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This repository was archived by the owner on Nov 1, 2024. It is now read-only.
Thank you very much for sharing this repo. This is a very interesting algorithm and I think the paper is very helpful. Currently I am trying to train the model using your provided datasets, however, when I run the command line "python -m cpa.train --data datasets/GSM_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type sigm", I always get an error "AssertionError: Covariate c is missing in the provided adata". If I follow your codes in the notebook, I will get the error "module 'cpa' has no attribute 'api'". It seems that you may get some updated version of the module "cpa"?
I am currently doing research regarding the out of sample prediction problems for perturbation data. I think your method should be very helpful and I really looking forward to successfully running the algorithm. Hope I can get your response regarding this issue, thank you very much for your time.
Dear authors,
Thank you very much for sharing this repo. This is a very interesting algorithm and I think the paper is very helpful. Currently I am trying to train the model using your provided datasets, however, when I run the command line "python -m cpa.train --data datasets/GSM_new.h5ad --save_dir /tmp --max_epochs 1 --doser_type sigm", I always get an error "AssertionError: Covariate c is missing in the provided adata". If I follow your codes in the notebook, I will get the error "module 'cpa' has no attribute 'api'". It seems that you may get some updated version of the module "cpa"?
I am currently doing research regarding the out of sample prediction problems for perturbation data. I think your method should be very helpful and I really looking forward to successfully running the algorithm. Hope I can get your response regarding this issue, thank you very much for your time.
Best,
Hongxu