<div align="center">
<img src="https://raw.githubusercontent.com/yutanagano/sceptr/main/sceptr.svg" width=700>
[](https://pypi.org/p/sceptr)
[](https://sceptr.readthedocs.io)
[](https://github.com/yutanagano/sceptr?tab=MIT-1-ov-file#readme)
[](https://arxiv.org/abs/2406.06397v1)
### Check out the [documentation page](https://sceptr.readthedocs.io).
</div>
---
**SCEPTR** (**S**imple **C**ontrastive **E**mbedding of the **P**rimary sequence of **T** cell **R**eceptors) is a small, fast, and accurate TCR representation model that can be used for alignment-free TCR analysis, including for TCR-pMHC interaction prediction and TCR clustering (metaclonotype discovery).
Our [preprint](https://arxiv.org/abs/2406.06397) demonstrates that SCEPTR can be used for few-shot TCR specificity prediction with improved accuracy over previous methods.
SCEPTR is a BERT-like transformer-based neural network implemented in [Pytorch](https://pytorch.org).
With the default model providing best-in-class performance with only 153,108 parameters (typical protein language models have tens or hundreds of millions), SCEPTR runs fast- even on a CPU!
And if your computer does have a [CUDA-enabled GPU](https://en.wikipedia.org/wiki/CUDA), the sceptr package will automatically detect and use it, giving you blazingly fast performance without the hassle.
sceptr's API exposes three intuitive functions: `calc_vector_representations`, `calc_cdist_matrix`, and `calc_pdist_vector`- and it's all you need to make full use of the SCEPTR models.
What's even better is that they are fully compliant with [pyrepseq](https://pyrepseq.readthedocs.io)'s [tcr_metric](https://pyrepseq.readthedocs.io/en/latest/api.html#pyrepseq.metric.tcr_metric.TcrMetric) API, so sceptr will fit snugly into the rest of your repertoire analysis workflow.
## Installation
```bash
pip install sceptr
```
## Citing SCEPTR
Please cite our [preprint](https://arxiv.org/abs/2406.06397).
### BibTex
```bibtex
@misc{nagano2024contrastive,
title={Contrastive learning of T cell receptor representations},
author={Yuta Nagano and Andrew Pyo and Martina Milighetti and James Henderson and John Shawe-Taylor and Benny Chain and Andreas Tiffeau-Mayer},
year={2024},
eprint={2406.06397},
archivePrefix={arXiv},
primaryClass={q-bio.BM}
}
```
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