scanpy.tl.leiden#
- scanpy.tl.leiden(adata, resolution=1, *, restrict_to=None, random_state=0, key_added='leiden', adjacency=None, directed=None, use_weights=True, n_iterations=-1, partition_type=None, neighbors_key=None, obsp=None, copy=False, flavor='leidenalg', **clustering_args)[source]#
Cluster cells into subgroups [Traag et al., 2019].
Cluster cells using the Leiden algorithm [Traag et al., 2019], an improved version of the Louvain algorithm [Blondel et al., 2008]. It has been proposed for single-cell analysis by Levine et al. [2015].
This requires having ran
neighbors()
orbbknn()
first.- Parameters:
- adata
AnnData
The annotated data matrix.
- resolution
float
(default:1
) A parameter value controlling the coarseness of the clustering. Higher values lead to more clusters. Set to
None
if overridingpartition_type
to one that doesn’t accept aresolution_parameter
.- random_state
Union
[int
,RandomState
,None
] (default:0
) Change the initialization of the optimization.
- restrict_to
tuple
[str
,Sequence
[str
]] |None
(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
str
(default:'leiden'
) adata.obs
key under which to add the cluster labels.- adjacency
spmatrix
|None
(default:None
) Sparse adjacency matrix of the graph, defaults to neighbors connectivities.
- directed
bool
|None
(default:None
) Whether to treat the graph as directed or undirected.
- use_weights
bool
(default:True
) If
True
, edge weights from the graph are used in the computation (placing more emphasis on stronger edges).- n_iterations
int
(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. 2 is faster and the default for underlying packages.
- partition_type
type
[MutableVertexPartition
] |None
(default:None
) Type of partition to use. Defaults to
RBConfigurationVertexPartition
. For the available options, consult the documentation forfind_partition()
.- neighbors_key
str
|None
(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
|None
(default:None
) Use .obsp[obsp] as adjacency. You can’t specify both
obsp
andneighbors_key
at the same time.- copy
bool
(default:False
) Whether to copy
adata
or modify it inplace.- flavor
Literal
['leidenalg'
,'igraph'
] (default:'leidenalg'
) Which package’s implementation to use.
- **clustering_args
Any further arguments to pass to
find_partition()
(which in turn passes arguments to thepartition_type
) origraph.Graph.community_leiden()
fromigraph
.
- adata
- Return type:
- Returns:
Returns
None
ifcopy=False
, else returns anAnnData
object. Sets the following fields:adata.obs['leiden' | key_added]
pandas.Series
(dtypecategory
)Array of dim (number of samples) that stores the subgroup id (
'0'
,'1'
, …) for each cell.adata.uns['leiden' | key_added]['params']
dict
A dict with the values for the parameters
resolution
,random_state
, andn_iterations
.