Add two distance metrics, three-way comparison and bootstrapping

pertpy/metadata/_drug.py

@@ -45,7 +45,7 @@ def _download_drug_annotation(
                 block_size=4096,
                 is_zip=False,
             )
-        dgidb_df = pd.read_table(dgidb_path)
+        dgidb_df = pd.read_table(dgidb_path, delimiter=",")
         return dgidb_df
 
     else:

pertpy/tools/_distances/_distance_tests.py

@@ -66,11 +66,14 @@ def __init__(
         self.alpha = alpha
         self.correction = correction
         self.cell_wise_metric = (
-            cell_wise_metric if cell_wise_metric else Distance(self.metric, self.obsm_key).cell_wise_metric
+            cell_wise_metric if cell_wise_metric else Distance(self.metric, obsm_key=self.obsm_key).cell_wise_metric
         )
 
         self.distance = Distance(
-            self.metric, layer_key=self.layer_key, obsm_key=self.obsm_key, cell_wise_metric=self.cell_wise_metric
+            self.metric,
+            layer_key=self.layer_key,
+            obsm_key=self.obsm_key,
+            cell_wise_metric=self.cell_wise_metric,
         )
 
     def __call__(
@@ -176,7 +179,8 @@ def test_xy(self, adata: AnnData, groupby: str, contrast: str, show_progressbar:
         # Evaluate the test
         # count times shuffling resulted in larger distance
         comparison_results = np.array(
-            pd.concat([r["distance"] - df["distance"] for r in results], axis=1) > 0, dtype=int
+            pd.concat([r["distance"] - df["distance"] for r in results], axis=1) > 0,
+            dtype=int,
         )
         n_failures = pd.Series(np.clip(np.sum(comparison_results, axis=1), 1, np.inf), index=df.index)
         pvalues = n_failures / self.n_perms
@@ -284,7 +288,8 @@ def test_precomputed(self, adata: AnnData, groupby: str, contrast: str, verbose:
         # Evaluate the test
         # count times shuffling resulted in larger distance
         comparison_results = np.array(
-            pd.concat([r["distance"] - df["distance"] for r in results], axis=1) > 0, dtype=int
+            pd.concat([r["distance"] - df["distance"] for r in results], axis=1) > 0,
+            dtype=int,
         )
         n_failures = pd.Series(np.clip(np.sum(comparison_results, axis=1), 1, np.inf), index=df.index)
         pvalues = n_failures / self.n_perms