diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e15ffad9..b49bc12c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -25,7 +25,7 @@ repos:
             # https://github.com/jupyterlab/jupyterlab/issues/12675
             language_version: "17.9.1"
     - repo: https://github.com/astral-sh/ruff-pre-commit
-      rev: v0.0.290
+      rev: v0.0.291
       hooks:
           - id: ruff
             args: [--fix, --exit-non-zero-on-fix]
diff --git a/pertpy/tools/_distances/_distances.py b/pertpy/tools/_distances/_distances.py
index 6733de86..74b30cd6 100644
--- a/pertpy/tools/_distances/_distances.py
+++ b/pertpy/tools/_distances/_distances.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from collections.abc import Iterable
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -14,6 +13,8 @@
 from sklearn.metrics import pairwise_distances
 
 if TYPE_CHECKING:
+    from collections.abc import Iterable
+
     from anndata import AnnData
 
 
@@ -144,7 +145,7 @@ def pairwise(
         # Some metrics are able to handle precomputed distances. This means that
         # the pairwise distances between all cells are computed once and then
         # passed to the metric function. This is much faster than computing the
-        # pairwise distances for each group separately. Other metrics are not 
+        # pairwise distances for each group separately. Other metrics are not
         # able to handle precomputed distances such as the PsuedobulkDistance.
         if self.metric_fct.accepts_precomputed:
             # Precompute the pairwise distances if needed
@@ -180,7 +181,7 @@ def pairwise(
         df.columns.name = groupby
         df.name = f"pairwise {self.metric}"
         return df
-    
+
     def onesided_distances(
         self,
         adata: AnnData,
@@ -218,7 +219,7 @@ def onesided_distances(
         # Some metrics are able to handle precomputed distances. This means that
         # the pairwise distances between all cells are computed once and then
         # passed to the metric function. This is much faster than computing the
-        # pairwise distances for each group separately. Other metrics are not 
+        # pairwise distances for each group separately. Other metrics are not
         # able to handle precomputed distances such as the PsuedobulkDistance.
         if self.metric_fct.accepts_precomputed:
             # Precompute the pairwise distances if needed
diff --git a/tests/tools/_distances/test_distances.py b/tests/tools/_distances/test_distances.py
index 4d927f51..bd71b671 100644
--- a/tests/tools/_distances/test_distances.py
+++ b/tests/tools/_distances/test_distances.py
@@ -36,11 +36,13 @@ def test_distance_pairwise(self, adata, distance):
         assert isinstance(df, DataFrame)
         assert df.columns.equals(df.index)
         assert np.sum(df.values - df.values.T) == 0  # symmetry
-    
+
     @mark.parametrize("distance", actual_distances + pseudo_distances)
     def test_distance_onesided(self, adata, distance):
         Distance = pt.tl.Distance(distance, "X_pca")
         selected_group = adata.obs.perturbation.unique()[0]
-        df = Distance.onesided_distances(adata, groupby="perturbation", selected_group=selected_group, show_progressbar=True)
+        df = Distance.onesided_distances(
+            adata, groupby="perturbation", selected_group=selected_group, show_progressbar=True
+        )
         assert isinstance(df, Series)
         assert df.loc[selected_group] == 0  # distance to self is 0