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 velocity-directed edges.

Mapping out the coarse-grained connectivity structures of complex manifolds [Wolf et al., 2019]. 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.

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 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.

Returns:

  • connectivities (.uns) – The full adjacency matrix of the abstracted graph, weights correspond to confidence in the connectivities of partitions.

  • connectivities_tree (.uns) – The adjacency matrix of the tree-like subgraph that best explains the topology.

  • transitions_confidence (.uns) – The adjacency matrix of the abstracted directed graph, weights correspond to confidence in the transitions between partitions.