diff --git a/tests/utils_diffusion_maps_from_kernel.py b/tests/utils_diffusion_maps_from_kernel.py
index 1732e6f7..c533bc65 100644
--- a/tests/utils_diffusion_maps_from_kernel.py
+++ b/tests/utils_diffusion_maps_from_kernel.py
@@ -1,3 +1,4 @@
+import pytest
 import numpy as np
 import pandas as pd
 from scipy.sparse import csr_matrix
@@ -33,12 +34,9 @@ def test_diffusion_maps_seed():
     kernel = create_mock_kernel(50)
     result1 = diffusion_maps_from_kernel(kernel, seed=0)
     result2 = diffusion_maps_from_kernel(kernel, seed=0)
-    result3 = diffusion_maps_from_kernel(kernel, seed=1)
 
     # Seed usage should yield the same result
     assert np.allclose(result1["EigenValues"], result2["EigenValues"])
-    # Different seed should yield different results
-    assert not np.allclose(result1["EigenValues"], result3["EigenValues"])
 
 def test_diffusion_maps_eigen():
     kernel = create_mock_kernel(50)
diff --git a/tests/utils_run_diffusion_maps.py b/tests/utils_run_diffusion_maps.py
new file mode 100644
index 00000000..31b677fb
--- /dev/null
+++ b/tests/utils_run_diffusion_maps.py
@@ -0,0 +1,50 @@
+import pytest
+import pandas as pd
+import scanpy as sc
+from scipy.sparse import csr_matrix, issparse
+import numpy as np
+
+from palantir.utils import run_diffusion_maps
+
+# Generate mock DataFrame data
+def mock_dataframe(rows, cols):
+    return pd.DataFrame(np.random.rand(rows, cols))
+
+# Generate mock sc.AnnData object
+def mock_anndata(rows, cols, keys):
+    ad = sc.AnnData(np.random.rand(rows, cols))
+    for key in keys:
+        ad.obsm[key] = np.random.rand(rows, cols)
+    return ad
+
+def test_run_diffusion_maps_dataframe():
+    df = mock_dataframe(50, 30)
+    result = run_diffusion_maps(df)
+    
+    assert isinstance(result, dict)
+    assert set(result.keys()) == {'T', 'EigenVectors', 'EigenValues', 'kernel'}
+    
+    assert isinstance(result['kernel'], csr_matrix)
+    assert isinstance(result['T'], csr_matrix)
+    assert isinstance(result['EigenVectors'], pd.DataFrame)
+    assert isinstance(result['EigenValues'], pd.Series)
+
+def test_run_diffusion_maps_anndata():
+    keys = ['X_pca']
+    ad = mock_anndata(50, 30, keys)
+    result = run_diffusion_maps(ad)
+    
+    assert 'DM_Kernel' in ad.obsp
+    assert 'DM_Similarity' in ad.obsp
+    assert 'DM_EigenVectors' in ad.obsm
+    assert 'DM_EigenValues' in ad.uns
+    
+    assert np.array_equal(ad.obsp['DM_Kernel'].toarray(), result['kernel'].toarray())
+    assert np.array_equal(ad.obsp['DM_Similarity'].toarray(), result['T'].toarray())
+    assert np.array_equal(ad.obsm['DM_EigenVectors'], result['EigenVectors'].values)
+    assert np.array_equal(ad.uns['DM_EigenValues'], result['EigenValues'])
+
+def test_run_diffusion_maps_exceptions():
+    # Test with neither pd.DataFrame nor sc.AnnData
+    with pytest.raises(ValueError):
+        run_diffusion_maps("invalid_type")