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 (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 (float | tuple[float | None, float | None] | 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 (Sequence[str] | None) – 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 (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 (str | None) – 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 (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 (str | bool) – Whether to share y-axis across multiple plots.

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

• random_state (int | None) – Random seed for reproducibility.

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

• n_jobs (int | None) – 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 (tuple[float, float] | None) – Size of the figure.

• dpi (int | None) – Dots per inch.

• save (str | Path | 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:

Mapping[str, Mapping[str, BaseModel]] | None

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. The returned models can be passed back as model to reuse the already computed gene trends instead of recomputing them. Also updates adata.uns with the following:

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