Compositional Perturbation Autoencoder (CPA)

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  • License: BSD License (BSD-3-Clause)
  • Author: Mohsen Naghipourfar
  • Requires: Python >=3.7.2, <3.11
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Project description

CPA - Compositional Perturbation Autoencoder

What is CPA?

Alt text

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.

Project details

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Avatar for ArianAmani from gravatar.com ArianAmani Avatar for naghipourfar from gravatar.com naghipourfar

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Project links
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  • License: BSD License (BSD-3-Clause)
  • Author: Mohsen Naghipourfar
  • Requires: Python >=3.7.2, <3.11
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