The Compositional Perturbation Autoencoder (CPA) is a deep generative framework to learn effects of perturbations at the single-cell level. CPA performs OOD predictions of unseen combinations of drugs, learns interpretable embeddings, estimates dose-response curves, and provides uncertainty estimates.

CPA - Compositional Perturbation Autoencoder

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

What is CPA?

CPA is a deep generative 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.

Examples and Reproducibility

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

Installation

TBD

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.

Link to the paper

TBD

Cite

TBD

License

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