cellrank.pl.heatmap#

cellrank.pl.heatmap(adata, model, genes, lineages=None, backward=False, mode=HeatmapMode.LINEAGES, time_key='latent_time', time_range=None, callback=None, cluster_key=None, show_absorption_probabilities=False, cluster_genes=False, keep_gene_order=False, scale=True, n_convolve=5, show_all_genes=False, cbar=True, lineage_height=0.33, fontsize=None, xlabel=None, cmap=<matplotlib.colors.ListedColormap object>, dendrogram=True, return_genes=False, return_models=False, return_figure=False, n_jobs=1, backend='loky', show_progress_bar=True, figsize=None, dpi=None, save=None, **kwargs)[source]#

Plot a heatmap of smoothed gene expression along specified lineages.

This requires a pseudotemporal ordering of cellular dynamics, computed using a method like DPT [Haghverdi et al., 2016] or Palantir [Setty et al., 2019]. The function combines the pseudotemporal ordering with CellRank’s fate probabilities to visualize each gene’s expression along specific trajectories. In the heatmap, genes are ordered according to their peak in pseudotime, which emphasizes expression cascades of sequential activation.

Parameters:

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

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

    Model based on cellrank.models.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 anndata.AnnData.var_names or in anndata.AnnData.raw.var_names, if use_raw = True.

  • lineages (Union[str, Sequence[str], None]) – Names of the lineages for which to plot. If None, plot all lineages.

  • backward (bool) – Direction of the process.

  • mode (Literal['genes', 'lineages']) –

    Valid options are:

    • ’lineages’ - group by genes for each lineage in lineages.

    • ’genes’ - group by lineages for each gene in genes.

  • time_key (str) – Key in attr:anndata.AnnData.obs where the pseudotime is stored.

  • time_range (Union[float, Tuple[Optional[float], Optional[float]], None, List[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.

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

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

  • cluster_key (Union[str, Sequence[str], None]) – Key(s) in anndata.AnnData.obs containing categorical observations to be plotted on the top of heatmap. Only available when mode = 'lineages'.

  • show_absorption_probabilities (bool) – Whether to also plot absorption probabilities alongside the smoothed expression. Only available when mode = 'lineages'.

  • cluster_genes (bool) – Whether to cluster genes using seaborn.clustermap() when mode = 'lineages'.

  • keep_gene_order (bool) – Whether to keep the gene order for later lineages after the first was sorted. Only available when cluster_genes = False and mode = 'lineages'.

  • scale (bool) – Whether to normalize the gene expression [0, 1] range.

  • n_convolve (Optional[int]) – Size of the convolution window when smoothing absorption probabilities.

  • show_all_genes (bool) – Whether to show all genes on y-axis.

  • cbar (bool) – Whether to show the colorbar.

  • lineage_height (float) – Height of a bar when mode = 'genes'.

  • fontsize (Optional[float]) – Size of the title’s font.

  • xlabel (Optional[str]) – Label on the x-axis. If None, it is determined based on time_key.

  • cmap (ListedColormap) – Colormap to use when visualizing the smoothed expression.

  • dendrogram (bool) – Whether to show dendrogram when cluster_genes = True.

  • return_genes (bool) – Whether to return the sorted or clustered genes. Only available when mode = 'lineages'.

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

  • return_figure (bool) – Whether to return the figure object. Sets return_genes = True

  • 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 joblib.Parallel for valid options.

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

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

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

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

Return type:

Union[Dict[str, DataFrame], Tuple[Mapping[str, Mapping[str, BaseModel]], Dict[str, DataFrame]], None]

Returns:

: None

If return_models = False, just plots the figure and optionally saves it based on save.

Dict[str, Dict[str, cellrank.models.BaseModel]]

Otherwise returns the fitted models as {'gene_1': {'lineage_1': <model_11>, ...}, ...}. Models which have failed will be instances of cellrank.models.FailedModel.

If return_genes = True and mode = 'lineages', returns pandas.DataFrame containing the clustered or sorted genes.

If return_figure = True, returns a tuple containing the figure and genes.