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_macrostates(n_states=2)
mc.set_terminal_states_from_macrostates()
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_allclose(mc.absorption_probabilities.X.sum(1),
1.0,
rtol=1e-6)
return adata, mc
scv.tl.velocity_graph(samples[key],
mode_neighbors='connectivities',
compute_uncertainties=True)
# Forward direction (final states)
outdir = 'results/trajectory/cellrank/forward'
if not os.path.exists(outdir):
os.makedirs(outdir)
scv.settings.figdir = outdir
for key in samples:
vk = VelocityKernel(samples[key])
vk.compute_transition_matrix(softmax_scale=None)
g = GPCCA(vk)
g.compute_schur(n_components=20)
g.plot_spectrum(real_only=False, save="{}_eigenvalues.png".format(key))
if key == "H508_EV" or key == "HT29_EV":
g.plot_schur(use=4,
cluster_key=clusters,
show=False,
dpi=300,
save='{}_schur.png'.format(key))
g.compute_metastable_states(n_states=4, cluster_key=clusters)
g.plot_metastable_states(show=False,
dpi=300,
save='{}_metastable.png'.format(key))
g.plot_metastable_states(
same_plot=False,
show=False,
dpi=300,