scvelo.tl.paga¶

scvelo.tl.
paga
(adata, groups=None, vkey='velocity', use_time_prior=True, root_key=None, end_key=None, threshold_root_end_prior=None, minimum_spanning_tree=True, copy=False)¶ PAGA graph with velocitydirected edges.
Mapping out the coarsegrained connectivity structures of complex manifolds [Wolf19]. By quantifying the connectivity of partitions (groups, clusters) of the singlecell graph, partitionbased graph abstraction (PAGA) generates a much simpler abstracted graph (PAGA graph) of partitions, in which edge weights represent confidence in the presence of connections.
Parameters:  adata :
AnnData
An annotated data matrix.
 groups : key for categorical in adata.obs, optional (default: ‘louvain’)
You can pass your predefined groups by choosing any categorical annotation of observations (adata.obs).
 vkey : str or None (default: None)
Key for annotations of observations/cells or variables/genes.
 use_time_prior : str or bool, optional (default: True)
Obs key for pseudotime values. If True, ‘velocity_pseudotime’ is used if available.
 root_key : str or bool, optional (default: None)
Obs key for root states.
 end_key : str or bool, optional (default: None)
Obs key for end states.
 threshold_root_end_prior : float (default: 0.9)
Threshold for root and final states priors, to be in the range of [0,1]. Values above the threshold will be considered as terminal and included as prior.
 minimum_spanning_tree : bool, optional (default: True)
Whether to prune the tree such that a path from AtoB is removed if another more confident path exists.
 copy : bool, optional (default: False)
Copy adata before computation and return a copy. Otherwise, perform computation inplace and return None.
Returns:  **connectivities** ((adata.uns[‘connectivities’])) – The full adjacency matrix of the abstracted graph, weights correspond to confidence in the connectivities of partitions.
 **connectivities_tree** ((adata.uns[‘connectivities_tree’])) – The adjacency matrix of the treelike subgraph that best explains the topology.
 **transitions_confidence** ((adata.uns[‘transitions_confidence’])) – The adjacency matrix of the abstracted directed graph, weights correspond to confidence in the transitions between partitions.
 adata :