cellrank.tl.lineages
- cellrank.tl.lineages(adata, backward=False, copy=False, return_estimator=False, **kwargs)[source]
Compute probabilistic lineage assignment using RNA velocity.
For each cell i in \({1, ..., N}\) and initial or terminal state j in \({1, ..., M}\), the probability is computed that cell i is either going to terminal state j (
backward=False
) or is coming from initial state j (backward=True
).This function computes the absorption probabilities of a Markov chain towards the initial or terminaltates uncovered by
cellrank.tl.initial_states()
orcellrank.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 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 (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.copy (
bool
) – Whether to update the existingadata
object or to return a copy.return_estimator (
bool
) – Whether to return the estimator. Only available whencopy = False
.kwargs (
Any
) – Keyword arguments forcellrank.tl.estimators.BaseEstimator.compute_absorption_probabilities()
.
- Return type
- Returns
Depending on
copy
andreturn_estimator
, either updates the existingadata
object, returns its copy or returns the estimator.