Compositional Perturbation Autoencoder (CPA)
Project description
CPA - Compositional Perturbation Autoencoder 
What is CPA?
CPA is a framework to learn the effects of perturbations at the single-cell level. CPA encodes and learns phenotypic drug responses across different cell types, doses, and combinations. CPA allows:
- Out-of-distribution predictions of unseen drug and gene combinations at various doses and among different cell types.
- Learn interpretable drug and cell-type latent spaces.
- Estimate the dose-response curve for each perturbation and their combinations.
- Transfer pertubration effects from on cell-type to an unseen cell-type.
Usage and installation
See here for documentation and tutorials.
How to optmize CPA hyperparamters for your data
Datasets and Pre-trained models
Datasets and pre-trained models are available here.
Support and contribute
If you have a question or new architecture or a model that could be integrated into our pipeline, you can post an issue
Reference
If CPA is helpful in your research, please consider citing the Lotfollahi et al. 2023
@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}
}
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