CPA - Compositional Perturbation Autoencoder

This code in not being maintained anymore, please use the new implementation here.

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 cpa module:

• cpa.train contains scripts to train the model.
• cpa.api contains user friendly scripts to interact with the model via scanpy.
• cpa.plotting contains scripts to plotting functions.
• cpa.model contains modules of cpa model.
• cpa.data contains data loader, which transforms anndata structure to a class compatible with cpa model.

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.

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:

• Note that hyperparameters in the demo.ipynb are set as default but might not work work for new datasets.

Examples and Reproducibility

you can find more example and hyperparamters tuning scripts and also reproducbility notebooks for the plots in the paper in the reproducibility repo.

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 cpa model:

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

Documentation

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

from cpa.api import API

help(API)

from cpa.plotting import CPAVisuals

help(CPAVisuals)

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 publication if you find CPA useful in your research.

@article{lotfollahi2023predicting,
  title={Predicting cellular responses to complex perturbations in high-throughput screens},
  author={Lotfollahi, Mohammad and Klimovskaia Susmelj, Anna and De Donno, Carlo and Hetzel, Leon and Ji, Yuge and Ibarra, Ignacio L and Srivatsan, Sanjay R and Naghipourfar, Mohsen and Daza, Riza M and Martin, Beth and others},
  journal={Molecular Systems Biology},
  pages={e11517},
  year={2023}
}

The paper titled Predicting cellular responses to complex perturbations in high-throughput screens can be found [here](https://www.biorxiv.org/content/10.1101/2021.04.14.439903v2](https://www.embopress.org/doi/full/10.15252/msb.202211517).

License

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