Gene importance, Cell Generation, and Single covariate handling #34
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- Implemented the `generate` method in the `CellDISECT` class for conditional cell generation based on categorical covariates. - Added detailed docstring for the new method, including parameters, return values, and usage examples.
…Module. Added logic to return None for attribute-specific decoders when only one covariate is present, ensuring correct behavior by relying on the shared decoder.
- Introduced a `use_bias` parameter in the `CellDISECT` and `CellDISECTModule` classes to control the inclusion of bias terms in neural network layers. (encoders) - Implemented a new method `get_gene_importance` in the `CellDISECT` class to compute gene importance scores based on encoder weights, returning results as a pandas DataFrame. - Updated documentation to reflect the new parameter and method functionality.
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Pull Request Overview
This pull request introduces gene importance calculation, conditional cell generation functionality, and improved single covariate handling in the CellDISECT model.
Key Changes:
- Added optional bias parameter for encoder/decoder layers
- Implemented gene importance scoring based on encoder weights
- Added cell generation method for sampling new cells with specified covariates
- Fixed handling of single covariate scenarios in counterfactual decoding
Reviewed Changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 6 comments.
| File | Description |
|---|---|
| celldisect/_module.py | Added bias parameter to encoder initialization and fixed single covariate handling in counterfactual predictions by returning None when only one covariate exists |
| celldisect/_model.py | Added use_bias parameter, implemented get_gene_importance() for calculating gene importance scores, and added generate() method for conditional cell generation with library size handling |
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…issing library size parameters. Cleaned up code by removing unused import and ensuring consistent formatting in layer handling.
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