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.
I obtained consistent embedding space visualizations but pretty different numbers on the evaluation metrics. Could you please help advise if I missed anything? Thanks!
Hello,
Thank you for sharing the tutorial notebook! I was trying to replicate the results in the Norman notebook: https://github.com/theislab/cpa/blob/main/docs/tutorials/Norman.ipynb.
I obtained consistent embedding space visualizations but pretty different numbers on the evaluation metrics. Could you please help advise if I missed anything? Thanks!
Results in the tutorial:
My results:
