cellrank.pl.cluster_trends

cellrank.pl.cluster_trends(adata, model, genes, lineage, time_key, backward=False, time_range=None, clusters=None, n_points=200, covariate_key=None, ratio=0.05, cmap='viridis', norm=True, recompute=False, callback=None, ncols=3, sharey=False, key=None, random_state=None, 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 and plot gene expression trends within a lineage.

See also

• See Visualizing and Clustering Gene Expression Trends on how to visualize the gene trends.

This function is based on Palantir [Setty et al., 2019]. 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.

Parameters

• adata (AnnData) – Annotated data object.

• model (Union[BaseModel, Mapping[str, Mapping[str, BaseModel]]]) – Model based on BaseModel to fit. If a dict, gene and lineage specific models can be specified. Use '*' to indicate all genes or lineages, for example {'gene_1': {'*': ...}, 'gene_2': {'lineage_1': ..., '*': ...}}.

• genes (Sequence[str]) – Genes in var_names.

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

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

• backward (bool) – Direction of the process.

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

  Specify start and end times:

  • 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.

  • float - it specifies the maximum pseudotime.

• 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.

• covariate_key (Union[str, Sequence[str], None]) – Keys in obs containing observations to be plotted at the bottom of each plot.

• gene_symbols – Key in var to use instead of var_names.

• ratio (float) – Height ratio of each covariate in covariate_key.

• cmap (Optional[str]) – Colormap to use for continuous covariates in covariate_key.

• 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 BaseModel and some keyword arguments for prepare() and returns the prepared model. Can be specified in gene- and lineage-specific manner, similarly to the 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 uns where to save the results. If None, it will be saved as 'lineage_{lineage}_trend' .

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

• 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 (Literal['loky', 'multiprocessing', 'threading']) – Which backend to use for parallelization. See 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 pca().

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

• clustering_kwargs (Dict) – Keyword arguments for leiden().

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

• kwargs (Any) – Keyword arguments for prepare().

Return type

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

Returns

: If return_models = False, just plots the figure and optionally saves it based on save. Otherwise returns the fitted models as {'gene_1': {'lineage_1': <model_11>, ...}, ...}. Models which have failed will be instances of cellrank.models.FailedModel. Also updates adata.uns with the following:

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