dynamo.tl.leiden

dynamo.tl.leiden(adata, resolution=1.0, use_weight=False, weights=None, initial_membership=None, adj_matrix=None, adj_matrix_key=None, seed=None, result_key=None, layer=None, obsm_key=None, selected_cluster_subset=None, selected_cell_subset=None, directed=True, copy=False, **kwargs)[source]

Apply leiden clustering to the input adata.

For other general community detection related parameters, please refer to dynamo's cluster_community() function.

The Leiden algorithm is an improvement of the Louvain algorithm. Based on the cdlib package, the Leiden algorithm consists of three phases: (1) local moving of nodes, (2) refinement of the partition, (3) aggregation of the network based on the refined partition, using the non-refined partition to create an initial partition for the aggregate network.

Please note that since 2/23/23, we have replaced the integrated louvain method from cdlib package with that from the original leidenalg package.

Parameters:

  • adata (AnnData) – an adata object

  • resolution (float) – the resolution of the clustering that determines the level of detail in the clustering process. An increase in this value will result in the generation of a greater number of clusters.

  • use_weight (bool) – whether to use the weight of the edges in the clustering process. Default False.

  • weights (Union[str, Iterable, None]) – weights of edges. Can be either an iterable (list of double) or an edge attribute.

  • initial_membership (Optional[List[int]]) – list of int. Initial membership for the partition. If None then defaults to a singleton partition.

  • adj_matrix (Optional[csr_matrix]) – the adjacency matrix to use for the cluster_community function.

  • adj_matrix_key (Optional[str]) – the key of the adjacency matrix in adata.obsp used for the cluster_community function.

  • seed (Optional[int]) – seed for the random number generator. By default uses a random seed if nothing is specified.

  • result_key (Optional[str]) – the key to use for saving clustering results which will be included in both adata.obs and adata.uns.

  • layer (Optional[str]) – the adata layer where cluster algorithms will work on.

  • obsm_key (Optional[str]) – the key of the obsm that points to the expression embedding to be used for dyn.tl.neighbors to calculate the nearest neighbor graph.

  • selected_cluster_subset (Optional[Tuple[str, List[int]]]) – a tuple of 2 elements (cluster_key, allowed_clusters) filtering cells in adata based on cluster_key in adata.obs and only reserves cells in the allowed clusters.

  • selected_cell_subset (Optional[List[int]]) – a list of cell indices to cluster.

  • directed (bool) – whether the graph is directed.

  • copy (bool) – return a copy instead of writing to adata.

  • **kwargs – additional arguments to pass to the cluster_community function.

Returns:

An updated AnnData object with the leiden clustering results added. The adata is updated up with the result_key key to use for saving clustering results which will be included in both adata.obs and adata.uns. adata.obs[result_key] saves the clustering identify of each cell where the adata.uns[result_key] saves the relevant parameters for the leiden clustering .

Return type:

adata