stlearn.tl.clustering.leiden

stlearn.tl.clustering.leiden(adata: AnnData, resolution: float | None = None, random_state: int | RandomState | None = 0, restrict_to: tuple[str, Sequence[str]] | None = None, key_added: str = 'leiden', adjacency: spmatrix | None = None, directed: bool = False, use_weights: bool = False, partition_type: type[MutableVertexPartition] | None = None, obsp: str | None = None, copy: bool = False, flavor: Literal['leidenalg', 'igraph'] = 'igraph', n_iterations: int = 2) → AnnData | None[source]

Wrap function scanpy.tl.leiden

This requires having ran neighbors() or bbknn() first, or explicitly passing a adjacency matrix.

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

  • key_added – Key under which to add the cluster labels. (default: 'leiden')

  • adjacency – Sparse adjacency matrix of the graph, defaults to adata.uns['neighbors']['connectivities'].

  • directed – Interpret the adjacency matrix as directed graph?

  • use_weights – Use weights from knn graph.

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

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

  • copy – Copy adata or modify it inplace.

  • flavor – Which package’s implementation to use.

  • n_iterations – How many iterations of the Leiden clustering algorithm to perform.

Returns:

By default (copy=False), updates adata with the following fields:

  • adata.obs['leiden'] (pandas.Series, dtype category) - Array of dim (number of samples) that stores the subgroup id ('0', '1', …) for each cell.

When copy=True is set, a copy of adata with those fields is returned.

Return type:

None or AnnData