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YosefLab · Dec 10, 2023 · 5fb8aa7 · 5fb8aa7
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Showing 13 changed files with 97 additions and 97 deletions.
diff --git a/popv/algorithms/_bbknn.py b/popv/algorithms/_bbknn.py
@@ -9,13 +9,13 @@
 class BBKNN:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        result_key: Optional[str] = "popv_knn_on_bbknn_prediction",
-        embedding_key: Optional[str] = "X_bbknn_umap_popv",
-        method_dict: Optional[dict] = {},
-        classifier_dict: Optional[dict] = {},
-        embedding_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        result_key: str | None = "popv_knn_on_bbknn_prediction",
+        embedding_key: str | None = "X_bbknn_umap_popv",
+        method_dict: dict | None = None,
+        classifier_dict: dict | None = None,
+        embedding_dict: dict | None = None,
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/algorithms/_celltypist.py b/popv/algorithms/_celltypist.py
@@ -7,11 +7,11 @@
 class CELLTYPIST:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        result_key: Optional[str] = "popv_celltypist_prediction",
-        method_dict: Optional[dict] = {},
-        classifier_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None  = "_labels_annotation",
+        result_key: str | None  = "popv_celltypist_prediction",
+        method_dict: dict | None  = None,
+        classifier_dict: dict | None = None,
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/algorithms/_harmony.py b/popv/algorithms/_harmony.py
@@ -12,13 +12,13 @@
 class HARMONY:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        result_key: Optional[str] = "popv_knn_on_harmony_prediction",
-        embedding_key: Optional[str] = "X_umap_harmony_popv",
-        method_dict: Optional[dict] = {},
-        classifier_dict: Optional[dict] = {},
-        embedding_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        result_key: str | None = "popv_knn_on_harmony_prediction",
+        embedding_key: str | None = "X_umap_harmony_popv",
+        method_dict: dict | None = None,
+        classifier_dict: dict | None = None,
+        embedding_dict: dict | None = None,
     ) -> None:
         """
         Class to compute KNN classifier after Harmony integration.

diff --git a/popv/algorithms/_onclass.py b/popv/algorithms/_onclass.py
@@ -10,13 +10,13 @@
 class ONCLASS:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        layers_key: Optional[str] = None,
-        max_iter: Optional[int] = 30,
-        cell_ontology_obs_key: Optional[str] = None,
-        result_key: Optional[str] = "popv_onclass_prediction",
-        seen_result_key: Optional[str] = "popv_onclass_seen",
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        layers_key: str | None = None,
+        max_iter: Optionalint] = 30,
+        cell_ontology_obs_key: str | None = None,
+        result_key: str | None = "popv_onclass_prediction",
+        seen_result_key: str | None = "popv_onclass_seen",
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/algorithms/_rf.py b/popv/algorithms/_rf.py
@@ -10,11 +10,11 @@
 class RF:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        layers_key: Optional[str] = None,
-        result_key: Optional[str] = "popv_rf_prediction",
-        classifier_dict: Optional[str] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        layers_key: str | None = None,
+        result_key: str | None = "popv_rf_prediction",
+        classifier_dict: str | None = {},
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/algorithms/_scaffold_algorithm.py b/popv/algorithms/_scaffold_algorithm.py
@@ -10,14 +10,14 @@ class NEW_ALGORITHM:
     # Remove embedding key if not an integration method.
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        layers_key: Optional[str] = None,
-        result_key: Optional[str] = "popv_knn_on_scanorama_prediction",
-        embedding_key: Optional[str] = "X_umap_scanorma_popv",
-        method_dict: Optional[dict] = {},
-        classifier_dict: Optional[dict] = {},
-        embedding_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        layers_key: str | None = None,
+        result_key: str | None = "popv_knn_on_scanorama_prediction",
+        embedding_key: str | None = "X_umap_scanorma_popv",
+        method_dict: dict | None = None,
+        classifier_dict: dict | None = None,
+        embedding_dict: dict | None = None,
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/algorithms/_scanorama.py b/popv/algorithms/_scanorama.py
@@ -13,13 +13,13 @@
 class SCANORAMA:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        result_key: Optional[str] = "popv_knn_on_scanorama_prediction",
-        embedding_key: Optional[str] = "X_umap_scanorma_popv",
-        method_dict: Optional[dict] = {},
-        classifier_dict: Optional[dict] = {},
-        embedding_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        result_key: str | None = "popv_knn_on_scanorama_prediction",
+        embedding_key: str | None = "X_umap_scanorma_popv",
+        method_dict: dict | None = None,
+        classifier_dict: dict | None = None,
+        embedding_dict: dict | None = None,
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/algorithms/_scanvi.py b/popv/algorithms/_scanvi.py
@@ -10,16 +10,16 @@
 class SCANVI_POPV:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        n_epochs_unsupervised: Optional[int] = None,
-        n_epochs_semisupervised: Optional[int] = None,
-        save_folder: Optional[str] = None,
-        result_key: Optional[str] = "popv_scanvi_prediction",
-        embedding_key: Optional[str] = "X_scanvi_umap_popv",
-        model_kwargs: Optional[dict] = {},
-        classifier_kwargs: Optional[dict] = {},
-        embedding_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        n_epochs_unsupervised: int | None = None,
+        n_epochs_semisupervised: int | None = None,
+        save_folder: str | None = None,
+        result_key: str | None = "popv_scanvi_prediction",
+        embedding_key: str | None = "X_scanvi_umap_popv",
+        model_kwargs: dict | None = None,
+        classifier_kwargs: dict | None = None,
+        embedding_dict: dict | None = None,
     ) -> None:
         """
         Class to compute classifier in scANVI model and predict labels.

diff --git a/popv/algorithms/_scvi.py b/popv/algorithms/_scvi.py
@@ -13,15 +13,15 @@
 class SCVI_POPV:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        max_epochs: Optional[int] = None,
-        save_folder: Optional[str] = None,
-        result_key: Optional[str] = "popv_knn_on_scvi_prediction",
-        embedding_key: Optional[str] = "X_scvi_umap_popv",
-        model_kwargs: Optional[dict] = {},
-        classifier_dict: Optional[dict] = {},
-        embedding_dict: Optional[dict] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        max_epochs: int | None = None,
+        save_folder: str | None = None,
+        result_key: str | None = "popv_knn_on_scvi_prediction",
+        embedding_key: str | None = "X_scvi_umap_popv",
+        model_kwargs: dict | None = None,
+        classifier_dict: dict | None = None,
+        embedding_dict: dict | None = None,
     ) -> None:
         """
         Class to compute KNN classifier after scVI integration.

diff --git a/popv/algorithms/_svm.py b/popv/algorithms/_svm.py
@@ -11,11 +11,11 @@
 class SVM:
     def __init__(
         self,
-        batch_key: Optional[str] = "_batch_annotation",
-        labels_key: Optional[str] = "_labels_annotation",
-        layers_key: Optional[str] = None,
-        result_key: Optional[str] = "popv_svm_prediction",
-        classifier_dict: Optional[str] = {},
+        batch_key: str | None = "_batch_annotation",
+        labels_key: str | None = "_labels_annotation",
+        layers_key: str | None = None,
+        result_key: str | None = "popv_svm_prediction",
+        classifier_dict: str | None = {},
     ) -> None:
         """
         Class to compute KNN classifier after BBKNN integration.

diff --git a/popv/annotation.py b/popv/annotation.py
@@ -18,9 +18,9 @@
 
 def annotate_data(
     adata: anndata.AnnData,
-    methods: Optional[list] = None,
-    save_path: Optional[str] = None,
-    methods_kwargs: Optional[dict] = None,
+    methods: list | None = None,
+    save_path: str | None = None,
+    methods_kwargs: dict | None = None,
 ) -> None:
     """
     Annotate an AnnData dataset preprocessed by preprocessing.Process_Query by using the annotation pipeline.
@@ -127,7 +127,7 @@ def compute_consensus(adata: anndata.AnnData, prediction_keys: list) -> None:
 def ontology_vote_onclass(
     adata: anndata.AnnData,
     prediction_keys: list,
-    save_key: Optional[str] = "popv_prediction",
+    save_key: str | None = "popv_prediction",
 ):
     """
     Compute prediction using ontology aggregation method.

diff --git a/popv/preprocessing.py b/popv/preprocessing.py
@@ -19,20 +19,20 @@ def __init__(
         ref_adata: anndata.AnnData,
         ref_labels_key: str = "cell_ontology_class",
         ref_batch_key: str = "donor_method",
-        query_labels_key: Optional[str] = None,
-        query_batch_key: Optional[str] = None,
-        query_layers_key: Optional[str] = None,
-        prediction_mode: Optional[str] = "retrain",
-        cl_obo_folder: Optional[Union[List, str, bool]] = None,
-        unknown_celltype_label: Optional[str] = "unknown",
-        n_samples_per_label: Optional[int] = 300,
-        pretrained_scvi_path: Optional[str] = None,
-        save_path_trained_models: Optional[str] = "tmp/",
-        hvg: Optional[int] = 4000,
-        accelerator: Optional[str] = "cuda",
-        devices: Optional[Union[int, str]] = "auto",
-        compute_embedding: Optional[bool] = True,
-        return_probabilities: Optional[bool] = True,
+        query_labels_key: str | None = None,
+        query_batch_key: str | None = None,
+        query_layers_key: str | None = None,
+        prediction_mode: str | None = "retrain",
+        cl_obo_folder: Union[List, str, bool] | None = None,
+        unknown_celltype_label: str | None = "unknown",
+        n_samples_per_label: int | None = 300,
+        pretrained_scvi_path: str | None = None,
+        save_path_trained_models: str | None = "tmp/",
+        hvg: int | None = 4000,
+        accelerator: str | None = "cuda",
+        devices: Union[int, str] | None = "auto",
+        compute_embedding: bool = True,
+        return_probabilities: bool = True,
     ) -> None:
         """
         Processes the query and reference dataset in preperation for the annotation pipeline.

diff --git a/popv/visualization.py b/popv/visualization.py
@@ -89,9 +89,9 @@ def _sample_report(adata, cell_type_key, score_key, pred_keys):
 
 def agreement_score_bar_plot(
     adata,
-    popv_prediction_key: Optional[str] = "popv_prediction",
-    consensus_score_key: Optional[str] = "popv_prediction_score",
-    save_folder: Optional[str] = None,
+    popv_prediction_key: str | None = "popv_prediction",
+    consensus_score_key: str | None = "popv_prediction_score",
+    save_folder: str | None = None,
 ):
     """
     Create bar-plot of prediction scores in query cells after running popv.
@@ -142,8 +142,8 @@ def agreement_score_bar_plot(
 
 def prediction_score_bar_plot(
     adata,
-    popv_prediction_score: Optional[str] = "popv_prediction_score",
-    save_folder: Optional[str] = None,
+    popv_prediction_score: str | None = "popv_prediction_score",
+    save_folder: str | None = None,
 ):
     """
     Create bar-plot of prediction scores in query cells after running popv.
@@ -181,8 +181,8 @@ def prediction_score_bar_plot(
 
 def celltype_ratio_bar_plot(
     adata,
-    popv_prediction: Optional[str] = "popv_prediction",
-    save_folder: Optional[str] = None,
+    popv_prediction: str | None = "popv_prediction",
+    save_folder: str | None = None,
 ):
     """
     Create bar-plot of celltype rations in query as well as reference cells after running popv.
@@ -223,8 +223,8 @@ def celltype_ratio_bar_plot(
 def make_agreement_plots(
     adata,
     prediction_keys: list,
-    popv_prediction_key: Optional[str] = "popv_prediction",
-    save_folder: Optional[str] = None,
+    popv_prediction_key: str | None = "popv_prediction",
+    save_folder: str | None = None,
 ):
     """
     Create plot of confusion matrix for different popv methods and consensus prediction.