cellrank.tl.initial_states¶
- cellrank.tl.initial_states(adata, estimator=<class 'cellrank.tl.estimators._gpcca.GPCCA'>, mode='deterministic', backward_mode='transpose', n_states=None, cluster_key=None, key=None, force_recompute=False, show_plots=False, copy=False, return_estimator=False, fit_kwargs=mappingproxy({}), **kwargs)[source]¶
Find initial states of a dynamic process of single cells based on RNA velocity [La Manno et al., 2018].
The function models dynamic cellular processes as a Markov chain, where the transition matrix is computed based on the velocity vector of each individual cell. Based on this Markov chain, we provide two estimators to compute initial states, both of which are based on spectral methods.
For the estimator
cellrank.tl.estimators.GPCCA
, cells are fuzzily clustered into macrostates, using Generalized Perron Cluster Cluster Analysis [Reuter et al., 2018]. In short, this coarse-grains the Markov chain into a set of macrostates representing the slow time-scale dynamics, i.e. transitions between these macrostates are rare. The most stable ones of these will represent initial, while the others represent intermediate macrostates.For the estimator
cellrank.tl.estimators.CFLARE
, cells are filtered into transient/recurrent cells using the left eigenvectors of the transition matrix and clustered into distinct groups of initial states using the right eigenvectors of the transition matrix of the Markov chain.- Parameters
adata (
anndata.AnnData
) – Annotated data object.estimator (
PropertyMeta
) – Estimator class to use to compute the initial states.mode (
str
) –How to compute transition probabilities. Valid options are:
’deterministic’ - deterministic computation that doesn’t propagate uncertainty.
’monte_carlo’ - Monte Carlo average of randomly sampled velocity vectors.
’stochastic’ - second order approximation, only available when
jax
is installed.’sampling’ - sample 1 transition matrix from the velocity distribution.
backward_mode (
str
) –How to compute the backward transitions. Valid options are:
’transpose’ - compute transitions from neighboring cells j to cell i.
’negate’ - negate the velocity vector.
n_states (
Optional
[int
]) – If you know how many initial states you are expecting, you can provide this number. Otherwise, an eigengap heuristic is used.cluster_key (
Optional
[str
]) – Key fromadata.obs
where cluster annotations are stored. These are used to give names to the initial states.key (
Optional
[str
]) – Key inadata.obsp
where the transition matrix is saved. If not found, compute a new one usingcellrank.tl.transition_matrix()
.force_recompute (
bool
) – Whether to always recompute the transition matrix even if one exists.show_plots (
bool
) – Whether to show plots of the spectrum and eigenvectors in the embedding.n_jobs – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.
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
.fit_kwargs (
Mapping
) – Keyword arguments forcellrank.tl.BaseEstimator.fit()
, such asn_cells
.kwargs – Keyword arguments for
cellrank.tl.transition_matrix()
, such asweight_connectivities
orsoftmax_scale
.
- Returns
Depending on
copy
andreturn_estimator
, either updates the existingadata
object, returns its copy or returns the estimator.Marked cells are added to
adata.obs['initial_states']
.- Return type
anndata.AnnData
,cellrank.tl.estimators.BaseEstimator
orNone