def test_compute_macrostates_min_chi_normal_run(self,
adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_macrostates(n_states=[2, 4], use_min_chi=True)
_check_compute_macro(mc)
def test_rename_terminal_states_try_joinining_states(
self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(terminal_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_terminal_states(use=2)
with pytest.raises(ValueError):
mc.rename_terminal_states({"0, 1": "foo"})
def test_compute_gdpt_cellname_iroot(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.adata.uns["iroot"] = mc.adata.obs_names[0]
mc.compute_gdpt()
assert "gdpt_pseudotime" in mc.adata.obs
def test_rename_terminal_states_normal_run(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(terminal_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_terminal_states(use=2)
mc.rename_terminal_states({"0": "foo", "1": "bar"})
np.testing.assert_array_equal(mc._get(P.TERM).cat.categories, ["foo", "bar"])
def test_manual_combination_no_precomputed(self, adata: AnnData):
density_normalize = False
vk = VelocityKernel(adata).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata).compute_transition_matrix(
density_normalize=density_normalize)
T_vk = vk.transition_matrix
T_ck = ck.transition_matrix
T_comb_manual = 0.8 * T_vk + 0.2 * T_ck
vk = VelocityKernel(adata).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata).compute_transition_matrix(
density_normalize=density_normalize)
comb_kernel = 0.8 * vk + 0.2 * ck
comb_kernel.compute_transition_matrix()
T_comb_kernel = comb_kernel.transition_matrix
np.testing.assert_allclose(T_comb_manual.A,
T_comb_kernel.A,
rtol=_rtol)
def test_plot_lineage_drivers_not_computed(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(terminal_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_terminal_states(use=2)
mc.compute_absorption_probabilities()
with pytest.raises(RuntimeError):
mc.plot_lineage_drivers("0")
def test_plot_lineage_drivers_normal_run(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(final_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_final_states(use=2)
mc.compute_absorption_probabilities()
mc.compute_lineage_drivers(use_raw=False, cluster_key="clusters")
mc.plot_lineage_drivers("0", use_raw=False)
def test_compute_gdpt_cellname_key_added(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_gdpt(key_added="foobar")
assert "foobar" in mc.adata.obs
def test_compute_terminal_states_no_cells(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_macrostates(n_states=2)
with pytest.raises(TypeError):
mc.compute_terminal_states(n_cells=None)
def create_kernels(
adata: AnnData,
velocity_variances: Optional[str] = None,
connectivity_variances: Optional[str] = None,
) -> Tuple[VelocityKernel, ConnectivityKernel]:
vk = VelocityKernel(adata)
vk._mat_scaler = adata.uns.get(
velocity_variances, np.random.normal(size=(adata.n_obs, adata.n_obs))
)
ck = ConnectivityKernel(adata)
ck._mat_scaler = adata.uns.get(
connectivity_variances, np.random.normal(size=(adata.n_obs, adata.n_obs))
)
vk._transition_matrix = csr_matrix(np.eye(adata.n_obs))
ck._transition_matrix = np.eye(adata.n_obs, k=1) / 2 + np.eye(adata.n_obs) / 2
ck._transition_matrix[-1, -1] = 1
ck._transition_matrix = csr_matrix(ck._transition_matrix)
np.testing.assert_allclose(
np.sum(ck._transition_matrix.A, axis=1), 1
) # sanity check
return vk, ck
def test_compute_metastable_cache(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=11, method="krylov")
mc.compute_metastable_states(n_states=2)
assert mc._get(P.SCHUR).shape[1] == 11
assert mc._get(P.SCHUR_MAT).shape == (11, 11)
def test_plot_lineage_drivers_invalid_n_genes(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(terminal_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_terminal_states(use=2)
mc.compute_absorption_probabilities()
mc.compute_lineage_drivers(use_raw=False, cluster_key="clusters")
with pytest.raises(ValueError):
mc.plot_lineage_drivers("0", use_raw=False, n_genes=0)
def test_rename_terminal_states_empty_mapping(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(terminal_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_terminal_states(use=2)
orig_cats = list(mc._get(P.TERM).cat.categories)
mc.rename_terminal_states({})
np.testing.assert_array_equal(mc._get(P.TERM).cat.categories, orig_cats)
def test_compute_lineage_drivers_invalid_lineages(self,
adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(final_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_final_states(use=2)
mc.compute_absorption_probabilities()
with pytest.raises(KeyError):
mc.compute_lineage_drivers(use_raw=False, lineages=["foo"])
def test_set_final_states_from_metastable_states_non_positive_cells(
self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_metastable_states(n_states=2, n_cells=None)
with pytest.raises(ValueError):
mc.set_final_states_from_metastable_states(n_cells=0)
def test_set_final_states_from_metastable_states_invalid_name(
self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_metastable_states(n_states=2)
with pytest.raises(KeyError):
mc.set_final_states_from_metastable_states(names=["foobar"])
def test_compute_lineage_drivers_normal_run(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(final_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_final_states(use=2)
mc.compute_absorption_probabilities()
mc.compute_lineage_drivers(use_raw=False, cluster_key="clusters")
for lineage in ["0", "1"]:
assert f"{DirPrefix.FORWARD} {lineage}" in mc.adata.var.keys()
def test_set_terminal_states_from_macrostates_rename_not_unique_new_names(
self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_macrostates(n_states=3, n_cells=5)
with pytest.raises(ValueError):
mc.set_terminal_states_from_macrostates({"0": "1", "1": "1"})
_check_renaming_no_write_terminal(mc)
def test_compute_initial_states_from_forward_no_macro(
self, adata_large: AnnData):
vk = VelocityKernel(
adata_large,
backward=False).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large,
backward=False).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
with pytest.raises(RuntimeError):
mc._compute_initial_states(1)
def test_combination_correct_parameters(self, adata: AnnData):
from cellrank.tl.kernels import CosineScheme
k = VelocityKernel(adata).compute_transition_matrix(
softmax_scale=4,
seed=42,
scheme="cosine",
) + (ConnectivityKernel(adata).compute_transition_matrix(
density_normalize=False) +
ConnectivityKernel(adata).compute_transition_matrix(
density_normalize=True))
k.compute_transition_matrix()
assert isinstance(k.params, dict)
assert len(k.params) == 3
assert {"dnorm": True} in k.params.values()
assert {"dnorm": False} in k.params.values()
assert {
"softmax_scale": 4,
"mode": "deterministic",
"seed": 42,
"scheme": str(CosineScheme()),
} in k.params.values()
def test_plot_lineage_drivers_not_computed(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_metastable_states(n_states=2)
mc.set_final_states_from_metastable_states()
mc.compute_absorption_probabilities()
with pytest.raises(RuntimeError):
mc.plot_lineage_drivers("0")
def test_adding_hidden_constants(self, adata: AnnData):
k = VelocityKernel(adata) + ConnectivityKernel(adata)
assert _is_bin_mult(k[0])
assert isinstance(k[0], KernelMul)
assert isinstance(k[0][0], Constant)
assert isinstance(k[0][1], VelocityKernel)
assert k[0][0].transition_matrix == 1.0
assert _is_bin_mult(k[1])
assert isinstance(k[1], KernelMul)
assert isinstance(k[1][0], Constant)
assert isinstance(k[1][1], ConnectivityKernel)
assert k[1][0].transition_matrix == 1.0
def test_plot_lineage_drivers_normal_run(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_metastable_states(n_states=2)
mc.set_final_states_from_metastable_states()
mc.compute_absorption_probabilities()
mc.compute_lineage_drivers(use_raw=False, cluster_key="clusters")
mc.plot_lineage_drivers("0", use_raw=False)
def test_compute_final_states(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_metastable_states(n_states=2)
mc.compute_final_states(n_cells=5)
mc.compute_absorption_probabilities()
_check_abs_probs(mc)
def compare_with_scanpy(self, adata: AnnData):
# check whether cellrank's transition matrix matches scanpy's
density_normalize = False
ck = ConnectivityKernel(adata).compute_transition_matrix(
density_normalize=density_normalize)
T_cr = ck.transition_matrix
neigh = Neighbors(adata)
neigh.compute_transitions(density_normalize=density_normalize)
T_sc = neigh.transitions
# check whether these are the same while leaving them sparse
assert T_sc.shape == T_cr.shape
assert len(T_sc.indices) == len(T_cr.indices)
assert np.allclose((T_cr - T_sc).data, 0)
def test_compute_terminal_states_n_main_states(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_macrostates(n_states=2)
mc.compute_terminal_states(n_cells=5, method="top_n", n_states=1)
mc.compute_absorption_probabilities()
mc.compute_lineage_priming()
_check_abs_probs(mc)
def test_plot_lineage_drivers_invalid_n_genes(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(terminal_kernel)
mc.compute_schur(n_components=10, method="krylov")
mc.compute_macrostates(n_states=2)
mc.set_terminal_states_from_macrostates()
mc.compute_absorption_probabilities()
mc.compute_lineage_drivers(use_raw=False, cluster_key="clusters")
with pytest.raises(ValueError):
mc.plot_lineage_drivers("0", use_raw=False, n_genes=0)
def test_compute_approx_normal_run(self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
terminal_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.CFLARE(terminal_kernel)
mc.compute_eigendecomposition(k=5)
mc.compute_terminal_states(use=2)
assert is_categorical_dtype(mc._get(P.TERM))
assert mc._get(P.TERM_PROBS) is not None
assert TermStatesKey.FORWARD.s in mc.adata.obs.keys()
assert _probs(TermStatesKey.FORWARD) in mc.adata.obs.keys()
assert _colors(TermStatesKey.FORWARD) in mc.adata.uns.keys()
def test_compute_metastable_states_min_chi_dict_wrong_keys(
self, adata_large: AnnData):
vk = VelocityKernel(adata_large).compute_transition_matrix(
softmax_scale=4)
ck = ConnectivityKernel(adata_large).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = cr.tl.estimators.GPCCA(final_kernel)
mc.compute_schur(n_components=10, method="krylov")
with pytest.raises(KeyError):
mc.compute_metastable_states(n_states={
"foo": 2,
"max": 3
},
use_min_chi=True)
def test_manual_combination_sam(self, adata: AnnData):
mode = "sampling"
vk = VelocityKernel(adata).compute_transition_matrix(mode=mode,
softmax_scale=4)
ck = ConnectivityKernel(adata).compute_transition_matrix()
T_vk = vk.transition_matrix
T_ck = ck.transition_matrix
T_comb_manual = 0.8 * T_vk + 0.2 * T_ck
comb_kernel = 0.8 * vk + 0.2 * ck
T_comb_kernel = comb_kernel.transition_matrix
np.testing.assert_allclose(T_comb_kernel.A,
T_comb_manual.A,
rtol=_rtol)
