# scXpand
<div align="center">
<br/>
<img src="docs/_static/images/scXpand_symbol.jpeg" alt="scXpand Logo" width="300"/>
<br/>
<br/>
<h3>Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing</h3>
<br/>
<p>
<a href="https://scxpand.readthedocs.io">Documentation</a> •
<a href="#installation">Installation</a> •
<a href="#quick-start">Quick Start</a> •
<a href="docs/usage_examples.rst">Usage Examples</a> •
<a href="docs/data_format.rst">Data Format</a> •
<a href="docs/output_format.rst">Output Format</a> •
<a href="#model-architectures">Model Architectures</a> •
<a href="#citation">Citation</a>
</p>
</div>
<div style="width: 100vw; margin-left: calc(-50vw + 50%); margin-right: calc(-50vw + 50%); margin-top: 20px; margin-bottom: 40px; padding: 0 40px;">
<img src="docs/_static/images/scXpand_datasets.jpeg" alt="scXpand Datasets Overview" style="width: 100%; height: auto; display: block; margin: 0; padding: 0;"/>
</div>
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](https://scxpand.readthedocs.io)** for comprehensive guides and API reference.
## Features
- **Multiple Model Architectures**: Autoencoder, MLP, LightGBM, Logistic Regression, and SVM for comprehensive analysis
- **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
```bash
pip install scxpand
```
## Quick Start
```python
import scxpand
# List available pre-trained models
scxpand.list_pretrained_models()
# Run inference with automatic model download
results = scxpand.run_inference_with_pretrained(
model_name="pan_cancer_autoencoder",
data_path="your_data.h5ad"
)
```
Or via command line:
```bash
# Pre-trained model inference (curated models)
scxpand predict --data_path your_data.h5ad --model_name pan_cancer_autoencoder
# Direct URL inference (any external model - seamless sharing!)
scxpand predict --data_path your_data.h5ad --model_url "https://your-platform.com/model.zip"
# Local model inference
scxpand predict --data_path your_data.h5ad --model_path results/my_model
```
## Development
For development installation and model training, see the [documentation](https://scxpand.readthedocs.io/en/latest/installation.html).
## Model Architectures
scXpand provides multiple model architectures to suit different use cases and data characteristics:
#### Autoencoder-based Classifiers
Architecture featuring an encoder with auxiliary decoder for reconstruction and classifier head for expansion prediction. This approach leverages representation learning to capture complex patterns in single-cell data.
#### Multi-Layer Perceptron (MLP)
Standard feed-forward neural networks for direct expansion prediction.
#### LightGBM
Gradient boosting for classification tasks with excellent performance on tabular data.
#### Linear Models
Classical machine learning approaches including logistic regression and support vector machines.
## License
This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.
## Citation
If you use scXpand in your research, please cite:
```bibtex
@article{scxpand2024,
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={2024},
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!
<p align="center">
<a href="https://mapmyvisitors.com/web/1byyd">
<img src="https://mapmyvisitors.com/map.png?d=yRhTNMKyBcxvPwQsz-rFDDwHhMjSeVYRSYtxm4oUNdY&cl=ffffff">
</a>
</p>
<p align="center">
<a href="#">
<img src="https://visitor-badge.laobi.icu/badge?page_id=ronamit.scxpand&left_text=scXpand%20Visitors" alt="Visitors" />
</a>
</p>
Raw data
{
"_id": null,
"home_page": null,
"name": "scxpand",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.11",
"maintainer_email": "Ofir Shorer <ofirshorer@campus.technion.ac.il>",
"keywords": "single-cell, RNA-seq, T-cell, clonal-expansion, machine-learning, bioinformatics",
"author": null,
"author_email": "Ron Amit <ron2amit@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/59/b5/5099f57460a6118f4f72ba32ca7323ab5453f04894df518508374d1ae47b/scxpand-0.1.0.tar.gz",
"platform": null,
"description": "# scXpand\n\n\n\n<div align=\"center\">\n <br/>\n <img src=\"docs/_static/images/scXpand_symbol.jpeg\" alt=\"scXpand Logo\" width=\"300\"/>\n <br/>\n <br/>\n <h3>Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing</h3>\n <br/>\n <p>\n <a href=\"https://scxpand.readthedocs.io\">Documentation</a> \u2022\n <a href=\"#installation\">Installation</a> \u2022\n <a href=\"#quick-start\">Quick Start</a> \u2022\n <a href=\"docs/usage_examples.rst\">Usage Examples</a> \u2022\n <a href=\"docs/data_format.rst\">Data Format</a> \u2022\n <a href=\"docs/output_format.rst\">Output Format</a> \u2022\n <a href=\"#model-architectures\">Model Architectures</a> \u2022\n <a href=\"#citation\">Citation</a>\n </p>\n</div>\n\n<div style=\"width: 100vw; margin-left: calc(-50vw + 50%); margin-right: calc(-50vw + 50%); margin-top: 20px; margin-bottom: 40px; padding: 0 40px;\">\n <img src=\"docs/_static/images/scXpand_datasets.jpeg\" alt=\"scXpand Datasets Overview\" style=\"width: 100%; height: auto; display: block; margin: 0; padding: 0;\"/>\n</div>\n\nA framework for predicting T-cell clonal expansion from single-cell RNA sequencing data.\n\n**Manuscript in preparation** - detailed methodology and benchmarks coming soon.\n\n**[View full documentation](https://scxpand.readthedocs.io)** for comprehensive guides and API reference.\n\n\n## Features\n\n- **Multiple Model Architectures**: Autoencoder, MLP, LightGBM, Logistic Regression, and SVM for comprehensive analysis\n- **Scalable Processing**: Handles millions of cells with memory-efficient data streaming from disk during training\n- **Automated Hyperparameter Optimization**: Built-in Optuna integration for model tuning\n\n## Installation\n\n```bash\npip install scxpand\n```\n\n## Quick Start\n\n```python\nimport scxpand\n\n# List available pre-trained models\nscxpand.list_pretrained_models()\n\n# Run inference with automatic model download\nresults = scxpand.run_inference_with_pretrained(\n model_name=\"pan_cancer_autoencoder\",\n data_path=\"your_data.h5ad\"\n)\n```\n\nOr via command line:\n\n```bash\n# Pre-trained model inference (curated models)\nscxpand predict --data_path your_data.h5ad --model_name pan_cancer_autoencoder\n\n# Direct URL inference (any external model - seamless sharing!)\nscxpand predict --data_path your_data.h5ad --model_url \"https://your-platform.com/model.zip\"\n\n# Local model inference\nscxpand predict --data_path your_data.h5ad --model_path results/my_model\n```\n\n## Development\n\nFor development installation and model training, see the [documentation](https://scxpand.readthedocs.io/en/latest/installation.html).\n\n## Model Architectures\n\nscXpand provides multiple model architectures to suit different use cases and data characteristics:\n\n#### Autoencoder-based Classifiers\n\nArchitecture featuring an encoder with auxiliary decoder for reconstruction and classifier head for expansion prediction. This approach leverages representation learning to capture complex patterns in single-cell data.\n\n#### Multi-Layer Perceptron (MLP)\n\nStandard feed-forward neural networks for direct expansion prediction.\n\n#### LightGBM\n\nGradient boosting for classification tasks with excellent performance on tabular data.\n\n#### Linear Models\n\nClassical machine learning approaches including logistic regression and support vector machines.\n\n## License\n\nThis project is licensed under the MIT License \u2013 see the [LICENSE](LICENSE) file for details.\n\n## Citation\n\nIf you use scXpand in your research, please cite:\n\n```bibtex\n@article{scxpand2024,\n title={scXpand: Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing without paired single-cell TCR sequencing},\n author={[Your Name]},\n journal={[Journal Name]},\n year={2024},\n doi={[DOI]}\n}\n```\n\nThis project was created in favor of the scientific community worldwide, with a special dedication to the cancer research community.\nWe hope you\u2019ll find this repository helpful, and we warmly welcome any requests or suggestions - please don\u2019t hesitate to reach out!\n\n<p align=\"center\">\n <a href=\"https://mapmyvisitors.com/web/1byyd\">\n <img src=\"https://mapmyvisitors.com/map.png?d=yRhTNMKyBcxvPwQsz-rFDDwHhMjSeVYRSYtxm4oUNdY&cl=ffffff\">\n </a>\n</p>\n<p align=\"center\">\n <a href=\"#\">\n <img src=\"https://visitor-badge.laobi.icu/badge?page_id=ronamit.scxpand&left_text=scXpand%20Visitors\" alt=\"Visitors\" />\n </a>\n</p>\n",
"bugtrack_url": null,
"license": null,
"summary": "Pan-cancer detection of T-cell clonal expansion from single-cell RNA sequencing",
"version": "0.1.0",
"project_urls": {
"Documentation": "https://scxpand.readthedocs.io",
"Homepage": "https://github.com/ronamit/scxpand",
"Issues": "https://github.com/ronamit/scxpand/issues",
"Repository": "https://github.com/ronamit/scxpand"
},
"split_keywords": [
"single-cell",
" rna-seq",
" t-cell",
" clonal-expansion",
" machine-learning",
" bioinformatics"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "6a7484d52701baa58f1f5b6c45c27f441baf9560a8a432e0e1d87995b7c107c6",
"md5": "f784c6ce9b37a2d020deb00808c91c84",
"sha256": "05c4507717349d3ac65299f86fe1fb1f47869505ee5806439a18a259ea4f6976"
},
"downloads": -1,
"filename": "scxpand-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "f784c6ce9b37a2d020deb00808c91c84",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.11",
"size": 132677,
"upload_time": "2025-09-06T10:42:09",
"upload_time_iso_8601": "2025-09-06T10:42:09.650501Z",
"url": "https://files.pythonhosted.org/packages/6a/74/84d52701baa58f1f5b6c45c27f441baf9560a8a432e0e1d87995b7c107c6/scxpand-0.1.0-py3-none-any.whl",
"yanked": true,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "59b55099f57460a6118f4f72ba32ca7323ab5453f04894df518508374d1ae47b",
"md5": "0519490afddf396a2bb92a7f1c4d2ba2",
"sha256": "7997d9fa70ff96a4f9937206505d23ed895021205893106bea2d79a70a9caa89"
},
"downloads": -1,
"filename": "scxpand-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "0519490afddf396a2bb92a7f1c4d2ba2",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.11",
"size": 115831,
"upload_time": "2025-09-06T10:42:14",
"upload_time_iso_8601": "2025-09-06T10:42:14.645791Z",
"url": "https://files.pythonhosted.org/packages/59/b5/5099f57460a6118f4f72ba32ca7323ab5453f04894df518508374d1ae47b/scxpand-0.1.0.tar.gz",
"yanked": true,
"yanked_reason": null
}
],
"upload_time": "2025-09-06 10:42:14",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "ronamit",
"github_project": "scxpand",
"github_not_found": true,
"lcname": "scxpand"
}
JSON
