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In mode 'full', the original data is overwritten with adata.X = self.model.predict({'count': adata.X, 'size_factors': adata.obs.size_factors})
before the latent representation is created using: adata.obsm['X_dca'] = self.encoder.predict({'count': adata.X, 'size_factors': adata.obs.size_factors})
"X_dca" therefore does not contain the accurate latent representation if this mode is used.
The text was updated successfully, but these errors were encountered:
In mode 'full', the original data is overwritten with
adata.X = self.model.predict({'count': adata.X, 'size_factors': adata.obs.size_factors})
before the latent representation is created using:
adata.obsm['X_dca'] = self.encoder.predict({'count': adata.X, 'size_factors': adata.obs.size_factors})
"X_dca" therefore does not contain the accurate latent representation if this mode is used.
The text was updated successfully, but these errors were encountered: