immunewatch-clustcr 1.0.1

a Python interface for rapid clustering of large sets of CDR3 sequences

ImmuneWatch ClusTCR

A Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity

A two-step clustering approach that combines the speed of the Faiss Clustering Library with the accuracy of Markov Clustering Algorithm

On a standard machine*, clusTCR can cluster 1 million CDR3 sequences in under 5 minutes.
_{*Intel(R) Core(TM) i7-10875H CPU @ 2.30GHz, using 8 CPUs}

Compared to other state-of-the-art clustering algorithms (GLIPH2, iSMART and tcrdist), clusTCR shows comparable clustering quality, but provides a steep increase in speed and scalability.

Documentation & Install

All of our documentation, installation info and examples can be found in the above link! To get you started, here's how to install clusTCR

$ pip install immunewatch-clustcr

Development Guide

Environment

uv venv
source .venv/bin/activate
uv pip install -e ".[dev]"

Testing

# Run all tests
pytest

# Run with coverage report
pytest --cov=clustcr --cov-report=html

Build Distribution

# Install build tool
uv pip install build twine

# Build source and wheel distributions
python -m build

# Check the built distributions
twine check dist/*

# Upload to TestPyPI
twine upload --repository testpypi dist/*

# Upload to PyPI
twine upload dist/*

Cite

Please cite as:

Sebastiaan Valkiers, Max Van Houcke, Kris Laukens, Pieter Meysman, ClusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity, Bioinformatics, 2021;, btab446, https://doi.org/10.1093/bioinformatics/btab446

Bibtex:

@article{valkiers2021clustcr,
    author = {Valkiers, Sebastiaan and Van Houcke, Max and Laukens, Kris and Meysman, Pieter},
    title = "{ClusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences with unknown antigen specificity}",
    journal = {Bioinformatics},
    year = {2021},
    month = {06},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btab446},
    url = {https://doi.org/10.1093/bioinformatics/btab446},
    note = {btab446},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab446/38660282/btab446.pdf},
}