Add basic_stats and max_pmito_s in Preprocessing #466
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Ukyeon
commented
Mar 22, 2023
Ukyeon
commented
- Add basic_stats into early step in the preprocessing.
- Add new parameter max_pmito_s in the filter_cells_by_outliers function so that it allow user to remove cells that have too many mitochondrial genes expressed.
- Fix minor issues regarding docstring.
dynamo/preprocessing/Preprocessor.py
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| if self.collapse_species_adata: | ||
| main_info("applying collapse species adata...") | ||
| self.collapse_species_adata(adata) | ||
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| if self.convert_gene_name: | ||
| main_info("applying convert_gene_name function...") | ||
| self.convert_gene_name(adata) | ||
| main_info("making adata observation index unique after gene name conversion...") | ||
| self.unique_var_obs_adata(adata) |
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we should actually keep this. because we don't always need to run collapse_species_adata and convert_gene_name. We only need to do this if we have e.g. uu, ul, su, sl 4 species and the gene name is not in official name but emsemble id
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in fact, we need to to have a function to directly detect whether it is necessary to collapse or convert gene names automatically
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I am uncertain if the current 'if statements' are necessary since the functions being called already contain condition checking statements.
For example,
if np.all([name in adata.layers.keys() for name in splicing_and_labeling]): -----> if we have e.g. uu, ul, su, sl 4 species
if np.all(adata.var_names.str.startswith("ENS")) or scopes is not None: ------> the gene name is not in official name but emsemble id
In my opinion, we can remove the if statements and call each function, including the unique_var_obs_adata to clarify and simplify the code. However, if you believe that those statements are necessary or a redundant function to check is needed, I am open to creating them.
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This fix has already been applied to the master branch. |
