scxpand 0.1.13

Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing

scXpand: Pan-cancer Detection of T-cell Clonal Expansion

Detect T-cell clonal expansion from single-cell RNA sequencing data without paired TCR sequencing

Documentation • Installation • Quick Start • Examples • Citation

A framework for predicting T-cell clonal expansion from single-cell RNA sequencing data.

Manuscript in preparation - detailed methodology and benchmarks coming soon.

View full documentation for comprehensive guides and API reference.

Features

• Multiple Model Architectures:
  • Autoencoder-based: Encoder-decoder with reconstruction and classification heads
  • MLP: Multi-layer perceptron
  • LightGBM: Gradient boosted decision trees
  • Linear Models: Logistic regression and support vector machines
• Scalable Processing: Handles millions of cells with memory-efficient data streaming from disk during training
• Automated Hyperparameter Optimization: Built-in Optuna integration for model tuning

Installation

For CUDA-enabled systems:

pip install scxpand[cuda] --index-url https://download.pytorch.org/whl/cu121

For other systems (CPU or non-CUDA GPU backends):

pip install scxpand

See the full installation guide for detailed setup instructions.

Quick Start

import scxpand
# Make sure that "your_data.h5ad" includes only T cells for the results to be meaningful
# Ensure that "your_data.var_names" are provided as Ensembl IDs (as the pre-trained models were trained using this gene representation)
# Please refer to our documentation for more information

# List available pre-trained models
scxpand.list_pretrained_models()

# Run inference with automatic model download
results = scxpand.run_inference(
    model_name="pan_cancer_autoencoder",  # default model
    data_path="your_data.h5ad"
)

Documentation

See our Tutorial Notebook for a complete example with data preprocessing, T-cell filtering, gene ID conversion, and model application using a real breast cancer dataset.

Getting Started:

• Development Guide - Setup for local development of scXpand
• Usage Examples - Detailed tutorials and CLI examples
• Data Format - Input data requirements and specifications

Model Training & Optimization:

• Model Architectures - Detailed architecture descriptions and configurations
• Hyperparameter Optimization - Automated model tuning with Optuna
• Data Pipeline - Preprocessing and normalization details

Analysis & Evaluation:

• Evaluation Metrics - Performance assessment and stratified analysis
• Output Format - Understanding model outputs and results

📖 Full Documentation - Complete guides, API reference, and interactive tutorials

License

This project is licensed under the MIT License – see the LICENSE file for details.

Citation

If you use scXpand in your research, please cite:

@article{scxpand2025,
  title={scXpand: Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing},
  author={[Your Name]},
  journal={[Journal Name]},
  year={2025},
  doi={[DOI]}
}

This project was created in favor of the scientific community worldwide, with a special dedication to the cancer research community. We hope you’ll find this repository helpful, and we warmly welcome any requests or suggestions - please don’t hesitate to reach out!