schist.inference._pmleiden
Module Contents
Classes
Functions
|
Cluster cells into subgroups [Traag18]. |
- class schist.inference._pmleiden.MutableVertexPartition
- schist.inference._pmleiden.leiden(adata: anndata.AnnData, resolution: float = 1, n_init: int = 100, *, restrict_to: Tuple[str, Sequence[str]] | None = None, random_state: scanpy._utils.AnyRandom = 0, key_added: str = 'leiden', adjacency: scipy.sparse.spmatrix | None = None, directed: bool = True, use_weights: bool = True, n_iterations: int = -1, partition_type: Type[leidenalg.VertexPartition.MutableVertexPartition] | None = None, neighbors_key: str | None = None, obsp: str | None = None, collect_marginals: bool = True, n_jobs: int = -1, copy: bool = False, save_model: str | None = None, dispatch_backend: str | None = 'threads', **partition_kwargs) anndata.AnnData | None
Cluster cells into subgroups [Traag18].
Cluster cells 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()orbbknn()first.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.
- n_init
The number of random initializations to take for consensus
- random_state
Change the initialization of the optimization.
- restrict_to
Restrict the clustering to the categories within the key for sample annotation, tuple needs to contain (obs_key, list_of_categories).
- key_added
adata.obs key under which to add the cluster labels.
- adjacency
Sparse adjacency matrix of the graph, defaults to neighbors connectivities.
- directed
Whether to treat the graph as directed or undirected.
- use_weights
If True, edge weights from the graph are used in the computation (placing more emphasis on stronger edges).
- n_iterations
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
Type of partition to use. Defaults to
RBConfigurationVertexPartition. For the available options, consult the documentation forfind_partition().- neighbors_key
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
Use .obsp[obsp] as adjacency. You can’t specify both obsp and neighbors_key at the same time.
- collect_marginals
Wheter to retrieve the marginal probability to belong to a group
- n_jobs
Number of parallel jobs to calculate partitions
- copy
Whether to copy adata or modify it inplace.
- save_model
If provided, this will be the filename for the PartitionModeState to be saved
- **partition_kwargs
Any further arguments to pass to ~leidenalg.find_partition (which in turn passes arguments to the partition_type).
Returns
- adata.obs[key_added]
Array of dim (number of cells) 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.