Quick Start Guide

Introduction

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

This vignette provides a quick introduction to using SCORPION for gene regulatory network inference.

Installation

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

# From GitHub
remotes::install_github("Zaoqu-Liu/SCORPION")

Loading the Package

library(SCORPION)
library(Matrix)

Example Data

SCORPION comes with example data consisting of:

Gene expression matrix: A sparse matrix with genes as rows and cells as columns
TF-target motif prior: A data frame describing transcription factor binding sites
Protein-protein interactions: A data frame of known TF-TF interactions

# Load example data
data(scorpionTest)

# Examine the structure
cat("Gene expression matrix:", nrow(scorpionTest$gex), "genes x", 
    ncol(scorpionTest$gex), "cells\n")
#> Gene expression matrix: 230 genes x 80 cells
cat("TF-target motif edges:", nrow(scorpionTest$tf), "\n")
#> TF-target motif edges: 4485
cat("Protein-protein interactions:", nrow(scorpionTest$ppi), "\n")
#> Protein-protein interactions: 12754

Running SCORPION

The main function scorpion() takes three inputs and returns three networks:

# Run SCORPION
set.seed(123)
result <- scorpion(
  tfMotifs = scorpionTest$tf,      # TF-target motif prior
  gexMatrix = scorpionTest$gex,    # Gene expression matrix
  ppiNet = scorpionTest$ppi,       # Protein-protein interactions
  gammaValue = 10,                 # Metacell aggregation ratio
  alphaValue = 0.1,                # Learning rate
  hammingValue = 0.001             # Convergence threshold
)

Output Structure

SCORPION returns a list containing:

# Network dimensions
cat("Regulatory network:", dim(result$regNet)[1], "TFs x", 
    dim(result$regNet)[2], "genes\n")
#> Regulatory network: 783 TFs x 214 genes
cat("Co-regulatory network:", dim(result$coregNet)[1], "x", 
    dim(result$coregNet)[2], "genes\n")
#> Co-regulatory network: 214 x 214 genes
cat("Cooperative network:", dim(result$coopNet)[1], "x", 
    dim(result$coopNet)[2], "TFs\n")
#> Cooperative network: 783 x 783 TFs

# Summary statistics
cat("\nNumber of genes:", result$numGenes, "\n")
#> 
#> Number of genes: 214
cat("Number of TFs:", result$numTFs, "\n")
#> Number of TFs: 783
cat("Number of edges:", result$numEdges, "\n")
#> Number of edges: 167562

Extracting Top Regulatory Edges

# Get regulatory network
regNet <- result$regNet

# Find top positive regulators
top_edges <- which(regNet > 2, arr.ind = TRUE)
if(nrow(top_edges) > 0) {
  top_df <- data.frame(
    TF = rownames(regNet)[top_edges[,1]],
    Gene = colnames(regNet)[top_edges[,2]],
    Score = regNet[top_edges]
  )
  top_df <- top_df[order(-top_df$Score), ]
  head(top_df, 10)
}
#>          TF     Gene    Score
#> 289   ESRRB      CA2 22.59849
#> 999    HSF5    FCRLA 17.98239
#> 1320   RFX5 HLA-DQA1 17.29737
#> 3265  ZNF57    TTC38 16.70364
#> 2504  ZNF18    PTCRA 16.49227
#> 3008 DMRTB1    TCL1A 16.48504
#> 1090   FLI1      GP9 16.41962
#> 446  PHOX2B     CD68 16.03947
#> 2344  GATA1      PF4 15.97612
#> 444    ESR2     CD68 15.89983

Visualizing Network Statistics

# Edge weight distribution
hist(as.vector(result$regNet), breaks = 50, 
     main = "Distribution of Regulatory Edge Weights",
     xlab = "Edge Weight (Z-score)", 
     col = "steelblue", border = "white")
abline(v = c(-2, 2), col = "red", lty = 2)

Distribution of regulatory edge weights

Session Information

sessionInfo()
#> R version 4.4.0 (2024-04-24)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS 15.6.1
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
#> 
#> locale:
#> [1] C
#> 
#> time zone: Asia/Shanghai
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] Matrix_1.7-4   SCORPION_1.2.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] cli_3.6.5         knitr_1.51        rlang_1.1.7       xfun_0.56        
#>  [5] otel_0.2.0        textshaping_1.0.4 jsonlite_2.0.0    htmltools_0.5.9  
#>  [9] ragg_1.5.0        sass_0.4.10       rmarkdown_2.30    grid_4.4.0       
#> [13] evaluate_1.0.5    jquerylib_0.1.4   fastmap_1.2.0     yaml_2.3.12      
#> [17] lifecycle_1.0.5   compiler_4.4.0    codetools_0.2-20  igraph_2.2.1     
#> [21] irlba_2.3.5.1     fs_1.6.6          pkgconfig_2.0.3   htmlwidgets_1.6.4
#> [25] pbapply_1.7-4     systemfonts_1.3.1 lattice_0.22-7    digest_0.6.39    
#> [29] R6_2.6.1          RANN_2.6.2        parallel_4.4.0    magrittr_2.0.4   
#> [33] bslib_0.9.0       tools_4.4.0       pkgdown_2.2.0     cachem_1.1.0     
#> [37] desc_1.4.3

Zaoqu Liu

2026-01-29