SCORPION

Documentation: https://zaoqu-liu.github.io/SCORPION/

What’s New 🎉

v1.2.2 (January 2026)

• 🚀 RcppArmadillo acceleration: C++ implementation of core algorithms with BLAS optimization (~2.5x faster)
• 📚 Documentation website: Full pkgdown site with tutorials at https://zaoqu-liu.github.io/SCORPION/
• 📖 Vignettes: 4 comprehensive guides (Quick Start, Algorithm, Advanced Usage, Visualization)
• ⚡ Performance: Vectorized operations + optimized BLAS for matrix computation
• 🛡️ Robustness: Enhanced numerical stability in Tanimoto similarity
• 🌐 R-universe: Easy installation via install.packages("SCORPION", repos = "https://zaoqu-liu.r-universe.dev")

Overview

SCORPION (Single-Cell Oriented Reconstruction of PANDA Individually Optimized Gene Regulatory Networks) is a computational framework for inferring cell-type-specific gene regulatory networks (GRNs) from single-cell RNA sequencing (scRNA-seq) data.

Motivation

Single-cell transcriptomics has revolutionized our understanding of cellular heterogeneity, yet the inherent sparsity and technical noise in scRNA-seq data pose significant challenges for accurate gene regulatory network inference. SCORPION addresses these limitations through a metacell-based coarse-graining strategy that preserves biological signal while reducing technical artifacts.

Method

SCORPION integrates three complementary data modalities to reconstruct transcriptome-wide GRNs:

1. Transcription factor (TF) binding motifs - sequence-based prior knowledge of potential regulatory relationships
2. Protein-protein interactions (PPI) - physical interactions between transcription factors
3. Gene expression profiles - cell-type-specific transcriptional states

The algorithm employs the PANDA (Passing Attributes between Networks for Data Assimilation) message-passing framework, which iteratively refines network edge weights through Tanimoto similarity-based updates until convergence.

Key Features

• Metacell aggregation: Reduces data sparsity by grouping transcriptionally similar cells using graph-based clustering in PCA space
• Multi-modal integration: Combines transcriptomic data with prior biological knowledge (TF motifs, PPI networks)
• Comparable networks: Generates fully-connected, weighted, and directed GRNs suitable for population-level comparative analyses
• Scalable: Efficient implementation supporting both CPU and GPU computation

Installation

From R-universe (Recommended)

install.packages("SCORPION", repos = "https://zaoqu-liu.r-universe.dev")

From CRAN

install.packages("SCORPION")

From GitHub (Development Version)

if (!requireNamespace("remotes", quietly = TRUE))
    install.packages("remotes")
remotes::install_github("Zaoqu-Liu/SCORPION")

Quick Start

library(SCORPION)

# Load example data
data(scorpionTest)

# Infer gene regulatory network
grn <- scorpion(
    tfMotifs = scorpionTest$tf,      # TF-target motif prior
    gexMatrix = scorpionTest$gex,    # Gene expression matrix (genes × cells)
    ppiNet = scorpionTest$ppi,       # Protein-protein interactions
    alphaValue = 0.1                 # Learning rate (default)
)

Input Data Format

Parameter	Description	Format
tfMotifs	TF-target regulatory prior	data.frame with columns: TF, Target, Score
gexMatrix	Gene expression matrix	matrix/dgCMatrix (genes × cells)
ppiNet	Protein-protein interactions	data.frame with columns: TF1, TF2, Score

Output

SCORPION returns a list containing:

Component	Description	Dimensions
regNet	TF-gene regulatory network	TFs × Genes
coregNet	Gene co-regulatory network	Genes × Genes
coopNet	TF cooperative network	TFs × TFs

Edge weights represent Z-scores indicating the strength of regulatory relationships.

Example Workflow

# Load package and data
library(SCORPION)
data(scorpionTest)

# Run SCORPION
result <- scorpion(
    tfMotifs = scorpionTest$tf,
    gexMatrix = scorpionTest$gex,
    ppiNet = scorpionTest$ppi,
    gammaValue = 10,        # Metacell graining level
    nPC = 25,               # Principal components for clustering
    alphaValue = 0.1,       # Learning rate
    hammingValue = 0.001    # Convergence threshold
)

# Examine output
dim(result$regNet)    # Regulatory network dimensions
# [1] 783 214

# Extract top regulatory edges
regNet <- result$regNet
top_edges <- which(abs(regNet) > 2, arr.ind = TRUE)

Parameters

Parameter	Default	Description
gammaValue	10	Ratio of cells to metacells
nPC	25	Number of principal components for kNN graph
alphaValue	0.1	Learning rate for network updates
hammingValue	0.001	Convergence threshold
assocMethod	“pearson”	Association method (“pearson”, “spearman”, “pcNet”)

Performance

SCORPION v1.2.2 includes significant performance optimizations through RcppArmadillo and BLAS acceleration.

Benchmark Results

Metric	Before Optimization	After Optimization	Speedup
Test data (230 genes × 80 cells)	~10 sec	~4 sec	2.5x
1000×1000 matrix multiplication	~230 ms	11 ms	21x
Tanimoto similarity computation	~230 ms	19 ms	12x

Optimization Features

Feature	Description
RcppArmadillo	Core Tanimoto algorithm implemented in C++
BLAS Acceleration	Leverages system-optimized BLAS (OpenBLAS, Apple Accelerate, MKL)
Vectorization	All R operations use vectorized implementations
Sparse Matrix	Native support for sparse matrices via Matrix package

Enable Apple Accelerate (macOS)

For optimal performance on macOS, enable Apple’s vecLib BLAS:

cd /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib
sudo ln -sf libRblas.vecLib.dylib libRblas.dylib

Enable OpenBLAS (Linux)

# Ubuntu/Debian
sudo apt-get install libopenblas-dev
sudo update-alternatives --config libblas.so.3

Estimated Runtime

Data Size	Cells	Genes	Approximate Time
Small	1,000	2,000	~5 sec
Medium	10,000	5,000	~1-2 min
Large	50,000	10,000	~5-10 min

Times measured with optimized BLAS on Apple M2.

Citation

If you use SCORPION in your research, please cite:

Osorio, D., Capasso, A., Eckhardt, S. G., Giri, U., Somma, A., Pitts, T. M., … & Kuijjer, M. L. (2023). Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data. Nature Computational Science. DOI: 10.1038/s43588-023-00558-w

Related Resources

• netZooR - Network Zoo package collection
• PANDA - Original PANDA algorithm

Contributing

We welcome contributions! Please submit issues and pull requests on GitHub.

License

GPL-3 © Zaoqu Liu