def test_reproducibility(self, adata: AnnData, n_jobs: int):
spatial_neighbors(adata)
res1 = nhood_enrichment(adata,
cluster_key=_CK,
seed=42,
n_jobs=n_jobs,
n_perms=20,
copy=True)
res2 = nhood_enrichment(adata,
cluster_key=_CK,
seed=42,
n_jobs=n_jobs,
n_perms=20,
copy=True)
res3 = nhood_enrichment(adata,
cluster_key=_CK,
seed=43,
n_jobs=n_jobs,
n_perms=20,
copy=True)
assert len(res1) == len(res2)
assert len(res2) == len(res3)
for key in range(len(res1)):
np.testing.assert_array_equal(res2[key], res1[key])
if key == 0: # z-score
with pytest.raises(AssertionError):
np.testing.assert_array_equal(res3[key], res2[key])
else: # counts
np.testing.assert_array_equal(res3[key], res2[key])
def test_spatial_neighbors_non_visium(non_visium_adata: AnnData):
"""
check correctness of neighborhoods for non-visium coordinates
"""
correct_knn_graph = np.array([
[0.0, 1.0, 1.0, 1.0],
[1.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 1.0],
[1.0, 1.0, 1.0, 0.0],
])
correct_radius_graph = np.array([
[0.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 0.0],
])
spatial_neighbors(non_visium_adata, n_neigh=3, coord_type=None)
spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()
assert np.array_equal(spatial_graph, correct_knn_graph)
spatial_neighbors(non_visium_adata, radius=5.0, coord_type=None)
spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()
assert np.array_equal(spatial_graph, correct_radius_graph)
def test_plot_nhood_enrichment_dendro(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.nhood_enrichment(adata, cluster_key=C_KEY)
# use count to avoid nan for scipy.cluster.hierarchy
pl.nhood_enrichment(adata,
cluster_key=C_KEY,
mode="count",
method="single")
def test_plot_centrality_scores_single(self, adata: AnnData):
selected_score = "degree_centrality"
gr.spatial_neighbors(adata)
gr.centrality_scores(adata, cluster_key=C_KEY)
pl.centrality_scores(adata,
cluster_key=C_KEY,
score=selected_score,
dpi=DPI)
def test_parallel_works(self, adata: AnnData, backend: str):
spatial_neighbors(adata)
nhood_enrichment(adata,
cluster_key=_CK,
n_jobs=2,
n_perms=20,
backend=backend)
self._assert_common(adata)
def test_plot_cbar_kwargs(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.nhood_enrichment(adata, cluster_key=C_KEY)
pl.nhood_enrichment(adata,
cluster_key=C_KEY,
cbar_kwargs={
"label": "FOOBARBAZQUUX",
"filled": False
})
def test_spatial_neighbors_visium(visium_adata: AnnData, n_rings: int,
n_neigh: int, sum_dist: int):
"""
check correctness of neighborhoods for visium coordinates
"""
spatial_neighbors(visium_adata, n_rings=n_rings)
assert visium_adata.obsp[Key.obsp.spatial_conn()][0].sum() == n_neigh
assert visium_adata.uns[
Key.uns.spatial_neighs()]["distances_key"] == Key.obsp.spatial_dist()
if n_rings > 1:
assert visium_adata.obsp[Key.obsp.spatial_dist()][0].sum() == sum_dist
def test_spatial_neighbors_non_visium(non_visium_adata: AnnData):
"""
check correctness of neighborhoods for non-visium coordinates
"""
correct_knn_graph = np.array([
[0.0, 1.0, 1.0, 1.0],
[1.0, 0.0, 1.0, 1.0],
[1.0, 1.0, 0.0, 1.0],
[1.0, 1.0, 1.0, 0.0],
])
correct_radius_graph = np.array([
[0.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0],
[1.0, 1.0, 0.0, 0.0],
])
correct_delaunay_graph = np.array([[0.0, 1.0, 0.0, 1.0],
[1.0, 0.0, 1.0, 1.0],
[0.0, 1.0, 0.0, 1.0],
[1.0, 1.0, 1.0, 0.0]])
correct_delaunay_dist = np.array([
[0.0, 2.0, 0.0, 4.12310563],
[2.0, 0.0, 6.32455532, 5.0],
[0.0, 6.32455532, 0.0, 5.38516481],
[4.12310563, 5.0, 5.38516481, 0.0],
])
spatial_neighbors(non_visium_adata, n_neigh=3, coord_type=None)
spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()
assert np.array_equal(spatial_graph, correct_knn_graph)
spatial_neighbors(non_visium_adata, radius=5.0, coord_type=None)
spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()
assert np.array_equal(spatial_graph, correct_radius_graph)
spatial_neighbors(non_visium_adata, delaunay=True, coord_type=None)
spatial_graph = non_visium_adata.obsp[Key.obsp.spatial_conn()].toarray()
spatial_dist = non_visium_adata.obsp[Key.obsp.spatial_dist()].toarray()
assert np.array_equal(spatial_graph, correct_delaunay_graph)
np.testing.assert_allclose(spatial_dist, correct_delaunay_dist)
def test_nhood_enrichment(self, adata: AnnData):
spatial_neighbors(adata)
nhood_enrichment(adata, cluster_key=_CK)
self._assert_common(adata)
def test_plot_cbar_vmin_vmax(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.nhood_enrichment(adata, cluster_key=C_KEY)
pl.nhood_enrichment(adata, cluster_key=C_KEY, vmin=10, vmax=20)
def test_plot_ripley_k_palette(self, adata_palette: AnnData):
adata = adata_palette
gr.spatial_neighbors(adata)
gr.ripley_k(adata, cluster_key=C_KEY)
pl.ripley_k(adata, cluster_key=C_KEY)
def test_plot_ripley_k(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.ripley_k(adata, cluster_key=C_KEY)
pl.ripley_k(adata, cluster_key=C_KEY)
def test_plot_nhood_enrichment(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.nhood_enrichment(adata, cluster_key=C_KEY)
pl.nhood_enrichment(adata, cluster_key=C_KEY)
def test_plot_centrality_scores(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.centrality_scores(adata, cluster_key=C_KEY)
pl.centrality_scores(adata, cluster_key=C_KEY)
def test_plot_interaction_dendro(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.interaction_matrix(adata, cluster_key=C_KEY)
pl.interaction_matrix(adata, cluster_key=C_KEY, method="single")
def test_plot_interaction(self, adata: AnnData):
gr.spatial_neighbors(adata)
gr.interaction_matrix(adata, cluster_key=C_KEY)
pl.interaction_matrix(adata, cluster_key=C_KEY)