ehrapy.tools.leiden

ehrapy.tools.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)[source]

Cluster observations into subgroups [Traag18].

Cluster observations 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 (AnnData) – AnnData object object containing all observations.

  • resolution (float) – 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.

  • restrict_to (Optional[tuple[str, Sequence[str]]]) – Restrict the clustering to the categories within the key for sample annotation, tuple needs to contain (obs_key, list_of_categories).

  • random_state (Union[int, RandomState, None]) – Random seed of the initialization of the optimization.

  • key_added (str) – adata.obs key under which to add the cluster labels.

  • adjacency (Optional[spmatrix]) – Sparse adjacency matrix of the graph, defaults to neighbors connectivities.

  • directed (bool) – Whether to treat the graph as directed or undirected.

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

  • n_iterations (int) – 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 (Optional[type[MutableVertexPartition]]) – Type of partition to use. Defaults to RBConfigurationVertexPartition. For the available options, consult the documentation for find_partition().

  • neighbors_key (Optional[str]) – 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 (Optional[str]) – Use .obsp[obsp] as adjacency. You can’t specify both obsp and neighbors_key at the same time.

  • copy (bool) – 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:

Optional[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.