Merge pull request #513 from Ukyeon/fano

Add calc_ntr and make an enabler for cell_cycle_score

dynamo/plot/cell_cycle.py

@@ -94,5 +94,3 @@ def cell_cycle_scores(
         plt.show()
     if save_show_or_return in ["return", "all"]:
         return ax
-    else:
-        raise NotImplementedError("Unavailable save_show_or_return flag: %s" % save_show_or_return)

dynamo/preprocessing/Preprocessor.py

@@ -34,6 +34,7 @@
 from .transform import Freeman_Tukey, log, log1p, log2
 from .utils import (
     _infer_labeling_experiment_type,
+    calc_new_to_total_ratio,
     collapse_species_adata,
     convert2symbol,
     convert_layers2csr,
@@ -68,6 +69,7 @@ def __init__(
         force_gene_list: Optional[List[str]] = None,
         sctransform_kwargs: Dict[str, Any] = {},
         regress_out_kwargs: Dict[List[str], Any] = {},
+        cell_cycle_score_enable: bool = False,
         cell_cycle_score_kwargs: Dict[str, Any] = {},
     ) -> None:
         """Preprocessor constructor.
@@ -109,14 +111,15 @@ def __init__(
         self.basic_stats = basic_stats
         self.convert_layers2csr = convert_layers2csr
         self.unique_var_obs_adata = unique_var_obs_adata
-        self.norm_method = log1p
+        self.norm_method = norm_method
         self.norm_method_kwargs = norm_method_kwargs
         self.sctransform = sctransform
 
         self.filter_cells_by_outliers = filter_cells_by_outliers_function
         self.filter_genes_by_outliers = filter_genes_by_outliers_function
         self.normalize_by_cells = normalize_by_cells_function
         self.calc_size_factor = size_factor_function
+        self.calc_new_to_total_ratio = calc_new_to_total_ratio
         self.select_genes = select_genes_function
         self.normalize_selected_genes = normalize_selected_genes_function
         self.regress_out = regress_out_parallel
@@ -138,6 +141,8 @@ def __init__(
         self.select_genes_kwargs = select_genes_kwargs
         self.sctransform_kwargs = sctransform_kwargs
         self.normalize_selected_genes_kwargs = normalize_selected_genes_kwargs
+        self.cell_cycle_score_enable = cell_cycle_score_enable
+        self.cell_cycle_score = cell_cycle_scores
         self.cell_cycle_score_kwargs = cell_cycle_score_kwargs
         self.regress_out_kwargs = regress_out_kwargs
 
@@ -399,14 +404,28 @@ def _pca(self, adata: AnnData) -> None:
             main_info("PCA dimension reduction")
             self.pca(adata, **self.pca_kwargs)
 
+    def _calc_ntr(self, adata: AnnData) -> None:
+        """Calculate the size factor of each cell based on method specified as the preprocessor's `calc_size_factor`.
+
+        Args:
+            adata: an AnnData object.
+        """
+
+        if self.calc_new_to_total_ratio:
+            main_debug("ntr calculation...")
+            # calculate NTR for every cell:
+            ntr, var_ntr = self.calc_new_to_total_ratio(adata)
+            adata.obs["ntr"] = ntr
+            adata.var["ntr"] = var_ntr
+
     def _cell_cycle_score(self, adata: AnnData) -> None:
         """Estimate cell cycle stage of each cell based on its gene expression pattern.
 
         Args:
             adata: an AnnData object.
         """
 
-        if self.cell_cycle_score:
+        if self.cell_cycle_score_enable:
             main_debug("cell cycle scoring...")
             try:
                 self.cell_cycle_score(adata, **self.cell_cycle_score_kwargs)
@@ -486,7 +505,6 @@ def config_monocle_recipe(self, adata: AnnData, n_top_genes: int = 2000) -> None
         self.pca = pca
         self.pca_kwargs = {"pca_key": "X_pca"}
 
-        self.cell_cycle_score = None  # optional: cell_cycle_scores
         self.cell_cycle_score_kwargs = {
             "layer": None,
             "gene_list": None,
@@ -502,10 +520,8 @@ def preprocess_adata_monocle(
 
         Args:
             adata: an AnnData object.
-            tkey: the key for time information (labeling time period for the
-                cells) in .obs. Defaults to None.
-            experiment_type: the experiment type of the data. If not provided,
-                would be inferred from the data. Defaults to None.
+            tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None.
+            experiment_type: the experiment type of the data. If not provided, would be inferred from the data.
         """
 
         main_info("Running monocle preprocessing pipeline...")
@@ -532,6 +548,7 @@ def preprocess_adata_monocle(
             self._regress_out(adata)
 
         self._pca(adata)
+        self._calc_ntr(adata)
         self._cell_cycle_score(adata)
 
         temp_logger.finish_progress(progress_name="Preprocessor-monocle")
@@ -778,32 +795,34 @@ def preprocess_adata(
             "monocle", "seurat", "sctransform", "pearson_residuals", "monocle_pearson_residuals"
         ] = "monocle",
         tkey: Optional[str] = None,
+        experiment_type: Optional[str] = None,
     ) -> None:
         """Preprocess the AnnData object with the recipe specified.
 
         Args:
             adata: An AnnData object.
             recipe: The recipe used to preprocess the data. Defaults to "monocle".
             tkey: the key for time information (labeling time period for the cells) in .obs. Defaults to None.
+            experiment_type: the experiment type of the data. If not provided, would be inferred from the data.
 
         Raises:
             NotImplementedError: the recipe is invalid.
         """
 
         if recipe == "monocle":
             self.config_monocle_recipe(adata)
-            self.preprocess_adata_monocle(adata, tkey=tkey)
+            self.preprocess_adata_monocle(adata, tkey=tkey, experiment_type=experiment_type)
         elif recipe == "seurat":
             self.config_seurat_recipe(adata)
-            self.preprocess_adata_seurat(adata, tkey=tkey)
+            self.preprocess_adata_seurat(adata, tkey=tkey, experiment_type=experiment_type)
         elif recipe == "sctransform":
             self.config_sctransform_recipe(adata)
-            self.preprocess_adata_sctransform(adata, tkey=tkey)
+            self.preprocess_adata_sctransform(adata, tkey=tkey, experiment_type=experiment_type)
         elif recipe == "pearson_residuals":
             self.config_pearson_residuals_recipe(adata)
-            self.preprocess_adata_pearson_residuals(adata, tkey=tkey)
+            self.preprocess_adata_pearson_residuals(adata, tkey=tkey, experiment_type=experiment_type)
         elif recipe == "monocle_pearson_residuals":
             self.config_monocle_pearson_residuals_recipe(adata)
-            self.preprocess_adata_monocle_pearson_residuals(adata, tkey=tkey)
+            self.preprocess_adata_monocle_pearson_residuals(adata, tkey=tkey, experiment_type=experiment_type)
         else:
-            raise NotImplementedError("preprocess recipe chosen not implemented: %s" % (recipe))
+            raise NotImplementedError("preprocess recipe chosen not implemented: %s" % recipe)