Chimeric Antigen Receptor Optimal Transport (CAROT)

CAROT applies the conditional Monge Gap to single-cell RNA sequencing data of Chimeric Antigen Receptor T cells. It extends the Conditional Monge Gap with CAR-specific dataloaders, embeddings, and trainers. The notebooks directory contains notebooks for generating the paper figures and additional analyses. The configs and scripts directories contain the configuration files and scripts needed to replicate the experiments from the paper.

Development setup & installation

We use poetry as package manager and tested the code in Python 3.10.

pip install poetry # into your base env
git clone git@github.com:AI4SCR/carot.git
cd carot
poetry install -v

If the installation was successful, activate the environment interactively via poetry shell.

Example usage

You can find example configs in tests/configs/ for the unconditional and conditional settings. To train a conditional Monge model:

from pathlib import Path

from carot.datasets.conditional_loader import ConditionalDataModule
from carot.trainers.conditional_monge_trainer import ConditionalMongeTrainer
from cmonge.utils import load_config


config_path = Path("tests/configs/conditional_synthetic.yml")
config = load_config(config_path)

logger_path = Path(config.logger_path)

datamodule = ConditionalDataModule(config.data, config.condition)
trainer = ConditionalMongeTrainer(jobid=1, logger_path=logger_path, config=config.model, datamodule=datamodule)

trainer.train(datamodule)
trainer.evaluate(datamodule)

Citation

If you find this work useful, please cite:

@article{driessen2026modeling,
  title={Modeling chimeric antigen receptor response at the single-cell level with conditional optimal transport},
  author={Driessen, Alice and Born, Jannis and Rueda, Roc{\'\i}o Castellanos and Reddy, Sai T and Rapsomaniki, Marianna},
  journal={Cell Systems},
  year={2026},
  pages={101591},
  doi={10.1016/j.cels.2026.101591},
  url={https://www.cell.com/cell-systems/fulltext/S2405-4712(26)00073-6}
}