cellrank.tl.lineages

cellrank.tl.lineages(adata, backward=False, copy=False, return_estimator=False, **kwargs)

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() or 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 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 existing adata object or to return a copy.

• return_estimator (bool) – Whether to return the estimator. Only available when copy=False.

• kwargs – Keyword arguments for cellrank.tl.estimators.BaseEstimator.compute_absorption_probabilities().

Returns

Depending on copy and return_estimator, either updates the existing adata object, returns its copy or returns the estimator.

Return type

anndata.AnnData, cellrank.tl.estimators.BaseEstimator or None

Examples

Plot lineages

Plot potential lineage drivers

Plot directed PAGA

Plot gene trends

Plot a heatmap of expression trends

Plot cluster lineage

Plot aggregated cellular fates

Plot circular embedding

Fit models and plot gene trends