Authors: Charlotte Bunne*, Stefan G. Stark*, Gabriele Gut*, Jacobo Sarabia Del Castillo, Mitch Levesque, Kjong-Van Lehmann, Lucas Pelkmans, Andreas Krause, Gunnar Rätsch
The preprint is available here.
Understanding and predicting molecular responses in single cells upon chemical, genetic, or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by coupling these unpaired distributions. We achieve this alignment with a learned transport map that allows us to infer the treatment responses of unseen untreated cells. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein imaging technology.
This repository contains the CellOT method and evaluation scripts to reproduce the results of experiments on predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein imaging technology. Further, we provide experiments on CellOT's generalization performance to unseen settings by (a) predicting the scRNA-seq responses of holdout lupus patients exposed to IFN-beta, and (b) modeling the hematopoietic developmental trajectories of different subpopulations.
To setup the corresponding conda environment run:
conda create --name cellot python=3.9.5
conda activate cellot
conda update -n base -c defaults conda
pip install --upgrade pip
Install requirements and dependencies via:
pip install -r requirements.txt
To install CellOT run:
python setup.py develop
Package requirements and dependencies are listed in requirements.txt. Installation takes < 5 minutes and has been tested on Linux (CentOS Linux release 7.9.2009), macOS (Version 12.4, with Apple M1 Pro and Version 11.3, with 2.6 GHz Intel Core i7).
You can download the preprocessed data here.
After downloading the dataset, the CellOT model can be trained via the scripts/train.py script. For example, we can train CellOT on 4i data to predict perturbation effects of Cisplatin:
python ./scripts/train.py --outdir ./results/4i/drug-cisplatin/model-cellot --config ./configs/tasks/4i.yaml --config ./configs/models/cellot.yaml --config.data.target cisplatin
All scripts to reproduce the experiments in the i.i.d. (independent-and-identically-distributed), o.o.s. (out-of-sample), and o.o.d. (out-of-distribution) setting can be found in scripts/submit. More details on the method and experiments can be found in the preprint.
The training of CellOT on 4i data takes around 3 hours on CPU. Once trained, the model can be evaluated via:
python ./scripts/evaluate.py --outdir ./results/4i/drug-cisplatin/model-cellot --setting iid --where data_space
The user can hereby choose if the model is evaluated in the iid or ood setting, and if the metrics are considered in the data or latent space (via the flag where). Please note that for 4i data, no o.o.s. or o.o.d. task exists and no embedding is necessary (i.e., evaluation in data_space).
In case you found our work useful, please consider citing us:
@article{bunne2021learning,
title={{Learning Single-Cell Perturbation Responses using Neural Optimal Transport}},
author={Bunne, Charlotte and Stark, Stefan G and Gut, Gabriele and del Castillo, Jacobo Sarabia, and Levesque, Mitch and Lehmann, Kjong-Van and Pelkmans, Lucas and Krause, Andreas and Ratsch, Gunnar},
journal={bioRxiv},
year={2021},
publisher={Cold Spring Harbor Laboratory}
}
In case you have questions, please contact Stefan G. Stark and Charlotte Bunne.
