CPA - Compositional Perturbation Autoencoder

CPA is a collaborative research project from Facebook AI Research (FAIR) and a computatiobal biology group of Prof. Fabian Theis (https://github.com/theislab) from Helmholtz Zentrum München.

What is CPA?

CPA is a framework to learn effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug response across different cell types, doses and drug combinations. CPA allows:

• Out-of-distribution predicitons of unseen drug combinations at various doses and among different cell types.
• Learn interpretable drug and cell type latent spaces.
• Estimate dose response curve for each perturbation and their combinations.
• Access the uncertainty of the estimations of the model.

Package Structure

The repository is centered around the compert module:

• compert.train contains scripts to train the model.
• compert.api contains user friendly scripts to interact with the model via scanpy.
• compert.plotting contains scripts to plotting functions.
• compert.model contains modules of compert model.
• compert.data contains data loader, which transforms anndata structure to a class compatible with compert model.
• compert.collect_results contains script for automatic model selection from sweeps.

Additional files and folders:

• datasets contains both versions of the data: raw and pre-processed.
• preprocessing contains notebooks to reproduce the datasets pre-processing from raw data.
• notebooks contains notebooks to reproduce plots from the paper and detailed analysis of each of the datasets.
• pretrained_models contains best models selected after the sweeps. These models were used for the analysis and figures in the paper.
• scripts contains bash files for automatic running of the model.

Usage

• As a first step, download the contents of datasets/ and pretrained_models/ from this tarball.

To learn how to use this repository, check ./notebooks/demo.ipynb, and the following scripts:

• ./scripts/run_one_epoch.sh runs one epoch for all datasets.

• ./scripts/run_sweeps.sh runs all sweeps.

• ./scripts/run_collect_results.sh, given a sweep, runs model-selection and prints results.

• Note that hyperparameters in the demo.ipynb are not default and will not work work for new datasets. Please make sure to run run_sweeps.sh for your new dataset to find best hyperparameters.

Examples and Reproducibility

All the examples and the reproducbility notebooks for the plots in the paper could be found in the notebooks/ folder.

Curation of your own data to train CPA

• To prepare your data to train CPA, you need to add specific fields to adata object and perfrom data split. Examples on how to add necessary fields for multiple datasets used in the paper can be found in preprocessing/ folder.

Training a model

There are two ways to train a compert model:

• Using the command line, e.g.: python -m compert.train --dataset_path datasets/GSM_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type sigm
• From jupyter notebook: example in ./notebooks/demo.ipynb

Testing

Run python ./scripts/run_one_epoch.sh to perfrom automatic testing for one epoch of all the datasets used in the study.

Documentation

Currently you can access the documentation via help function in IPython. For example:

from compert.api import ComPertAPI

help(ComPertAPI)

from compert.plotting import CompertVisuals

help(CompertVisuals)

A separate page with the documentation is coming soon.

Support and contribute

If you have a question or noticed a problem, you can post an issue.

Reference

Please cite the following preprint if you find CPA useful in your research.

@article {Lotfollahi2021.04.14.439903,
	author = {Lotfollahi, Mohammad and Susmelj, Anna Klimovskaia and De Donno, Carlo and Ji, Yuge and Ibarra, Ignacio L. and Wolf, F. Alexander and Yakubova, Nafissa and Theis, Fabian J. and Lopez-Paz, David},
	title = {Learning interpretable cellular responses to complex perturbations in high-throughput screens},
	elocation-id = {2021.04.14.439903},
	year = {2021},
	doi = {10.1101/2021.04.14.439903},
	publisher = {Cold Spring Harbor Laboratory},

	URL = {https://www.biorxiv.org/content/early/2021/05/18/2021.04.14.439903},
	eprint = {https://www.biorxiv.org/content/early/2021/05/18/2021.04.14.439903.full.pdf},
	journal = {bioRxiv}
}

The preprint titled Learning interpretable cellular responses to complex perturbations in high-throughput screens can be found here.

License

This source code is released under the MIT license, included here.