stlearn.tl.clustering.louvain

stlearn.tl.clustering.louvain(adata: AnnData, resolution: float | None = None, random_state: int | RandomState | None = 0, restrict_to: tuple[str, Sequence[str]] | None = None, key_added: str = 'louvain', adjacency: spmatrix | None = None, flavor: Literal['vtraag', 'igraph', 'rapids'] = 'vtraag', directed: bool = True, use_weights: bool = False, partition_type: type[MutableVertexPartition] | None = None, partition_kwargs: Mapping[str, Any] = mappingproxy({}), copy: bool = False) → AnnData | None[source]

Wrap function scanpy.tl.louvain Cluster cells into subgroups [Blondel08] [Levine15] [Traag17]. Cluster cells using the Louvain algorithm [Blondel08] in the implementation of [Traag17]. The Louvain algorithm has been proposed for single-cell analysis by [Levine15]. This requires having ran neighbors() or bbknn() first, or explicitly passing a adjacency matrix. :param adata: The annotated data matrix. :param resolution: For the default flavor ('vtraag'), you can provide a resolution

(higher resolution means finding more and smaller clusters), which defaults to 1.0. See “Time as a resolution parameter” in [Lambiotte09].

Parameters:

  • 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: 'louvain')

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

  • flavor – Choose between to packages for computing the cluster. 'vtraag' is much more powerful, and the default.

  • directed – Interpret the adjacency matrix as directed graph?

  • use_weights – Use weights from knn graph.

  • partition_type – Type of partition to use. Only a valid argument if flavor is 'vtraag'.

  • partition_kwargs – Key word arguments to pass to partitioning, if vtraag method is being used.

  • copy – Copy adata or modify it inplace.

Returns:

  • None – By default (copy=False), updates adata with the following fields: adata.obs['louvain'] (pandas.Series, dtype category)

    Array of dim (number of samples) that stores the subgroup id ('0', '1', …) for each cell.

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