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.
- adata :
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.
- adata :
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.