Comments (1)
ruochiz
commented on August 16, 2024
Removing batch effect with our model is still an on-going effort. The uploaded version only uses the batch id in the visualization step to inspect if batch effect exists in the data. We are currently working on something to use batch id as input to regress out the batch effect during the training process. I will update the repo after systematic evaluation to see if such model design can effectively remove batch effect.
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Related Issues (20)
- I cannot install Higashi through conda. My computer doesn't have gpu. HOT 3
- Higahi stop at train_for_imputation_nbr_0 on both API and CLI. HOT 21
- Higahi stop at train_for_imputation_nbr_0 on both API and CLI.
- Conda install doesn't work as expected HOT 1
- Moudle import error HOT 2
- Problem on running FastHigashi on Ramani et al. dataset HOT 1
- Higashi_vis set up HOT 2
- Higashi gets error during higashi_model.process_data() HOT 2
- Generating a hdf5 file for storing the coassayed signals HOT 2
- ValueError: Found array with 1 feature(s) (shape=(250, 1)) while a minimum of 2 is required by TruncatedSVD HOT 16
- Some question about json file
- some question about json file HOT 1
- NameError: name 'neg_num' is not defined HOT 10
- FastHigashi wrapper.prep_dataset: 'int' object has no attribute 'shape' HOT 9
- Some problem about color HOT 5
- question about cell order HOT 5
- Problem solved
- Error running Ramani data HOT 2
- higashi.process_data() won't finish HOT 20
- higashi.Higashi_backend.Modules import error HOT 5
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