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.
See here for documentation and tutorials.
If you have a question or new architecture or a model that could be integrated into our pipeline, you can post an issue
This code is inspired by an early implementatiom by Pierre Boyeau using scvi-tools.
