mw201608 / cpa

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

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