scppin-py

Pure Python implementation of scPPIN for single-cell protein-protein interaction network analysis

scppin-py detects functional modules in protein-protein interaction networks by integrating single-cell RNA sequencing data. This is a reimplementation of the original R package with an object-oriented Python API.

Original method: Klimm et al. (2020), BMC Genomics

Key Features

• Fast — Vectorized NumPy operations and igraph backend for speed and efficiency
• Class-Based API — Object-oriented design with method chaining
• Edge Weights — Supports pre-computed edge weights from dictionaries
• Scanpy Integration — Works with AnnData objects for p-value extraction
• Easy Installation — Single pip install command
• Standalone PCST — Direct PCST implementation without dependencies on expression data

Quick Start

Installation

pip install scppin

Or install directly from GitHub:

pip install git+https://github.com/shahrozeabbas/scppin-py.git

Basic Usage

from scppin import scPPIN

# Create model and load network
model = scPPIN()
model.load_network('edges.csv')

# Set node weights (p-values from differential expression)
pvalues = {'TP53': 0.0001, 'MDM2': 0.001, 'CDKN1A': 0.005}
model.set_node_weights(pvalues)

# Optionally set edge weights (from pre-computed dictionary)
edge_weights = {('TP53', 'MDM2'): 0.9, ('TP53', 'CDKN1A'): 0.8}
model.set_edge_weights(weights=edge_weights)

# Detect functional module using PCST
model.detect_module(fdr=0.01, edge_weight_attr='weight')

# Visualize
model.plot_module()

Documentation

Full documentation: https://scppin-py.readthedocs.io

• Installation Guide
• Quick Start Tutorial
• API Reference
• Examples
• Algorithm Overview

Citation

If you use scppin-py in your research, please cite the original paper:

@article{klimm2020functional,
  title={Functional module detection through integration of single-cell RNA sequencing data with protein--protein interaction networks},
  author={Klimm, Florian and Toledo, Enrique M and Monfeuga, Thomas and Zhang, Fang and Deane, Charlotte M and Reinert, Gesine},
  journal={BMC Genomics},
  volume={21},
  number={1},
  pages={756},
  year={2020},
  publisher={BioMed Central},
  doi={10.1186/s12864-020-07144-2}
}

If you wish to additionally cite this Python implementation:

@software{abbas2024scppin,
  author={Abbas, Shahroze},
  title={scppin-py: Pure Python implementation of scPPIN},
  year={2024},
  url={https://github.com/shahrozeabbas/scppin-py},
  note={Python reimplementation of the scPPIN R package}
}

License

GPL-3.0 (same as original R package)

Links

• Documentation
• Original R Package
• Original Paper