scFates.tl.cellrank_to_tree

scFates.tl.cellrank_to_tree(adata, time, Nodes, method='ppt', ppt_lambda=100, auto_root=False, root_params={}, reassign_pseudotime=False, key_cellrank='term_states_fwd_memberships', copy=False, **kwargs)

Converts CellRank [Lange21] fate probabilities into a principal tree that can be analysed by scFates.

It combines the projection generated by cr.pl.circular_projection with any measure of differentiation (CytoTRACE, latent time). A tree is fitted onto this new embedding.

Parameters:

adata

Annotated data matrix.

time

time key to use for the additional dimension used in combination with cr.pl.circular_projection.

Nodes int

Number of nodes that compose the principal graph.

method Literal['ppt', 'epg'] (default: 'ppt')

If ppt, uses simpleppt approach, ppt_lambda and ppt_sigma are the parameters controlling the algorithm. If epg, uses ComputeElasticPrincipalTree function from elpigraph python package, epg_lambda epg_mu and epg_trimmingradius are the parameters controlling the algorithm.

ppt_lambda int (default: 100)

Parameter for simpleppt, penalty for the tree length [Mao15]. Usually works well at default for the conversion.

auto_root bool (default: False)

Automatically select the root tip using the time key.

min_val

min_val parameter from scFates.tl.root()

reassign_pseudotime bool (default: False)

whether use the time key to replace the distances comptued from the tree.

key_cellrank default: 'term_states_fwd_memberships'

where to get the forward terminal fate memberships.

copy default: False

Return a copy instead of writing to adata.

kwargs

arguments to pass to function scFates.tl.tree().

Returns:

adata : anndata.AnnData if copy=True it returns or else add fields to adata:

.obsm[‘X_fates’]

representation generated by combining the time key with projection generated by cellrank.pl.circular_projection().

.uns[‘ppt’]

dictionnary containing information from simpelppt tree if method=’ppt’

.uns[‘epg’]

dictionnary containing information from elastic principal tree if method=’epg’

.uns[‘graph’][‘B’]

adjacency matrix of the principal points

.uns[‘graph’][‘R’]

soft assignment of cells to principal point in representation space

.uns[‘graph’][‘F’]

coordinates of principal points in representation spac