schist.inference._flat_model
Module Contents
Functions
|
Cluster cells into subgroups [Peixoto14]. |
- schist.inference._flat_model.flat_model(adata: anndata.AnnData, n_sweep: int = 10, beta: float = np.inf, tolerance: float = 1e-06, collect_marginals: bool = True, deg_corr: bool = True, n_init: int = 100, n_jobs: int = -1, refine_model: bool = False, refine_iter: int = 100, max_iter: int = 100000, *, restrict_to: Tuple[str, Sequence[str]] | None = None, random_seed: int | None = None, key_added: str = 'sbm', adjacency: scipy.sparse.spmatrix | None = None, neighbors_key: str | None = 'neighbors', directed: bool = False, use_weights: bool = False, save_model: str | None = None, copy: bool = False, dispatch_backend: str | None = 'threads') anndata.AnnData | None
Cluster cells into subgroups [Peixoto14].
Cluster cells using the Stochastic Block Model [Peixoto14], performing Bayesian inference on node groups.
This requires having ran
neighbors()orbbknn()first.Parameters
- adata
The annotated data matrix.
- n_sweep
Number of MCMC sweeps to get the initial guess
- beta
Inverse temperature for the initial MCMC sweep
- tolerance
Difference in description length to stop MCMC sweep iterations
- collect_marginals
Whether or not collect node probability of belonging to a specific partition.
- deg_corr
Whether to use degree correction in the minimization step. In many real world networks this is the case, although this doesn’t seem the case for KNN graphs used in scanpy.
- n_init
Number of initial minimizations to be performed. This influences also the precision for marginals
- refine_model
Wether to perform a further mcmc step to refine the model
- refine_iter
Number of refinement iterations.
- max_iter
Maximum number of iterations during minimization, set to infinite to stop minimization only on tolerance
- key_added
adata.obs key under which to add the cluster labels.
- adjacency
Sparse adjacency matrix of the graph, defaults to adata.uns[‘neighbors’][‘connectivities’] in case of scanpy<=1.4.6 or adata.obsp[neighbors_key][connectivity_key] for scanpy>1.4.6
- neighbors_key
The key passed to sc.pp.neighbors
- 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). Note that this increases computation times
- save_model
If provided, this will be the filename for the PartitionModeState to be saved
- copy
Whether to copy adata or modify it inplace.
- random_seed
Random number to be used as seed for graph-tool
- n_jobs
Number of parallel computations used during model initialization
Returns
- adata.obs[key_added]
Array of dim (number of cells) that stores the subgroup id (‘0’, ‘1’, …) for each cell.
- adata.uns[‘schist’][‘params’]
A dict with the values for the parameters resolution, random_state, and n_iterations.
- adata.uns[‘schist’][‘stats’]
A dict with the values returned by mcmc_sweep
- adata.obsm[‘CM_sbm’]
A np.ndarray with cell probability of belonging to a specific group
- adata.uns[‘schist’][‘state’]
The BlockModel state object