squidpy.pl.spatial_segment

squidpy.pl.spatial_segment(adata, color=None, groups=None, library_id=None, library_key=None, spatial_key='spatial', img=True, img_res_key='hires', img_alpha=None, img_cmap=None, img_channel=None, seg=None, seg_key='segmentation', seg_cell_id=None, seg_contourpx=None, seg_outline=False, use_raw=None, layer=None, alt_var=None, crop_coord=None, cmap=None, palette=None, alpha=1.0, norm=None, na_color=(0, 0, 0, 0), connectivity_key=None, edges_width=1.0, edges_color='grey', library_first=True, frameon=None, wspace=None, hspace=0.25, ncols=4, outline=False, outline_color=('black', 'white'), outline_width=(0.3, 0.05), legend_loc='right margin', legend_fontsize=None, legend_fontweight='bold', legend_fontoutline=None, legend_na=True, colorbar=True, scalebar_dx=None, scalebar_units=None, title=None, axis_label=None, fig=None, ax=None, return_ax=False, figsize=None, dpi=None, save=None, scalebar_kwargs=mappingproxy({}), edges_kwargs=mappingproxy({}), **kwargs)

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

• squidpy.pl.spatial_scatter() on how to plot spatial data with overlayed data on top.

Parameters:

• adata (AnnData) – Annotated data object.

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

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

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

• seg_contourpx (Optional[int]) – 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 (Union[str, Sequence[Optional[str]], None]) – Key for annotations in anndata.AnnData.obs or variables/genes.

• groups (Union[str, Sequence[str], None]) – For discrete annotation in color, select which values to plot (other values are set to NAs).

• library_id (Union[str, Sequence[str], None]) – 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 (Optional[str]) – If multiple library_id, column in anndata.AnnData.obs which stores mapping between library_id and obs.

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

• img (Union[bool, ndarray[Any, dtype[Any]], Sequence[ndarray[Any, dtype[Any]]], None]) – Whether to plot the image. One (or more) numpy.ndarray can also be passed for plotting.

• img_res_key (Optional[str]) – Key for image resolution, used to get img and scale_factor from 'images' and 'scalefactors' entries for this library.

• img_alpha (Optional[float]) – Alpha value for the underlying image.

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

• img_channel (UnionType[int, list[int], None]) – To select which channel to plot (all by default).

• use_raw (Optional[bool]) – If True, use anndata.AnnData.raw.

• layer (Optional[str]) – Key in anndata.AnnData.layers or None for anndata.AnnData.X.

• alt_var (Optional[str]) – 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 (Union[Tuple[int, int, int, int], Sequence[Tuple[int, int, int, int]], None]) – 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 (UnionType[str, Colormap, None]) – Colormap for continuous annotations, see matplotlib.colors.Colormap.

• palette (Union[str, ListedColormap, None]) – Palette for discrete annotations, see matplotlib.colors.Colormap.

• alpha (float) – Alpha value for scatter point/shape.

• norm (Union[Normalize, Sequence[Normalize], None]) – Colormap normalization for continuous annotations, see matplotlib.colors.Normalize.

• na_color (str | tuple[float, ...]) – Color to be used for NAs values, if present.

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

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

• edges_color (Union[str, Sequence[str], Sequence[float]]) – Color of the edges.

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

• frameon (Optional[bool]) – If True, draw a frame around the panels.

• wspace (Optional[float]) – Width space between panels.

• hspace (float) – Height space between panels.

• ncols (int) – Number of panels per row.

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

• outline_color (tuple[str, str]) – Color of the border.

• outline_width (tuple[float, float]) – Width of the border.

• legend_loc (Optional[str]) – Location of the legend, see matplotlib.legend.Legend.

• legend_fontsize (Union[int, float, Literal['xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'], None]) – Font size of the legend, see matplotlib.text.Text.set_fontsize().

• legend_fontweight (Union[int, Literal['light', 'normal', 'medium', 'semibold', 'bold', 'heavy', 'black']]) – Font weight of the legend, see matplotlib.text.Text.set_fontweight().

• legend_fontoutline (Optional[int]) – Font outline of the legend, see matplotlib.patheffects.withStroke.

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

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

• scalebar_dx (Union[float, Sequence[float], None]) – Size of one pixel in units specified by scalebar_units.

• scalebar_units (Union[str, Sequence[str], None]) – Units of scalebar_dx.

• title (Union[str, Sequence[str], None]) – Panel titles.

• axis_label (Union[str, Sequence[str], None]) – Panel axis labels.

• fig (Optional[Figure]) – Optional matplotlib.figure.Figure to use.

• ax (Union[Axes, Sequence[Axes], None]) – Optional matplotlib.axes.Axes to use.

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

• figsize (Optional[tuple[float, float]]) – Size of the figure in inches.

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

• save (UnionType[str, Path, None]) – Whether to save the plot.

• scalebar_kwargs (Mapping[str, Any]) – Keyword arguments for matplotlib_scalebar.ScaleBar().

• edges_kwargs (Mapping[str, Any]) – Keyword arguments for networkx.draw_networkx_edges().

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

Return type:

Union[Axes, Sequence[Axes], None]

Returns:

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