yutanagano/sceptr

Simple Contrastive Embedding of the Primary sequence of T cell Receptors

SCEPTR

Check out the documentation page.

SCEPTR (Simple Contrastive Embedding of the Primary sequence of T cell Receptors) 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 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. 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, 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's tcr_metric API, so sceptr will fit snugly into the rest of your repertoire analysis workflow.

Installation

pip install sceptr

sceptr's Issues

PyPI does not allow direct dependency

And since sceptr depends on libtcrlm, libtcrlm needs to also be on PyPI.