evolocity.tl.random_walk¶
-
evolocity.tl.random_walk(data, root_node=0, walk_length=10, n_walks=1, forward_walk=True, path_key='rw_paths', vkey='velocity', groupby=None, groups=None, self_transitions=False, eps=0.001, random_state=0, copy=False, **kwargs)¶ Runs a random walk on the evolocity graph.
- Parameters
- data :
AnnData Annotated data matrix.
- root_node : int (default: 0)
Index of node at which to start random walk.
- walk_length : int (default: 10)
Number of steps in walk.
- n_walks : int (default: 1)
Number of walks to take.
- forward_walk : bool (default: True)
Whether to go in the same or reverse direction of evolocity.
- path_key : str (default: ‘rw_paths’)
Name at which to store the random walks.
- vkey : str (default: ‘velocity’)
Name of velocity estimates to be used.
- groupby : str, list or np.ndarray (default: None)
Key of observations grouping to consider. Only to be set, if each group is assumed to have a distinct lineage with an independent root and end point.
- groups : str, list or np.ndarray (default: None)
Groups selected to find terminal states on. Must be an element of .obs[groupby]. To be specified only for very distinct/disconnected clusters.
- self_transitions : bool (default: False)
Allow transitions from one node to itself.
- eps : float (default: 1e-3)
Tolerance for eigenvalue selection.
- random_state : int or None (default: 0)
Seed used by the random number generator. If None, use the RandomState instance by np.random.
- copy : bool (default: False)
Return a copy instead of writing to data.
- **kwargs
Passed to evolocity.tl.transition_matrix(), e.g. scale, basis.
- data :
- Returns
Returns or updates data with the attributes
rw_paths (.uns) – rows of matrix correspond to random walks, columns correspond to steps