diff --git a/pertpy/preprocessing/_guide_rna.py b/pertpy/preprocessing/_guide_rna.py
index ada15573..9ebded21 100644
--- a/pertpy/preprocessing/_guide_rna.py
+++ b/pertpy/preprocessing/_guide_rna.py
@@ -48,7 +48,7 @@ def assign_by_threshold(
         """
         counts = adata.X if layer is None else adata.layers[layer]
         if scipy.sparse.issparse(counts):
-            counts = counts.A
+            counts = counts.toarray()
 
         assigned_grnas = np.where(counts >= assignment_threshold, 1, 0)
         assigned_grnas = scipy.sparse.csr_matrix(assigned_grnas)
@@ -92,7 +92,7 @@ def assign_to_max_guide(
         """
         counts = adata.X if layer is None else adata.layers[layer]
         if scipy.sparse.issparse(counts):
-            counts = counts.A
+            counts = counts.toarray()
 
         assigned_grna = np.where(
             counts.max(axis=1).squeeze() >= assignment_threshold,
@@ -152,9 +152,9 @@ def plot_heatmap(
 
         if order_by is None:
             if scipy.sparse.issparse(data):
-                max_values = data.max(axis=1).A.squeeze()
+                max_values = data.max(axis=1).toarray().squeeze()
                 data_argmax = data.argmax(axis=1).A.squeeze()
-                max_guide_index = np.where(max_values != data.min(axis=1).A.squeeze(), data_argmax, -1)
+                max_guide_index = np.where(max_values != data.min(axis=1).toarray().squeeze(), data_argmax, -1)
             else:
                 max_guide_index = np.where(
                     data.max(axis=1).squeeze() != data.min(axis=1).squeeze(), data.argmax(axis=1).squeeze(), -1
diff --git a/pertpy/tools/_milo.py b/pertpy/tools/_milo.py
index cffc108d..6fa99959 100644
--- a/pertpy/tools/_milo.py
+++ b/pertpy/tools/_milo.py
@@ -1014,7 +1014,7 @@ def plot_nhood_counts_by_cond(
         if subset_nhoods is None:
             subset_nhoods = nhood_adata.obs_names
 
-        pl_df = pd.DataFrame(nhood_adata[subset_nhoods].X.A, columns=nhood_adata.var_names).melt(
+        pl_df = pd.DataFrame(nhood_adata[subset_nhoods].X.toarray(), columns=nhood_adata.var_names).melt(
             var_name=nhood_adata.uns["sample_col"], value_name="n_cells"
         )
         pl_df = pd.merge(pl_df, nhood_adata.var)
diff --git a/pertpy/tools/_perturbation_space/_discriminator_classifiers.py b/pertpy/tools/_perturbation_space/_discriminator_classifiers.py
index 33fc2768..72644abf 100644
--- a/pertpy/tools/_perturbation_space/_discriminator_classifiers.py
+++ b/pertpy/tools/_perturbation_space/_discriminator_classifiers.py
@@ -408,7 +408,7 @@ def __len__(self):
 
     def __getitem__(self, idx):
         """Returns a sample and corresponding perturbations applied (labels)"""
-        sample = self.data[idx].A.squeeze() if scipy.sparse.issparse(self.data) else self.data[idx]
+        sample = self.data[idx].toarray().squeeze() if scipy.sparse.issparse(self.data) else self.data[idx]
         num_label = self.labels.iloc[idx]
         str_label = self.pert_labels.iloc[idx]