episcanpy.api.tl.leiden

episcanpy.api.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, neighbors_key=None, obsp=None, copy=False, **partition_kwargs)

Cluster cells into subgroups [Traag18].

Cluster cells using the Leiden algorithm [Traag18], an improved version of the Louvain algorithm [Blondel08]. It has been proposed for single-cell analysis by [Levine15].

This requires having ran neighbors() or bbknn() first.

Parameters

adata : AnnDataAnnData

The annotated data matrix.

resolution : floatfloat (default: 1)

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 : None | int | RandomStateUnion[None, int, RandomState] (default: 0)

Change the initialization of the optimization.

restrict_to : Tuple[str, Sequence[str]] | NoneOptional[Tuple[str, Sequence[str]]] (default: None)

Restrict the clustering to the categories within the key for sample annotation, tuple needs to contain (obs_key, list_of_categories).

key_added : strstr (default: 'leiden')

adata.obs key under which to add the cluster labels.

adjacency : spmatrix | NoneOptional[spmatrix] (default: None)

Sparse adjacency matrix of the graph, defaults to neighbors connectivities.

directed : boolbool (default: True)

Whether to treat the graph as directed or undirected.

use_weights : boolbool (default: True)

If True, edge weights from the graph are used in the computation (placing more emphasis on stronger edges).

n_iterations : intint (default: -1)

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[MutableVertexPartition] | NoneOptional[Type[MutableVertexPartition]] (default: None)

Type of partition to use. Defaults to RBConfigurationVertexPartition. For the available options, consult the documentation for find_partition().

neighbors_key : str | NoneOptional[str] (default: None)

Use neighbors connectivities as adjacency. If not specified, leiden looks .obsp[‘connectivities’] for connectivities (default storage place for pp.neighbors). If specified, leiden looks .obsp[.uns[neighbors_key][‘connectivities_key’]] for connectivities.

obsp : str | NoneOptional[str] (default: None)

Use .obsp[obsp] as adjacency. You can’t specify both obsp and neighbors_key at the same time.

copy : boolbool (default: False)

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).

Return type

AnnData | NoneOptional[AnnData]

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.