schist.inference
Package Contents
Functions
|
Cluster cells into subgroups [Peixoto14]. |
|
Cluster cells into subgroups [Peixoto14]. |
|
Cluster cells into subgroups [Peixoto14]. |
|
Cluster cells into subgroups [Traag18]. |
|
Cluster cells into subgroups using multiple modalities. |
- schist.inference.nested_model(adata: anndata.AnnData, deg_corr: bool = True, tolerance: float = 1e-06, n_sweep: int = 10, beta: float = np.inf, n_init: int = 100, collect_marginals: bool = True, 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 = 'nsbm', 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 nested Stochastic Block Model [Peixoto14], a hierarchical version of Stochastic Block Model [Holland83], performing Bayesian inference on node groups. NSBM should circumvent classical limitations of SBM in detecting small groups in large graphs replacing the noninformative priors used by a hierarchy of priors and hyperpriors.
This requires having ran
neighbors()orbbknn()first.Parameters
- adata
The annotated data matrix.
- 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.
- tolerance
Tolerance for fast model convergence.
- n_sweep
Number of iterations to be performed in the fast model MCMC greedy approach
- beta
Inverse temperature for MCMC greedy approach
- n_init
Number of initial minimizations to be performed. The one with smaller entropy is chosen
- 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
- n_jobs
Number of parallel computations used during model initialization
- 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
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[‘CA_nsbm_level_{n}’]
A np.ndarray with cell probability of belonging to a specific group
- adata.uns[‘schist’][‘state’]
The NestedBlockModel state object
- schist.inference.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
- schist.inference.planted_model(adata: anndata.AnnData, n_sweep: int = 10, beta: float = np.inf, tolerance=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 = 'ppbm', adjacency: scipy.sparse.spmatrix | None = None, neighbors_key: str | None = 'neighbors', directed: bool = False, use_weights: bool = False, copy: bool = False, save_model: str | None = None, dispatch_backend: str | None = 'threads') anndata.AnnData | None
Cluster cells into subgroups [Peixoto14].
Cluster cells using the Planted Partition Block Model [Peixoto14], performing Bayesian inference on node groups. This function, in particular, uses the Planted Block Model, which is particularly suitable in case of assortative graphs and it returns the optimal number of communities
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
- copy
Whether to copy adata or modify it inplace.
- save_model
If provided, this will be the filename for the PartitionModeState to be saved
- 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_ppbm’]
A np.ndarray with cell probability of belonging to a specific group
- adata.uns[‘schist’][‘state’]
The BlockModel state object
- schist.inference.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.
- schist.inference.nested_model_multi(adatas: List[anndata.AnnData], deg_corr: bool = True, tolerance: float = 1e-06, n_sweep: int = 10, beta: float = np.inf, n_init: int = 100, collect_marginals: bool = True, n_jobs: int = -1, refine_model: bool = False, refine_iter: int = 100, overlap: bool = False, max_iter: int = 100000, *, random_seed: int | None = None, key_added: str = 'multi_nsbm', adjacency: List[scipy.sparse.spmatrix] | None = None, neighbors_key: List[str] | None = ['neighbors'], directed: bool = False, use_weights: bool = False, save_model: str | None = None, copy: bool = False, dispatch_backend: str | None = 'threads') List[anndata.AnnData] | None
Cluster cells into subgroups using multiple modalities.
Cluster cells using the nested Stochastic Block Model [Peixoto14], performing Bayesian inference on node groups. This function takes multiple experiments, possibly across different modalities, and perform joint clustering.
This requires having ran
neighbors()orbbknn()first. It also requires cells having the same names if coming from paired experimentsParameters
- adatas
A list of processed AnnData. Neighbors must have been already calculated.
- 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.
- tolerance
Tolerance for fast model convergence.
- n_sweep
Number of iterations to be performed in the fast model MCMC greedy approach
- beta
Inverse temperature for MCMC greedy approach
- n_init
Number of initial minimizations to be performed. The one with smaller entropy is chosen
- 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
- overlap
Whether the different layers are dependent (overlap=True) or not (overlap=False)
- n_jobs
Number of parallel computations used during model initialization
- 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. If all AnnData share the same key, one only has to be specified, otherwise the full tuple of all keys must be provided
- 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
Returns
- adata.obs[key_added]
Array of dim (number of cells) that stores the subgroup id (‘0’, ‘1’, …) for each cell.
- adata.uns[‘schist’][‘multi_level_params’]
A dict with the values for the parameters resolution, random_state, and n_iterations.
- adata.uns[‘schist’][‘multi_level_stats’]
A dict with the values returned by mcmc_sweep
- adata.obsm[‘CA_multi_nsbm_level_{n}’]
A np.ndarray with cell probability of belonging to a specific group
- adata.uns[‘schist’][‘multi_level_state’]
The NestedBlockModel state object