squidpy.pl.spatial_segment

squidpy.pl.spatial_segment(adata, *, seg_cell_id, seg=True, seg_key='segmentation', seg_contourpx=None, seg_outline=False, **kwargs)[source]

Plot spatial omics data with segmentation masks on top.

Argument seg_cell_id in anndata.AnnData.obs controls unique segmentation mask’s ids to be plotted. By default, 'segmentation', seg_key for the segmentation and 'hires' for the image is attempted.

  • Use seg_key to display the image in the background.

  • Use seg_contourpx or seg_outline to control how the segmentation mask is displayed.

Use library_id to select the image. If multiple library_id are available, use library_key in anndata.AnnData.obs to plot the subsets. Use crop_coord to crop the spatial plot based on coordinate boundaries.

This function has few key assumptions about how coordinates and libraries are handled:

  • The arguments library_key and library_id control which dataset is plotted. If multiple libraries are present, specifying solely library_key will suffice, and all unique libraries will be plotted sequentially. To select specific libraries, use the library_id argument.

  • The argument color controls which features in obs/var are plotted. They are plotted for all available/specified libraries. The argument groups can be used to select categories to be plotted. This is valid only for categorical features in anndata.AnnData.obs.

  • If multiple library_id are available, arguments such as size and crop_coord accept lists to selectively customize different library_id plots. This requires that the length of such lists matches the number of unique libraries in the dataset.

  • Coordinates are in the pixel space of the source image, so an equal aspect ratio is assumed.

  • The origin (0, 0) is on the top left, as is common convention with image data.

  • The plotted points (dots) do not have a real “size” but it is relative to their coordinate/pixel space. This does not hold if no image is plotted, then the size corresponds to points size passed to matplotlib.axes.Axes.scatter().

If anndata.AnnData.uns ['spatial'] is present, use img_key, seg_key and size_key arguments to find values for img, seg and size. Alternatively, these values can be passed directly via img.

See also

Parameters:

  • adata (AnnData) – Annotated data object.

  • seg_cell_id (str) – Column in anndata.AnnData.obs with unique segmentation mask ids. Required to filter valid segmentation masks.

  • seg (bool | NDArray[Any] | Sequence[NDArray[Any]] | None) – Whether to plot the segmentation mask. One (or more) numpy.ndarray can also be passed for plotting.

  • seg_key (str) – Key of segmentation mask in anndata.AnnData.uns.

  • seg_contourpx (int | None) – Draw contour of specified width for each segment. If None, fills entire segment, see skimage.morphology.erosion().

  • seg_outline (bool) – Whether to plot boundaries around segmentation masks.

  • color – Key for annotations in anndata.AnnData.obs or variables/genes.

  • groups – For discrete annotation in color, select which values to plot (other values are set to NAs).

  • library_id – Name of the Z-dimension(s) that this function should be applied to. For not specified Z-dimensions, the identity function is applied.

  • library_key – If multiple library_id, column in anndata.AnnData.obs which stores mapping between library_id and obs.

  • spatial_key – Key in anndata.AnnData.obsm where spatial coordinates are stored.

  • img – Whether to plot the image. One (or more) numpy.ndarray can also be passed for plotting.

  • img_res_key – Key for image resolution, used to get img and scale_factor from 'images' and 'scalefactors' entries for this library.

  • img_alpha – Alpha value for the underlying image.

  • image_cmap – Colormap for the image, see matplotlib.colors.Colormap.

  • img_channel – To select which channel to plot (all by default).

  • use_raw – If True, use anndata.AnnData.raw.

  • layer – Key in anndata.AnnData.layers or None for anndata.AnnData.X.

  • alt_var – Which column to use in anndata.AnnData.var to select alternative var_name.

  • size – Size of the scatter point/shape. In case of spatial_shape it represents to the scaling factor for shape (accessed via size_key). In case of spatial_point, it represents the size argument in matplotlib.pyplot.scatter().

  • size_key – Key of of pixel size of shapes to be plotted, stored in anndata.AnnData.uns. Only needed for spatial_shape.

  • scale_factor – Scaling factor used to map from coordinate space to pixel space. Found by default if library_id and img_key can be resolved. Otherwise, defaults to 1.

  • crop_coord – Coordinates to use for cropping the image (left, right, top, bottom). These coordinates are expected to be in pixel space (same as spatial) and will be transformed by scale_factor. If not provided, image is automatically cropped to bounds of spatial, plus a border.

  • cmap – Colormap for continuous annotations, see matplotlib.colors.Colormap.

  • palette – Palette for discrete annotations, see matplotlib.colors.Colormap.

  • alpha – Alpha value for scatter point/shape.

  • norm – Colormap normalization for continuous annotations, see matplotlib.colors.Normalize.

  • na_color – Color to be used for NAs values, if present.

  • connectivity_key – Key for neighbors graph to plot. Default is: anndata.AnnData.obsp ['spatial_connectivities'].

  • edges_width – Width of the edges. Only used when connectivity_key != None .

  • edges_color – Color of the edges.

  • library_first – If multiple libraries are plotted, set the plotting order with respect to color.

  • frameon – If True, draw a frame around the panels.

  • wspace – Width space between panels.

  • hspace – Height space between panels.

  • ncols – Number of panels per row.

  • outline – If True, a thin border around points/shapes is plotted.

  • outline_color – Color of the border.

  • outline_width – Width of the border.

  • legend_loc – Location of the legend, see matplotlib.legend.Legend.

  • legend_fontsize – Font size of the legend, see matplotlib.text.Text.set_fontsize().

  • legend_fontweight – Font weight of the legend, see matplotlib.text.Text.set_fontweight().

  • legend_fontoutline – Font outline of the legend, see matplotlib.patheffects.withStroke.

  • legend_na – Whether to show NA values in the legend.

  • colorbar – Whether to show the colorbar, see matplotlib.pyplot.colorbar().

  • scalebar_dx – Size of one pixel in units specified by scalebar_units.

  • scalebar_units – Units of scalebar_dx.

  • title – Panel titles.

  • axis_label – Panel axis labels.

  • fig – Optional matplotlib.figure.Figure to use.

  • ax – Optional matplotlib.axes.Axes to use.

  • return_ax – Whether to return matplotlib.axes.Axes object(s).

  • figsize – Size of the figure in inches.

  • dpi – Dots per inch.

  • save – Whether to save the plot.

  • scalebar_kwargs – Keyword arguments for matplotlib_scalebar.ScaleBar.

  • edges_kwargs – Keyword arguments for networkx.drawing.nx_pylab.draw_networkx_edges() [Hagberg et al., 2008].

  • kwargs (Any) – Keyword arguments for matplotlib.pyplot.scatter() or matplotlib.pyplot.imshow().

Return type:

Axes | Sequence[Axes] | None

Returns:

Nothing, just plots the figure and optionally saves the plot.