def _create_gpcca(*, backward: bool = False) -> Tuple[AnnData, GPCCA]:
adata = _adata_medium.copy()
sc.tl.paga(adata, groups="clusters")
vk = VelocityKernel(adata, backward=backward).compute_transition_matrix(
softmax_scale=4
)
ck = ConnectivityKernel(adata, backward=backward).compute_transition_matrix()
final_kernel = 0.8 * vk + 0.2 * ck
mc = GPCCA(final_kernel)
mc.compute_partition()
mc.compute_eigendecomposition()
mc.compute_schur(method="krylov")
mc.compute_metastable_states(n_states=2)
mc.set_final_states_from_metastable_states()
mc.compute_absorption_probabilities()
mc.compute_lineage_drivers(cluster_key="clusters", use_raw=False)
assert adata is mc.adata
if backward:
assert str(AbsProbKey.BACKWARD) in adata.obsm
else:
assert str(AbsProbKey.FORWARD) in adata.obsm
np.testing.assert_array_almost_equal(mc.absorption_probabilities.sum(1), 1)
return adata, mc
cluster_key=clusters,
show=False,
dpi=300,
save='{}_schur.png'.format(key))
g.compute_metastable_states(n_states=1, cluster_key=clusters)
g.plot_metastable_states(show=False,
dpi=300,
save='{}_metastable.png'.format(key))
g.plot_metastable_states(discrete=True,
show=False,
dpi=300,
legend_fontsize=0,
save='{}_discrete_metastable.pdf'.format(key),
size=28)
g.set_final_states_from_metastable_states()
if key == "H508_EV" or key == "HT29_EV":
g.compute_absorption_probabilities()
g.compute_lineage_drivers()
samples[key].var.to_csv(key + "_lineages.tsv", sep='\t')
print(samples[key].var.columns)
if key == "HT29_EV":
g.plot_lineage_drivers(
'17', save='{}_17_lineage_drivers.png'.format(key))
g.plot_lineage_drivers(
'1', save='{}_10_lineage_drivers.png'.format(key))
g.plot_lineage_drivers('11',
save='{}_1_lineage_drivers.png'.format(key))
g.plot_lineage_drivers('4',
save='{}_4_lineage_drivers.png'.format(key))