tcrfoundation 0.1.2

A multimodal foundation model for T cell receptor and transcriptome analysis

TCRfoundation

A multimodal foundation model for single-cell immune profiling

A multimodal foundation model for single-cell immune profiling

Overview

TCRfoundation integrates gene expression and TCR sequences (α and β chains) from paired single-cell measurements through self-supervised pretraining with masked reconstruction and cross-modal contrastive learning.

Input and Pretraining Architecture

Gene expression profiles are encoded through feed-forward layers with multi-head attention, while TCR sequences are tokenized and processed through transformer blocks. The fused representations are learned via three objectives: masked gene expression reconstruction, masked TCR sequence reconstruction, and cross-modal alignment.

Fine-tuning Tasks

The pretrained model supports three downstream applications:

• T-cell state classification: Predict tissue origin, disease state, and cellular phenotype
• Binding specificity detection: Identify TCR-antigen interactions and quantify binding avidity
• Cross-modal prediction: Infer gene expression from TCR sequences

Installation

git clone https://github.com/Liao-Xu/TCRfoundation.git
cd TCRfoundation
pip install -e .

**Requirements**: Python 3.8+, PyTorch 1.13.1+