episcanpy.tl.leiden¶
-
episcanpy.tl.
leiden
(adata, resolution=1, *, restrict_to=None, random_state=0, key_added='leiden', adjacency=None, directed=True, use_weights=True, n_iterations=-1, partition_type=None, copy=False)¶ Cluster cells into subgroups [Traag18].
Cluster cells using the Leiden algorithm [Traag18], an improved version of the Louvain algorithm [Blondel08]. The Louvain algorithm has been proposed for single-cell analysis by [Levine15].
This requires having ran
neighbors()
orbbknn()
first.- Parameters
- adata
The annotated data matrix.
- resolution
A parameter value controlling the coarseness of the clustering. Higher values lead to more clusters. Set to None if overriding partition_type to one that doesn’t accept a resolution_parameter.
- random_state
Change the initialization of the optimization.
- restrict_to
Restrict the clustering to the categories within the key for sample annotation, tuple needs to contain (obs_key, list_of_categories).
- key_added
adata.obs key under which to add the cluster labels. (default: ‘leiden’)
- adjacency
Sparse adjacency matrix of the graph, defaults to adata.uns[‘neighbors’][‘connectivities’].
- directed
Whether to treat the graph as directed or undirected.
- use_weights
If True, edge weights from the graph are used in the computation (placing more emphasis on stronger edges).
- n_iterations
How many iterations of the Leiden clustering algorithm to perform. Positive values above 2 define the total number of iterations to perform, -1 has the algorithm run until it reaches its optimal clustering.
- partition_type
Type of partition to use. Defaults to
RBConfigurationVertexPartition
. For the available options, consult the documentation forfind_partition()
.- copy
Whether to copy adata or modify it inplace.
- **partition_kwargs
Any further arguments to pass to ~leidenalg.find_partition (which in turn passes arguments to the partition_type).
- Returns
- adata.obs[key_added]
Array of dim (number of samples) that stores the subgroup id (‘0’, ‘1’, …) for each cell.
- adata.uns[‘leiden’][‘params’]
A dict with the values for the parameters resolution, random_state, and n_iterations.