Package: SCORPION 1.2.2

SCORPION: Single Cell Oriented Reconstruction of PANDA Individual Optimized Networks

Constructs gene regulatory networks from single-cell gene expression data using the PANDA (Passing Attributes between Networks for Data Assimilation) algorithm.

Authors:Zaoqu Liu [aut, cre], Daniel Osorio [aut], Marieke L. Kuijjer [aut]

SCORPION_1.2.2.tar.gz
SCORPION_1.2.2.zip(r-4.7)SCORPION_1.2.2.zip(r-4.6)SCORPION_1.2.2.zip(r-4.5)
SCORPION_1.2.2.tgz(r-4.6-x86_64)SCORPION_1.2.2.tgz(r-4.6-arm64)SCORPION_1.2.2.tgz(r-4.5-x86_64)SCORPION_1.2.2.tgz(r-4.5-arm64)
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SCORPION_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SCORPION/json (API)

# Install 'SCORPION' in R:
install.packages('SCORPION', repos = c('https://zaoqu-liu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zaoqu-liu/scorpion/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • scorpionTest - Example single-cell gene expression, motif, and ppi data

On CRAN:

Conda:

openblascppopenmp

3.62 score 21 scripts 533 downloads 1 exports 16 dependencies

Last updated from:c01e59eb2e (on main). Checks:13 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK216
linux-devel-x86_64OK213
source / vignettesOK351
linux-release-arm64OK199
linux-release-x86_64OK211
macos-release-arm64OK138
macos-release-x86_64OK302
macos-oldrel-arm64OK94
macos-oldrel-x86_64OK248
windows-develOK204
windows-releaseOK159
windows-oldrelOK170
wasm-releaseOK180

Exports:scorpion

Dependencies:clicpp11glueigraphirlbalatticelifecyclemagrittrMatrixpbapplypkgconfigRANNRcppRcppArmadillorlangvctrs

Advanced Usage and Best Practices
Introduction | Parameter Tuning | Gamma Value (Metacell Aggregation) | Alpha Value (Learning Rate) | Hamming Threshold | Association Methods | Pearson Correlation (Default) | Spearman Correlation | Principal Component Regression (pcNet) | Comparison | Working with Seurat Objects | Filtering and Preprocessing | Filter Low-Expression Genes | Custom Gene Filtering | Parallel Processing | Output Analysis | Network Comparison | Differential Network Analysis | Best Practices | 1. Data Quality | 2. Prior Network Selection | 3. Parameter Selection | 4. Reproducibility | Session Information
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Last update: 2026-01-29
Started: 2026-01-29

Algorithm and Mathematical Framework
Overview | The PANDA Algorithm | Core Concept | Mathematical Formulation | 1. Tanimoto Similarity | 2. Network Update Rules | Convergence Criterion | Metacell Aggregation | The Sparsity Problem | Solution: Metacells | Gamma Parameter | Network Normalization | Double Z-score Normalization | Computational Complexity | References | Session Information
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Last update: 2026-01-29
Started: 2026-01-29

Network Visualization
Introduction | Running SCORPION | Network Statistics Visualization | Edge Weight Distribution | TF Targeting Statistics | Heatmap Visualization | Regulatory Network Heatmap | TF Cooperation Heatmap | Network Graph Visualization | Building igraph Network | Network Layout and Plotting | Subnetwork for Specific TF | Circular Network Plot | Network Centrality Analysis | Exporting Networks | Export to Cytoscape | Export to GraphML | Summary | Session Information
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Last update: 2026-01-29
Started: 2026-01-29

Quick Start Guide
Introduction | Installation | Loading the Package | Example Data | Running SCORPION | Output Structure | Extracting Top Regulatory Edges | Visualizing Network Statistics | Session Information
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Last update: 2026-01-29
Started: 2026-01-29