Add two distance metrics, three-way comparison and bootstrapping

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