cellrank.pl.cluster_lineage

cellrank.pl.cluster_lineage(adata, model, genes, lineage, backward=False, time_range=None, clusters=None, n_points=200, time_key='latent_time', norm=True, recompute=False, callback=None, ncols=3, sharey=False, key=None, random_state=None, use_leiden=False, show_progress_bar=True, n_jobs=1, backend='loky', figsize=None, dpi=None, save=None, pca_kwargs=mappingproxy({'svd_solver': 'arpack'}), neighbors_kwargs=mappingproxy({'use_rep': 'X'}), clustering_kwargs=mappingproxy({}), return_models=False, **kwargs)

Cluster gene expression trends within a lineage and plot the clusters.

This function is based on Palantir, see [Setty19]. It can be used to discover modules of genes that drive development along a given lineage. Consider running this function on a subset of genes which are potential lineage drivers, identified e.g. by running cellrank.tl.lineage_drivers().

Parameters

• adata (anndata.AnnData) – Annotated data object.

• model (Union[BaseModel, Mapping[str, Mapping[str, BaseModel]]]) –

  Model based on cellrank.ul.models.BaseModel to fit.

  If a dict, gene and lineage specific models can be specified. Use '*' to indicate all genes or lineages, for example {'Map2': {'*': ...}, 'Dcx': {'Alpha': ..., '*': ...}}.

• genes (Sequence[str]) – Genes in adata.var_names or in adata.raw.var_names, if use_raw=True.

• lineage (str) – Name of the lineage for which to cluster the genes.

• backward (bool) – Direction of the process.

• time_range (Union[float, Tuple[Optional[float], Optional[float]], None]) –

  Specify start and end times:

  • If a tuple, it specifies the minimum and maximum pseudotime. Both values can be None, in which case the minimum is the earliest pseudotime and the maximum is automatically determined.

  • If a float, it specifies the maximum pseudotime.

  This can also be specified on per-lineage basis.

• clusters (Optional[Sequence[str]]) – Cluster identifiers to plot. If None, all clusters will be considered. Useful when plotting previously computed clusters.

• n_points (int) – Number of points used for prediction.

• time_key (str) – Key in adata.obs where the pseudotime is stored.

• norm (bool) – Whether to z-normalize each trend to have zero mean, unit variance.

• recompute (bool) – If True, recompute the clustering, otherwise try to find already existing one.

• callback (Union[Callable, Mapping[str, Mapping[str, Callable]], None]) – Function which takes a cellrank.ul.models.BaseModel and some keyword arguments for cellrank.ul.models.BaseModel.prepare() and returns the prepared model. Can be specified in gene- and lineage-specific manner, similarly to model.

• ncols (int) – Number of columns for the plot.

• sharey (Union[str, bool]) – Whether to share y-axis across multiple plots.

• key (Optional[str]) – Key in adata.uns where to save the results. If None, it will be saved as lineage_{lineage}_trend .

• random_state (Optional[int]) – Random seed for reproducibility.

• use_leiden (bool) – Whether to use scanpy.tl.leiden() for clustering or scanpy.tl.louvain().

• show_progress_bar (bool) – Whether to show a progress bar. Disabling it may slightly improve performance.

• n_jobs (Optional[int]) – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

• backend (str) – Which backend to use for parallelization. See joblib.Parallel for valid options.

• figsize (Optional[Tuple[float, float]]) – Size of the figure.

• dpi (Optional[int]) – Dots per inch.

• save (Union[Path, str, None]) – Filename where to save the plot.

• pca_kwargs (Dict) – Keyword arguments for scanpy.pp.pca().

• neighbors_kwargs (Dict) – Keyword arguments for scanpy.pp.neighbors().

• clustering_kwargs (Dict) – Keyword arguments for scanpy.tl.louvain() or scanpy.tl.leiden().

• return_models (bool) – If True, return the fitted models for each gene in genes and lineage in lineages.

• kwargs – Keyword arguments for cellrank.ul.models.BaseModel.prepare().

Return type

Optional[Mapping[str, Mapping[str, BaseModel]]]

Returns

• None – If return_models=False, just plots the figure and optionally saves it based on save.

• Dict[str, Dict[str, cellrank.ul.models.BaseModel]] – Otherwise returns the fitted models as {'gene_1': {'lineage_1': <model_11>, ...}, ...}. Models which have failed will be instances of cellrank.ul.models.FailedModel.

  Also updates adata.uns with the following:

  • key or lineage_{lineage}_trend - an anndata.AnnData object of shape (n_genes, n_points) containing the clustered genes.

Examples

Plot cluster lineage