Compositional Perturbation Autoencoder (CPA)
Project description
CPA - Compositional Perturbation Autoencoder
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 predictions 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.
Usage and installation
See here for documentation and tutorials.
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
Acknowledgment
This code is inspired by an early implementatiom by Pierre Boyeau using scvi-tools.
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