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
def lineages(
adata: AnnData,
backward: bool = False,
copy: bool = False,
return_estimator: bool = False,
**kwargs,
) -> Optional[AnnData]:
"""
Compute probabilistic lineage assignment using RNA velocity.
For each cell `i` in :math:`{1, ..., N}` and %(initial_or_terminal)s state `j` in :math:`{1, ..., M}`,
the probability is computed that cell `i` is either going to %(terminal)s state `j` (``backward=False``)
or is coming from %(initial)s state `j` (``backward=True``).
This function computes the absorption probabilities of a Markov chain towards the %(initial_or_terminal) states
uncovered by :func:`cellrank.tl.initial_states` or :func:`cellrank.tl.terminal_states` using a highly efficient
implementation that scales to large cell numbers.
It's also possible to calculate mean and variance of the time until absorption for all or just a subset
of the %(initial_or_terminal)s states. This can be seen as a pseudotemporal measure, either towards any terminal
population of the state change trajectory, or towards specific ones.
Parameters
----------
%(adata)s
%(backward)s
copy
Whether to update the existing ``adata`` object or to return a copy.
return_estimator
Whether to return the estimator. Only available when ``copy=False``.
**kwargs
Keyword arguments for :meth:`cellrank.tl.estimators.BaseEstimator.compute_absorption_probabilities`.
Returns
-------
:class:`anndata.AnnData`, :class:`cellrank.tl.estimators.BaseEstimator` or :obj:`None`
Depending on ``copy`` and ``return_estimator``, either updates the existing ``adata`` object,
returns its copy or returns the estimator.
"""
if backward:
lin_key = AbsProbKey.BACKWARD
fs_key = TermStatesKey.BACKWARD
fs_key_pretty = TerminalStatesPlot.BACKWARD
else:
lin_key = AbsProbKey.FORWARD
fs_key = TermStatesKey.FORWARD
fs_key_pretty = TerminalStatesPlot.FORWARD
try:
pk = PrecomputedKernel(adata=adata, backward=backward)
except KeyError as e:
raise RuntimeError(
f"Compute transition matrix first as `cellrank.tl.transition_matrix(..., backward={backward})`."
) from e
start = logg.info(
f"Computing lineage probabilities towards {fs_key_pretty.s}")
mc = GPCCA(
pk, read_from_adata=True, inplace=not copy
) # GPCCA is more general than CFLARE, in terms of what is saves
if mc._get(P.TERM) is None:
raise RuntimeError(
f"Compute the states first as `cellrank.tl.{fs_key.s}(..., backward={backward})`."
)
# compute the absorption probabilities
mc.compute_absorption_probabilities(**kwargs)
logg.info(f"Adding lineages to `adata.obsm[{lin_key.s!r}]`\n Finish",
time=start)
return mc.adata if copy else mc if return_estimator else None
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))
if key == "H508_EV":
g.plot_lineage_drivers(