, 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 velocity-directed edges.

Mapping out the coarse-grained connectivity structures of complex manifolds [Wolf19]. By quantifying the connectivity of partitions (groups, clusters) of the single-cell graph, partition-based graph abstraction (PAGA) generates a much simpler abstracted graph (PAGA graph) of partitions, in which edge weights represent confidence in the presence of connections.

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 pseudo-time 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 A-to-B 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.


  • **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 tree-like 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.