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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
cpa-tools-0.2.10.tar.gz
(35.7 kB
view hashes)
Built Distribution
cpa_tools-0.2.10-py3-none-any.whl
(36.6 kB
view hashes)
Close
Hashes for cpa_tools-0.2.10-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c059af74347e8c12cc64b82ddd4aac93d98d2df0691cbe2f8dd51cee68c0cd66 |
|
MD5 | a62bf6666087eb2fc86d01d07fc2d427 |
|
BLAKE2b-256 | 640b0fcfef0f0d2bc9922108fac5b0e609441f36203ba5467d04a99a06eeb824 |