Small fixes #25
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Small fixes #25
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If global set_output = pandas we are mixing numpy arrays with pandas and get errors.
mstajdohar
reviewed
Aug 9, 2023
| @@ -72,7 +73,9 @@ def _get_norm_factors(self, X: Numeric2D) -> Numeric1D: | |||
| :param X: Expression raw count matrix (n_samples, n_features) | |||
| """ | |||
| X = remove_zero_genes(X) | |||
| lib_size = LibrarySize().fit_transform(X) | |||
| # Make sure that global set_config(transform_output="pandas") | |||
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Is this really necessary? It's kind off ugly to break the intended sklearn logic.
mstajdohar
reviewed
Aug 9, 2023
| @@ -72,7 +73,9 @@ def _get_norm_factors(self, X: Numeric2D) -> Numeric1D: | |||
| :param X: Expression raw count matrix (n_samples, n_features) | |||
| """ | |||
| X = remove_zero_genes(X) | |||
| lib_size = LibrarySize().fit_transform(X) | |||
| # Make sure that global set_config(transform_output="pandas") | |||
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Is this really necessary? It's kind off ugly to break the intended sklearn logic. @mzganec, have a look.
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Do you find this approach better?
lib_size = LibrarySize().fit_transform(X)
...
return upper_quartiles / np.array(lib_size).flatten()
@JureZmrzlikar If we go with this solution, we need to fix effective_lib_size in transform as well.
lib_size = LibrarySize().fit_transform(X)
effective_lib_size = np.array(lib_size).flatten() * factors
mstajdohar
approved these changes
Aug 10, 2023
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