def test_backward_manual_dense_norm(self, adata):
backward = True
vk = VelocityKernel(adata,
backward=backward).compute_transition_matrix(
density_normalize=False)
ck = ConnectivityKernel(adata,
backward=backward).compute_transition_matrix(
density_normalize=False)
# combine the kernels
comb = 0.8 * vk + 0.2 * ck
T_1 = comb.transition_matrix
conn = _get_neighs(adata, "connectivities")
T_1 = density_normalization(T_1, conn)
T_1 = _normalize(T_1)
transition_matrix(
adata,
diff_kernel="sum",
weight_diffusion=0.2,
density_normalize=True,
backward=backward,
)
T_2 = adata.uns[_transition(Direction.BACKWARD)]["T"]
np.testing.assert_allclose(T_1.A, T_2.A, rtol=_rtol)
def test_palantir_differ_dense_norm(self, adata: AnnData):
conn = _get_neighs(adata, "connectivities")
n_neighbors = _get_neighs_params(adata)["n_neighbors"]
pseudotime = adata.obs["latent_time"]
conn_biased = bias_knn(conn, pseudotime, n_neighbors)
T_1 = density_normalization(conn_biased, conn)
T_1 = _normalize(T_1)
pk = PalantirKernel(adata,
time_key="latent_time").compute_transition_matrix(
density_normalize=False)
T_2 = pk.transition_matrix
assert not np.allclose(T_1.A, T_2.A, rtol=_rtol)