stlearn.pl.deconvolution_plot

stlearn.pl.deconvolution_plot(adata: ~anndata._core.anndata.AnnData, library_id: str = None, use_label: str = 'louvain', cluster: [<class 'int'>, <class 'str'>] = None, celltype: str = None, celltype_threshold: float = 0, data_alpha: float = 1.0, threshold: float = 0.0, cmap: str = 'tab20', colors: list = None, tissue_alpha: float = 1.0, title: str = None, spot_size: ~typing.Union[float, int] = 10, show_axis: bool = False, show_legend: bool = True, show_donut: bool = True, cropped: bool = True, margin: int = 100, name: str = None, dpi: int = 150, output: str = None, copy: bool = False, figsize: tuple = (6.4, 4.8), show=True) → Optional[AnnData][source]

Clustering plot for sptial transcriptomics data. Also it has a function to display trajectory inference.

Parameters:

  • adata – Annotated data matrix.

  • library_id – Library id stored in AnnData.

  • use_label – Use label result of cluster method.

  • list_cluster – Choose set of clusters that will display in the plot.

  • data_alpha – Opacity of the spot.

  • tissue_alpha – Opacity of the tissue.

  • cmap – Color map to use.

  • spot_size – Size of the spot.

  • show_axis – Show axis or not.

  • show_legend – Show legend or not.

  • show_donut – Whether to show the donut plot or not.

  • show_trajectory – Show the spatial trajectory or not. It requires stlearn.spatial.trajectory.pseudotimespace.

  • show_subcluster – Show subcluster or not. It requires stlearn.spatial.trajectory.global_level.

  • name – Name of the output figure file.

  • dpi – DPI of the output figure.

  • output – Save the figure as file or not.

  • copy – Return a copy instead of writing to adata.

Return type:

Nothing