons") from single-cell transcriptomes and prioritize networks underlying complex genetic traits and disorders. scRBP is comprised of six main modules: (i) developing a comprehensive compendium of RBPs and their associated motif clusters from diverse public resources; (ii) systematic, motif-guided transcriptome-wide inference of RBP targets at both gene- and isoform-level resolution; (iii) construction of RBP-gene and/or RBP-isoform co-expression networks from short- or long-read single-cell transcriptomic data, respectively; (iv) defining high-fidelity regulons by integrating RBP-target interactions, and quantifying cell type-specific regulon activity scores (RAS); (v) integrating GWAS results to compute regulon-level genetic association scores (RGS); and (vi) constructing a unified trait-relevance score (TRS) by combining RAS and RGS for each regulon in a given cellular context, with statistical significance assessed using Monte Carlo (MC) sampling.

---

What scRBP Does

RBPs are key post-transcriptional regulators that control mRNA splicing, stability, and translation. scRBP enables you to:

• Construct which RBPs regulate which genes or isoforms in your single-cell data
• Prune raw RBP–gene associations using motif-binding evidence to obtain high-confidence regulons
• Score each cell or cell type for regulon activity score (RAS) using the AUCell algorithm
• Link RBP regulons to human disease through GWAS genetic enrichment (RGS via MAGMA)
• Integrate RAS and RGS into a unified Trait Relevance Score (TRS) that ranks disease-relevant RBPs

---

Pipeline at a Glance

Raw single-cell data (.h5ad / .feather)
          │
          ▼
[Step 1]  scRBP getSketch        ── Stratified GeoSketch cell downsampling
          │
          ▼
[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
          │
          ▼
[Step 4]  scRBP getModule        ── Extract regulon candidates (Top-N / percentile)
          │
          ▼
[Step 5]  scRBP getPrune         ── Motif-enrichment pruning via ctxcore
          │
          ▼
[Step 6]  scRBP getRegulon       ── Export pruned regulons to GMT format
          │
          ▼
[Step 7]  scRBP mergeRegulons    ── Merge region-specific GMT files
          │                          (3'UTR / 5'UTR / CDS / Introns)
          ▼
[Step 8]  scRBP ras              ── Regulon Activity Score (AUCell) per cell / cell type
          │                          (--mode sc | --mode ct)
          ▼
[Step 9]  scRBP rgs              ── Regulon Gene-Set analysis (MAGMA GWAS enrichment)
          │                          (--mode sc | --mode ct)
          ▼
[Step 10] scRBP trs              ── Trait Relevance Score (RAS × RGS integration)
                                     (--mode sc | --mode ct)

---

Installation

Requirements

• Python 3.9, 3.10, or 3.11 (Python 3.12+ not yet supported by pyscenic/arboreto)
• MAGMA binary (external, required only for Step 9 — scRBP rgs)

Option 1 — Install from PyPI (recommended)

pip install scRBP

This installs scRBP together with all Python dependencies in one step.

Option 2 — Install from source (development)

git clone https://github.com/mayunlong89/scRBP.git
cd scRBP/scRBP_package
pip install -e .

Option 3 — Install via conda (recommended for HPC / cluster)

git clone https://github.com/mayunlong89/scRBP.git
cd scRBP/scRBP_package

conda env create -f environment.yml
conda activate scrbp

pip install -e .

Install MAGMA (for Step 9 only)

MAGMA is a standalone binary not available on PyPI. Download from https://cncr.nl/research/magma and make it executable:

# Linux example
wget https://cncr.nl/research/magma/software/magma_v1.10_static_linux.zip
unzip magma_v1.10_static_linux.zip -d ~/tools/magma/
chmod +x ~/tools/magma/magma

Verify installation

scRBP --help
scRBP getGRN --help
scRBP --version

---

Step 1 — scRBP getSketch (Optional)

Purpose

Performs stratified GeoSketch downsampling on large single-cell datasets. For .h5ad input, cells are sampled per cell-type so that each type contributes at least a user-specified minimum number of cells, while keeping the total close to a global target. For .feather input, a global GeoSketch is applied.

GeoSketch uses a PCA-space geometric sketch to retain transcriptional diversity while making downstream analysis faster and statistically representative.

For datasets exceeding 300,000 cells, direct analysis may incur substantial computational costs. We recommend downsampling to ~50,000 cells.

Usage

scRBP getSketch \
    --input  <input_file>   \
    --output <output_file>  \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--input	str	—	Yes	Input expression file. Accepted formats: .h5ad (AnnData) or .feather (gene × cell matrix). .rds (Seurat) must be converted to .h5ad first using SeuratDisk in R.
--output	str	—	Yes	Output file path. Format auto-detected from extension: .h5ad, .csv, .feather, .npz.
--n_cells	int	50000	No	Target total number of cells to retain after sketching.
--n_pca	int	100	No	Number of PCA (TruncatedSVD) components before running GeoSketch.
--celltype_col	str	"celltype"	No	Column name in adata.obs for cell-type labels. Used for stratified sampling (.h5ad only).
--min_cells_per_type	int	50	No	Minimum number of cells to retain per cell type. If a type has fewer cells, all are kept.
--seed	int	42	No	Random seed for reproducibility.

Output Files

File	Description
<output>	Downsampled expression matrix in specified format.
<output_prefix>_cell_to_celltype.csv	(.h5ad input only) Two-column CSV mapping each retained cell barcode to its cell type.

Example

scRBP getSketch \
    --input  PBMC_healthy.h5ad \
    --output PBMC_sketch_50K.feather \
    --n_cells 50000 \
    --celltype_col annotation_broad3 \
    --min_cells_per_type 500 \
    --n_pca 100 \
    --seed 42

---

Step 2 — scRBP getGRN

Purpose

Infers a gene or isoform regulatory network (GRN) between RBPs and their target genes/isoforms using tree-based machine learning — either GRNBoost2 (gradient boosting, faster) or GENIE3 (random forest, more conservative). Optionally computes Spearman correlation and assigns a regulatory mode (activating / repressing / unknown) for each edge.

Two inference modes:

• gene mode: RBPs in the expression matrix are used directly as regulators; all genes are targets.
• isoform mode: Multiple isoforms of the same RBP gene are first aggregated into a single regulator signal, then individual isoforms serve as targets. Requires --isoform_annotation.

Recommended practice: run getGRN with 30 different --seed values, then merge with getMerge_GRN to obtain a stable consensus network.

Usage

scRBP getGRN \
    --matrix   <expression_matrix> \
    --rbp_list <rbp_list_file>     \
    --output   <output_prefix>     \
    --mode     gene|isoform        \
    [options]

Output filename auto-suffix

Mode	Output filename
gene	<prefix>_scRBP_gene_GRNs.tsv
isoform	<prefix>_scRBP_isoform_GRNs.tsv

Parameters — Core (all modes)

Parameter	Type	Default	Required	Description
--matrix	str	—	Yes	Expression matrix file. Formats: .csv, .csv.gz, .feather, .loom. Rows = features, columns = cells.
--rbp_list	str	—	Yes	Plain-text file of RBP gene symbols (one per line).
--output	str	—	Yes	Output prefix. Mode suffix appended automatically.
--mode	str	gene	No	gene — RBP→Gene network; isoform — RBP→Isoform network.
--method	str	grnboost2	No	grnboost2 (recommended) or genie3.
--n_workers	int	all CPUs	No	Number of parallel worker processes.
--correlation	bool	True	No	Compute Spearman correlation and assign regulatory mode per edge.
--threshold	float	0.03	No	Absolute Spearman correlation threshold; edges with `
--seed	int	1234	No	Random seed for the tree ensemble. Change across runs for consensus merging.

Parameters — Isoform mode only

Parameter	Type	Default	Required	Description
--isoform_annotation	str	None	Yes (isoform)	TSV/CSV mapping isoform/transcript IDs → gene symbols.
--rbp_agg_method	str	sum	No	How to aggregate multiple isoforms of the same RBP: sum, mean, max.
--remove_self_targets	bool	True	No	Remove edges where the target isoform belongs to the same gene as the RBP.
--min_target_cells_expressed	int	10	No	Minimum number of cells with expression > 0 to keep a target isoform.
--min_target_mean_expr	float	0.01	No	Minimum mean expression across all cells to keep a target isoform.

Output Files

File	Description
<prefix>_scRBP_gene_GRNs.tsv	Gene mode GRN. Columns: RBP, Gene, Importance, Correlation, Mode
<prefix>_scRBP_isoform_GRNs.tsv	Isoform mode GRN. Columns: RBP, Isoform, Importance, Correlation, Mode
<prefix>_rbp_aggregated_expr.tsv	Aggregated RBP expression matrix (isoform mode, if --save_rbp_agg_matrix True)
<prefix>_rbp_isoform_map.tsv	RBP → isoform membership table (isoform mode)
<prefix>_target_isoform_stats.tsv	Per-isoform expression statistics used for filtering (isoform mode)

Examples

Gene mode (30 seeds):

for SEED in $(seq 1 30); do
  scRBP getGRN \
      --matrix    PBMC_sketch_15K.feather \
      --rbp_list  human_RBP_list.txt \
      --output    grn_seed${SEED} \
      --mode      gene \
      --method    grnboost2 \
      --n_workers 20 \
      --correlation True \
      --seed      ${SEED}
done
# Output: grn_seed1_scRBP_gene_GRNs.tsv, grn_seed2_scRBP_gene_GRNs.tsv, ...

Isoform mode:

for SEED in 1 2 3; do
  scRBP getGRN \
      --matrix                     PBMC_isoform_sketch.feather \
      --rbp_list                   human_RBP_list.txt \
      --output                     iso_grn_seed${SEED} \
      --mode                       isoform \
      --isoform_annotation         gencode_v44_isoform_gene_map.tsv \
      --rbp_agg_method             sum \
      --remove_self_targets        True \
      --min_target_cells_expressed 10 \
      --min_target_mean_expr       0.01 \
      --method                     grnboost2 \
      --n_workers                  20 \
      --seed                       ${SEED}
done

---

Step 3 — scRBP getMerge_GRN

Purpose

Merges multiple GRN output files (from different random seeds) into a single consensus network. For each RBP–Gene edge: importance and correlation are averaged across all runs, n_present counts how many runs contained the edge, and presence_rate = n_present / total_runs indicates edge stability. Optional filters retain only high-confidence edges.

Usage

scRBP getMerge_GRN \
    --pattern "<glob_pattern>" \
    --output  <merged_output>  \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--pattern	str	—	Yes	Glob pattern matching all GRN seed files, e.g. "grn_seed*.tsv". Must be quoted.
--output	str	—	Yes	Output path for the merged TSV file.
--corr-threshold	float	0.0	No	Remove edges where averaged `
--n_present	int	0	No	Minimum number of seed runs an edge must appear in to be retained.
--present_rate	float	0.0	No	Minimum presence rate (n_present / N_runs). Typical cutoff: 0.5.

Example

scRBP getMerge_GRN \
    --pattern "grn_seed*.tsv" \
    --output  grn_consensus.tsv \
    --n_present 15 \
    --present_rate 0.5

Edges appearing in fewer than 50% of seeds are discarded, yielding a stable consensus network.

---

Step 4 — scRBP getModule

Purpose

Extracts RBP regulon candidate modules from the consensus GRN using multiple complementary strategies:

• TopN-per-gene (topN_per_gene_N): For each gene, keep only its top-N highest-importance RBPs.
• TopN-per-RBP (top_target_N): For each RBP, keep its top-N highest-importance target genes.
• Percentile (pctX_per_rbp): For each RBP, keep targets above the Xth importance percentile.

Usage

scRBP getModule \
    --input         <consensus_grn.tsv> \
    --output_merged <modules_output.tsv> \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--input	str	—	Yes	Consensus GRN TSV file. Required columns: RBP, Gene, Importance.
--output_merged	str	—	Yes	Output TSV file containing all strategies merged.
--importance_threshold	float	0.005	No	Global minimum importance cutoff applied before any strategy.
--top_n_list	str	"5,10,50"	No	Comma-separated N values for TopN-per-gene strategy.
--target_top_n	str	"50"	No	Comma-separated N values for TopN-per-RBP strategy.
--percentile	str	"0.75,0.9"	No	Comma-separated percentile thresholds (0–1) for percentile-per-RBP strategy.
--verbose	flag	off	No	Print per-strategy edge counts to stdout.

Example

scRBP getModule \
    --input              grn_consensus.tsv \
    --output_merged      modules.tsv \
    --importance_threshold 0.005 \
    --top_n_list         "5,10,50" \
    --target_top_n       "50" \
    --percentile         "0.75,0.9" \
    --verbose

---

Step 5 — scRBP getPrune

Purpose

Prunes candidate RBP regulons by testing whether the potential target genes of each RBP are statistically enriched for known RBP-binding motifs using the ctxcore enrichment scoring engine. This step filters out spurious GRN edges that lack motif support and produces pruned regulons with enrichment statistics (AUC, NES, leading-edge genes).

For each RBP × Strategy combination:

1. Maps the RBP to its associated sequence motifs via --motif_rbp_links.
2. Scores the ranked list of target genes against the motif ranking matrix.
3. Retains RBPs passing all threshold filters (rank, AUC, NES).

Usage

scRBP getPrune \
    --rbp_targets        <modules.tsv>         \
    --motif_rbp_links    <motif2rbp.csv>        \
    --motif_target_ranks <rankings.feather>    \
    --save_dir           <output_directory/>    \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--rbp_targets	str	—	Yes	TSV/CSV of RBP regulon candidates from getModule.
--motif_rbp_links	str	—	Yes	CSV/TSV linking motif IDs to RBP names (from pySCENIC / cistarget databases).
--motif_target_ranks	str	—	Yes	Feather file containing the motif × gene ranking matrix (cistarget database).
--save_dir	str	—	Yes	Output directory. Created if it does not exist.
--rank_threshold	int	1500	No	Maximum rank for a gene to be considered in enrichment scoring.
--auc_threshold	float	0.05	No	AUC threshold; RBPs with AUC < this value are discarded.
--nes_threshold	float	3.0	No	NES threshold; RBPs with NES < this value are discarded.
--min_genes	int	20	No	Minimum number of target genes required for an RBP regulon to be scored.
--n_jobs	int	all CPUs	No	Number of parallel worker processes.
--weight_mode	str	importance	No	Gene weighting mode. importance uses GRN importance scores; equal weights all genes equally.

Example

scRBP getPrune \
    --rbp_targets        modules.tsv \
    --motif_rbp_links    motif_to_rbp_hg38_v10.csv \
    --motif_target_ranks hg38_3UTR_rankings.feather \
    --save_dir           pruned_results/ \
    --rank_threshold     1500 \
    --auc_threshold      0.05 \
    --nes_threshold      3.0 \
    --min_genes          20 \
    --n_jobs             16

---

Step 6 — scRBP getRegulon

Purpose

Converts pruned enrichment results (from getPrune) into GMT regulon files in two formats:

• Symbol GMT: Gene names as HGNC symbols (for use with ras).
• Entrez GMT: Gene names as NCBI Entrez IDs (for use with rgs / MAGMA).

Usage

scRBP getRegulon \
    --input      <ctx_scores.csv>        \
    --out-symbol <regulons_symbol.gmt>   \
    --out-entrez <regulons_entrez.gmt>   \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--input	str	—	Yes	Input CSV file with pruned enrichment scores from getPrune.
--out-symbol	str	—	Yes	Output path for the Symbol-format GMT file.
--out-entrez	str	—	Yes	Output path for the Entrez-format GMT file.
--min_genes	int	1	No	Minimum number of target genes required to include an RBP in the output GMT.
--map-custom	str	None	No	Custom gene-to-Entrez mapping file (e.g. NCBI Homo_sapiens.gene_info or a gene.loc file).
--map-hgnc	str	None	No	HGNC complete set file for Symbol → Entrez mapping.
--map-ncbi	str	None	No	NCBI gene2refseq or gene_info file for Symbol → Entrez mapping.
--taxid	int	9606	No	NCBI taxonomy ID for filtering when using --map-ncbi. Default 9606 = human.
--drop-unmapped-genes	flag	off	No	Remove genes that could not be mapped to an Entrez ID.
--drop-empty-sets	flag	off	No	Remove RBP regulons that become empty after gene mapping.

Example

scRBP getRegulon \
    --input       pruned_results/ctx_scores_merged.csv \
    --out-symbol  regulons_symbol.gmt \
    --out-entrez  regulons_entrez.gmt \
    --map-custom  NCBI38.gene.loc \
    --min_genes   5 \
    --drop-unmapped-genes \
    --drop-empty-sets

---

Step 7 — scRBP mergeRegulons

Purpose

Merges region-specific GMT regulon files (e.g., 3′UTR, 5′UTR, CDS, Introns) generated from separate getPrune + getRegulon runs into a single combined GMT file. The function traverses a directory tree, finds GMT files matching a specified name pattern within region subdirectories, and concatenates them in a defined region priority order.

Usage

scRBP mergeRegulons \
    --base_dir <base_directory/>  \
    --input    <gmt_filename>     \
    --output   <merged_output.gmt>\
    [options]

Parameters

Parameter	Type	Default	Required	Description
--base_dir	str	—	Yes	Root directory to search for region subdirectories.
--input	str	—	Yes	GMT filename to look for inside each region subdirectory (e.g. regulons_symbol.gmt).
--output	str	—	Yes	Filename for the merged GMT output.
--recursive	flag	off	No	Process multiple tissue/dataset directories under --base_dir.
--tissue_glob	str	"z_GRNBoost2_*_30times"	No	Glob pattern for tissue/dataset directories when --recursive is used.
--region_glob	str	"Results_final_*_RBP_top1500_*"	No	Glob pattern for region subdirectories.
--append_region_to_setname	flag	off	No	Append region tag (e.g. _3UTR) to each regulon set name.
--dedup_lines	flag	off	No	Remove exact duplicate GMT lines after merging.
--region_order	list	["3UTR","5UTR","CDS","Introns"]	No	Priority order for regions.
--overwrite	flag	off	No	Overwrite the output file if it already exists.
--summary_out	str	None	No	Optional path to write a TSV summary table of per-region regulon counts.

Example

# Simple (single directory)
scRBP mergeRegulons \
    --base_dir ./analysis/ \
    --input    regulons_symbol.gmt \
    --output   regulons_combined.gmt

# Recursive (multiple tissues / datasets)
scRBP mergeRegulons \
    --base_dir /data/scRBP_results/ \
    --input    regulons_symbol.gmt \
    --output   regulons_all_regions.gmt \
    --recursive \
    --tissue_glob "z_GRNBoost2_*_30times" \
    --region_glob "Results_final_*_RBP_top1500_*" \
    --append_region_to_setname \
    --region_order 3UTR 5UTR CDS Introns \
    --summary_out region_summary.tsv

---

Step 8 — scRBP ras

Purpose

Computes Regulon Activity Scores (RAS) using the AUCell algorithm — for each cell or cell type, AUCell scores how active each RBP regulon is based on whether its target genes are at the top of that cell's expression ranking. Optionally computes Regulon Specificity Scores (RSS) based on Jensen-Shannon divergence.

Two modes are supported:

• --mode sc: RAS computed at single-cell resolution.
• --mode ct: RAS aggregated to cell-type level; also computes RSS and requires --celltypes-csv.

Usage

scRBP ras \
    --mode     <sc|ct>             \
    --matrix   <expression_file>   \
    --regulons <regulons.gmt>      \
    --out      <output_prefix/>    \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--mode	str	ct	No	Analysis mode. sc = single-cell AUCell scores; ct = cell-type-aggregated scores + RSS.
--matrix	str	—	Cond.	Expression matrix file. Formats: .csv, .csv.gz, .feather, .loom. Rows = genes, columns = cells. Required unless --aucell-in is provided.
--regulons	str	—	Cond.	Regulon file. Formats: .gmt / .gmt.gz (Symbol) or .pkl (pySCENIC). Required unless --aucell-in is provided.
--aucell-in	str	—	No	Precomputed AUCell CSV (cells × regulons). If provided, skips AUCell computation.
--out	str	—	Yes	Output path prefix or directory.
--out_format	str	csv	No	Output format for AUCell scores. Choices: csv, loom, both.
--n_workers	int	4	No	Number of parallel workers for AUCell computation.
--celltypes-csv	str	—	Cond.	CSV file with cell barcode and cell type columns. Required when --mode ct.
--cell-col	str	auto	No	Column name for cell barcodes in --celltypes-csv. Auto-detected if not specified.
--ctype-col	str	auto	No	Column name for cell-type labels in --celltypes-csv. Auto-detected if not specified.
--min_genes	int	1	No	Drop regulons with fewer than this many genes in the expression matrix.
--emit-expr-stats	flag	on	No	Compute and save per-gene expression statistics (mean_expr, pct_detected). Used as input to rgs.
--expr-stats-out	str	auto	No	Output path for expression statistics TSV. Auto-generated from --out if not specified.

Output Files

File	Description
aucell_sc.csv	AUCell scores — cells × regulons (--mode sc)
aucell_ct.csv	AUCell scores — cell-types × regulons (--mode ct)
rss.tsv	Regulon Specificity Scores per cell type (--mode ct only)
expr_stats.tsv	Per-gene expression statistics: mean_expr, pct_detected

Examples

Single-cell mode (--mode sc):

scRBP ras \
    --mode      sc \
    --matrix    PBMC_sketch_15K.feather \
    --regulons  regulons_symbol.gmt \
    --out       ras_sc_output/ \
    --n_workers 8 \
    --emit-expr-stats

Cell-type mode (--mode ct):

scRBP ras \
    --mode          ct \
    --matrix        PBMC_sketch_15K.feather \
    --regulons      regulons_symbol.gmt \
    --out           ras_ct_output/ \
    --celltypes-csv cell_to_celltype.csv \
    --n_workers     8 \
    --emit-expr-stats

---

Step 9 — scRBP rgs

Purpose

Computes Regulon Gene-Set (RGS) scores by running MAGMA gene-set analysis on each RBP regulon using GWAS summary statistics. This step assesses whether the target genes of each RBP regulon are enriched for GWAS disease-associated genes.

A key innovation is the automatic generation of matched null regulons — for each real regulon, n-null null gene sets are sampled from the genome, matched on four confounding dimensions: number of SNPs, number of parameters, mean gene expression, and percent detected. This controls for size and expression-level biases, enabling fair statistical comparison.

Two modes are supported:

• --mode sc: Per-regulon MAGMA analysis.
• --mode ct: Cell-type-stratified analysis; requires precomputed expression stats from ras --emit-expr-stats.

Usage

scRBP rgs \
    --mode      <sc|ct>           \
    --magma     <magma_binary>    \
    --genes-raw <gwas.genes.raw>  \
    --sets      <regulons.gmt>    \
    --out       <output_prefix>   \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--mode	str	—	Yes	Analysis mode. sc = per-regulon analysis; ct = cell-type-stratified analysis.
--magma	str	—	Yes	Full path to the MAGMA binary executable.
--genes-raw	str	—	Yes	MAGMA .genes.raw file from MAGMA gene analysis step.
--sets	str	—	Yes	GMT regulon file. Use Symbol GMT (--id-type symbol) or Entrez GMT (--id-type entrez, default).
--id-type	str	entrez	No	Gene ID type in GMT file: entrez or symbol.
--out	str	—	Yes	Output file prefix.
--gene-loc	str	—	No	MAGMA NCBI gene location file. Required when --id-type symbol.
--n-null	int	1000	No	Number of matched null regulons per real regulon. Higher = more stable empirical p-values.
--seed	int	2025	No	Random seed for null regulon sampling.
--q-bins	int	10	No	Number of quantile bins for null matching dimensions.
--min_genes	int	0	No	Minimum number of genes a regulon must have (after overlap with MAGMA universe) to be tested.
--threads	int	None	No	Number of threads for MAGMA.
--expr-stats	str	—	No	Precomputed expression statistics TSV from ras --emit-expr-stats. Used for 4D null matching.

Output Files

File	Description
<out>_real.csv	Parsed MAGMA results for real regulons: VARIABLE, NGENES, BETA, BETA_STD, SE, P, z, mlog10p
<out>_null_summary.tsv	Empirical null distribution summary per regulon
<out>.gsa.out	Raw MAGMA gene-set analysis output

Examples

Single-cell mode (--mode sc):

scRBP rgs \
    --mode      sc \
    --magma     /tools/magma \
    --genes-raw scz_gwas.genes.raw \
    --sets      regulons_entrez.gmt \
    --id-type   entrez \
    --out       rgs_output/scz_sc_rgs \
    --n-null    1000

Cell-type mode (--mode ct):

scRBP rgs \
    --mode       ct \
    --magma      /tools/magma \
    --genes-raw  scz_gwas.genes.raw \
    --sets       regulons_entrez.gmt \
    --id-type    entrez \
    --out        rgs_output/scz_ct_rgs \
    --gene-loc   NCBI38.gene.loc \
    --n-null     1000 \
    --expr-stats ras_ct_output/expr_stats.tsv \
    --threads    16

---

Step 10 — scRBP trs

Purpose

Integrates the Regulon Activity Score (RAS) and the Regulon Gene-Set Score (RGS) into a final Trait Relevance Score (TRS) for each RBP regulon across cells or cell types. The TRS formula balances the two signals while penalizing discordance:

TRS = norm(RAS) + norm(RGS) − λ × |norm(RAS) − norm(RGS)|

Where:

• norm() applies robust min-max normalization using upper quantile q_hi to reduce outlier influence.
• λ (lambda_penalty) controls how strongly discordance between RAS and RGS is penalized.

RBPs with high TRS are both highly active in the cell type and genetically linked to the trait.

Empirical p-values and z-scores are computed using matched null distributions from rgs. BH-FDR correction is applied in ct mode.

Two modes are supported:

• --mode sc: TRS computed per cell.
• --mode ct: TRS computed per cell type, with FDR correction.

Usage

scRBP trs \
    --mode       <sc|ct>             \
    --ras        <aucell_scores.csv>  \
    --rgs-csv    <rgs_results.csv>    \
    --out-prefix <output_prefix>      \
    [options]

Parameters

Parameter	Type	Default	Required	Description
--mode	str	—	Yes	Analysis mode. sc = single-cell TRS; ct = cell-type-level TRS with FDR correction.
--ras	str	—	Yes	RAS file from ras. Formats: .csv / .csv.gz (cells × regulons) or .loom.
--rgs-csv	str	—	Yes	RGS results CSV from rgs (<out>_real.csv). Must contain VARIABLE column and a score column (mlog10p or z).
--out-prefix	str	—	Yes	Output file prefix.
--rgs-score	str	mlog10p	No	RGS score column to use. mlog10p = −log10(MAGMA p-value, recommended); z = MAGMA z-score.
--lambda-penalty	float	1.0	No	Penalty coefficient λ for the discordance term. Higher values more strongly penalize RBPs where RAS and RGS disagree.
--q-hi-ras	float	0.99	No	Upper quantile for robust min-max normalization of RAS scores.
--q-hi-rgs	float	0.99	No	Upper quantile for robust min-max normalization of RGS scores.
--celltypes-csv	str	—	Cond.	CSV with columns cell_id, cell_type. Required when --mode ct.
--min_cells_pert_ct	int	25	No	Minimum number of cells a cell type must have to be included in ct mode.
--do-fdr	int	1	No	Apply BH-FDR correction in ct mode. 1 = apply (default); 0 = skip.

Output Files

File	Description
<prefix>.sc.TRS_matrix.csv	TRS scores matrix (--mode sc): rows = cells, columns = regulons
<prefix>.ct.TRS_matrix.csv	TRS scores matrix (--mode ct): rows = cell types, columns = regulons
<prefix>_trs_long.csv	Long-format TRS table: regulon, cell_type / cell, TRS, p_empirical, z_score, FDR

Examples

Single-cell mode (--mode sc):

scRBP trs \
    --mode         sc \
    --ras          ras_sc_output/aucell_sc.csv \
    --rgs-csv      rgs_output/scz_sc_rgs_real.csv \
    --out-prefix   trs_output/scz_sc_trs \
    --rgs-score    mlog10p \
    --lambda-penalty 1.0

Cell-type mode (--mode ct):

scRBP trs \
    --mode             ct \
    --ras              ras_ct_output/aucell_ct.csv \
    --rgs-csv          rgs_output/scz_ct_rgs_real.csv \
    --out-prefix       trs_output/scz_ct_trs \
    --rgs-score        mlog10p \
    --lambda-penalty   1.0 \
    --q-hi-ras         0.99 \
    --q-hi-rgs         0.99 \
    --celltypes-csv    cell_to_celltype.csv \
    --min_cells_pert_ct 25 \
    --do-fdr           1

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Complete Pipeline Example

Below is a full end-to-end example using PBMC data with schizophrenia GWAS:

# ── Inputs ──────────────────────────────────────────────────
MATRIX=PBMC_healthy.h5ad
RBP_LIST=human_RBP_list.txt
MOTIF_LINKS=motif_to_rbp_hg38_v10.csv
RANKINGS_3UTR=hg38_3UTR_v10_rankings.feather
MAGMA_BIN=/tools/magma
GWAS_RAW=scz_gwas.genes.raw
GENE_LOC=NCBI38.gene.loc

# ── Step 1: Downsample (optional) ───────────────────────────────────────
scRBP getSketch \
    --input $MATRIX --output pbmc_sketch.feather \
    --n_cells 15000 --celltype_col celltype --min_cells_per_type 500

# ── Step 2: Infer GRN (30 seeds) ────────────────────────────
for SEED in $(seq 1 30); do
  scRBP getGRN \
      --matrix pbmc_sketch.feather --rbp_list $RBP_LIST \
      --output grn/grn_seed${SEED} --n_workers 20 --seed $SEED
done

# ── Step 3: Merge GRN runs ───────────────────────────────────
scRBP getMerge_GRN \
    --pattern "grn/grn_seed*_scRBP_gene_GRNs.tsv" --output grn_consensus.tsv \
    --n_present 15 --present_rate 0.5

# ── Step 4: Extract modules ──────────────────────────────────
scRBP getModule \
    --input grn_consensus.tsv --output_merged modules.tsv \
    --top_n_list "5,10,50" --target_top_n "50"

# ── Step 5: Prune with motifs ────────────────────────────────
scRBP getPrune \
    --rbp_targets modules.tsv --motif_rbp_links $MOTIF_LINKS \
    --motif_target_ranks $RANKINGS_3UTR --save_dir pruned/ \
    --rank_threshold 1500 --nes_threshold 3.0 --n_jobs 16

# ── Step 6: Build GMT regulons ───────────────────────────────
scRBP getRegulon \
    --input pruned/ctx_scores.csv \
    --out-symbol regulons_symbol.gmt \
    --out-entrez regulons_entrez.gmt \
    --map-custom $GENE_LOC --min_genes 5

# ── Step 7: Merge regions (if run per-region) ────────────────
scRBP mergeRegulons \
    --base_dir results/ --input regulons_symbol.gmt \
    --output regulons_all.gmt --recursive

# ── Step 8: Compute RAS (cell-type mode) ─────────────────────
scRBP ras \
    --mode ct --matrix pbmc_sketch.feather \
    --regulons regulons_symbol.gmt --out ras_out/ \
    --celltypes-csv pbmc_sketch_cell_to_celltype.csv \
    --emit-expr-stats

# ── Step 9: Run MAGMA RGS (cell-type mode) ───────────────────
scRBP rgs \
    --mode ct --magma $MAGMA_BIN --genes-raw $GWAS_RAW \
    --sets regulons_entrez.gmt --id-type entrez \
    --out rgs_out/scz --gene-loc $GENE_LOC \
    --n-null 1000 --expr-stats ras_out/expr_stats.tsv

# ── Step 10: Compute TRS (cell-type mode) ────────────────────
scRBP trs \
    --mode ct --ras ras_out/aucell_ct.csv \
    --rgs-csv rgs_out/scz_real.csv \
    --out-prefix trs_out/scz_trs \
    --celltypes-csv pbmc_sketch_cell_to_celltype.csv

---

Command Reference

Step	Command	Key Inputs	Key Output
1	scRBP getSketch	.h5ad / .feather	Downsampled cells
2	scRBP getGRN	Expression matrix, RBP list	*_scRBP_gene_GRNs.tsv or *_scRBP_isoform_GRNs.tsv
3	scRBP getMerge_GRN	Multiple GRN TSV files (glob)	Consensus GRN TSV
4	scRBP getModule	Consensus GRN TSV	Modules TSV
5	scRBP getPrune	Modules TSV, motif files	Pruned scores (Parquet)
6	scRBP getRegulon	Pruned scores	Regulons GMT (symbol + Entrez)
7	scRBP mergeRegulons	Multiple GMT files	Merged GMT
8	scRBP ras (--mode sc|ct)	Expression matrix, GMT	AUCell scores, RSS matrix
9	scRBP rgs (--mode sc|ct)	MAGMA .genes.raw, GMT	RGS scores CSV
10	scRBP trs (--mode sc|ct)	RAS CSV, RGS CSV	TRS scores CSV

Use scRBP <command> --help to see all parameters for any step.

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Dependencies

Category	Packages
Core numerics	numpy, pandas, scipy, scikit-learn
Single-cell I/O	anndata, scanpy, loompy
Fast I/O	polars, pyarrow
Cell downsampling	geosketch
GRN inference	arboreto (GRNBoost2 / GENIE3)
Motif enrichment	ctxcore, pyscenic
Progress display	tqdm
GWAS enrichment	MAGMA binary (external, user-provided)

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Citation

If you use scRBP in your research, please cite:

Ma Y. et al. Decoding disease-associated RNA-binding protein-mediated regulatory networks through polygenic enrichment across diverse cellular contexts. (2026)

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License

MIT License. See LICENSE for details.

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Links

• GitHub: https://github.com/mayunlong89/scRBP
• Issues: https://github.com/mayunlong89/scRBP/issues