voronoi

Short Description

sm.pl.voronoi: This function enables the visualization of spatial data through the creation of Voronoi diagrams, offering a distinctive method to explore the spatial distribution of cells or features within a defined area. Users can color these diagrams according to values from any categorical column in their dataset, such as cell type or tissue compartment, particularly good looking for manuscripts.

Key considerations for optimal use of this function include:

• Application Scope: Voronoi diagrams are particularly effective for analyzing small to moderately sized spatial regions, supporting regions with up to 5,000 cells. This constraint ensures both interpretability and performance are maintained, as larger regions can result in complex visualizations that are difficult to interpret and may require extended processing times.

• Performance and Interpretability: While Voronoi diagrams offer insightful visualizations for spatial data, their utility diminishes with increasing dataset size. For regions containing more than 5,000 cells, the generated plots may become cluttered and challenging to interpret, alongside experiencing significant delays in generation time. Users are encouraged to segment larger datasets into smaller, manageable regions or utilize alternative visualization methods suitable for high-density spatial data.

Function

voronoi(adata, color_by=None, colors=None, x_coordinate='X_centroid', y_coordinate='Y_centroid', imageid='imageid', subset=None, x_lim=None, y_lim=None, flip_y=True, voronoi_edge_color='black', voronoi_line_width=0.1, voronoi_alpha=0.5, size_max=np.inf, overlay_points=None, overlay_points_categories=None, overlay_drop_categories=None, overlay_points_colors=None, overlay_point_size=5, overlay_point_alpha=1, overlay_point_shape='.', plot_legend=True, fileName='voronoi.pdf', saveDir=None, legend_size=6, **kwargs)

Parameters:

Name	Type	Description	Default
adata	AnnData	An AnnData object containing the spatial data to be visualized.	required
color_by	str	The name of the column used to color the Voronoi diagram. Typically, this column represents categorical variables such as cell types or tissue compartments.	None
colors	str or Dict	Custom color mapping for the Voronoi diagram. Can be specified as a seaborn color palette name or a dictionary mapping categories to colors.	None
x_coordinate	str	The column name containing the x-coordinates.	'X_centroid'
y_coordinate	str	The column name containing the y-coordinates.	'Y_centroid'
imageid	str	The column name containing identifiers for different images or spatial contexts.	'imageid'
subset	str	Specifies the identifier of a single image to focus the visualization on.	None
x_lim	list	The x-axis limits for the plot as a list of two elements: [xmin, xmax].	None
y_lim	list	The y-axis limits for the plot as a list of two elements: [ymin, ymax].	None
flip_y	bool	If set to True, the y-axis will be flipped. This may be necessary for some datasets where y-coordinates are inverted.	True
voronoi_edge_color	str	The color of the edges of the Voronoi cells.	'black'
voronoi_line_width	float	The line width of the Voronoi cell edges.	0.1
voronoi_alpha	float	The opacity of the Voronoi cells, ranging from 0 (completely transparent) to 1 (completely opaque).	0.5
size_max	float	The maximum size for the Voronoi cells. Can be used to limit the cell size in the visualization.	inf
overlay_points	str	The name of the column to use for overlaying points on the Voronoi diagram.	None
overlay_points_categories	list	Specific categories within the overlay_points column to include in the overlay.	None
overlay_drop_categories	list	Specific categories within the overlay_points column to exclude from the overlay.	None
overlay_points_colors	str or Dict	Custom color mapping for the overlay points. Can be specified as a seaborn color palette name or a dictionary mapping categories to colors.	None
overlay_point_size	float	The size of the overlay points.	5
overlay_point_alpha	float	The opacity of the overlay points, ranging from 0 (completely transparent) to 1 (completely opaque).	1
overlay_point_shape	str	The shape of the overlay points.	'.'
plot_legend	bool	Whether to display a legend for the plot.	True
fileName	str	Name of the file to save the plot. Relevant only if saveDir is not None.	'voronoi.pdf'
saveDir	str	Directory to save the generated plot. If None, the plot is not saved.	None
legend_size	float	The font size of the legend text.	6

Returns:

Name	Type	Description
Plot	matplotlib	Returns a plot.

Example

#E xample 1: Basic Voronoi plot with default settings

sm.pl.voronoi(adata)


# Example 2: Voronoi plot colored by cell type, with customized colors and overlay points for a specific phenotype

sm.pl.voronoi(adata, color_by='cell_type', colors='Set2', overlay_points='phenotype', overlay_points_colors={'phenotype1': 'red', 'phenotype2': 'blue'}, plot_legend=True)


# Example 3: Voronoi plot for a specific image subset, with adjusted alpha and line width, and without flipping the y-axis

sm.pl.voronoi(adata, subset='image_01', voronoi_alpha=0.7, voronoi_line_width=0.5)

Source code in scimap/plotting/voronoi.py

def voronoi(
    adata,
    color_by=None,
    colors=None,
    x_coordinate='X_centroid',
    y_coordinate='Y_centroid',
    imageid='imageid',
    subset=None,
    x_lim=None,
    y_lim=None,
    flip_y=True,
    voronoi_edge_color='black',
    voronoi_line_width=0.1,
    voronoi_alpha=0.5,
    size_max=np.inf,
    overlay_points=None,
    overlay_points_categories=None,
    overlay_drop_categories=None,
    overlay_points_colors=None,
    overlay_point_size=5,
    overlay_point_alpha=1,
    overlay_point_shape=".",
    plot_legend=True,
    fileName='voronoi.pdf',
    saveDir=None,
    legend_size=6,
    **kwargs,
):
    """
    Parameters:
            adata (anndata.AnnData):
                An AnnData object containing the spatial data to be visualized.

            color_by (str, optional):
                The name of the column used to color the Voronoi diagram. Typically, this column represents categorical
                variables such as cell types or tissue compartments.

            colors (str or Dict, optional):
                Custom color mapping for the Voronoi diagram. Can be specified as a seaborn color palette name or a dictionary
                mapping categories to colors.

            x_coordinate (str, optional):
                The column name containing the x-coordinates.

            y_coordinate (str, optional):
                The column name containing the y-coordinates.

            imageid (str, optional):
                The column name containing identifiers for different images or spatial contexts.

            subset (str, optional):
                Specifies the identifier of a single image to focus the visualization on.

            x_lim (list, optional):
                The x-axis limits for the plot as a list of two elements: [xmin, xmax].

            y_lim (list, optional):
                The y-axis limits for the plot as a list of two elements: [ymin, ymax].

            flip_y (bool, optional):
                If set to True, the y-axis will be flipped. This may be necessary for some datasets where y-coordinates are
                inverted.

            voronoi_edge_color (str, optional):
                The color of the edges of the Voronoi cells.

            voronoi_line_width (float, optional):
                The line width of the Voronoi cell edges.

            voronoi_alpha (float, optional):
                The opacity of the Voronoi cells, ranging from 0 (completely transparent) to 1 (completely opaque).

            size_max (float, optional):
                The maximum size for the Voronoi cells. Can be used to limit the cell size in the visualization.

            overlay_points (str, optional):
                The name of the column to use for overlaying points on the Voronoi diagram.

            overlay_points_categories (list, optional):
                Specific categories within the `overlay_points` column to include in the overlay.

            overlay_drop_categories (list, optional):
                Specific categories within the `overlay_points` column to exclude from the overlay.

            overlay_points_colors (str or Dict, optional):
                Custom color mapping for the overlay points. Can be specified as a seaborn color palette name or a dictionary
                mapping categories to colors.

            overlay_point_size (float, optional):
                The size of the overlay points.

            overlay_point_alpha (float, optional):
                The opacity of the overlay points, ranging from 0 (completely transparent) to 1 (completely opaque).

            overlay_point_shape (str, optional):
                The shape of the overlay points.

            plot_legend (bool, optional):
                Whether to display a legend for the plot.

            fileName (str, optional):
                Name of the file to save the plot. Relevant only if `saveDir` is not None.

            saveDir (str, optional):
                Directory to save the generated plot. If None, the plot is not saved.

            legend_size (float, optional):
                The font size of the legend text.

    Returns:
            Plot (matplotlib):
                    Returns a plot.

    Example:
            ```python


            #E xample 1: Basic Voronoi plot with default settings

            sm.pl.voronoi(adata)


            # Example 2: Voronoi plot colored by cell type, with customized colors and overlay points for a specific phenotype

            sm.pl.voronoi(adata, color_by='cell_type', colors='Set2', overlay_points='phenotype', overlay_points_colors={'phenotype1': 'red', 'phenotype2': 'blue'}, plot_legend=True)


            # Example 3: Voronoi plot for a specific image subset, with adjusted alpha and line width, and without flipping the y-axis

            sm.pl.voronoi(adata, subset='image_01', voronoi_alpha=0.7, voronoi_line_width=0.5)

            ```

    """

    # create the data frame needed
    data = adata.obs.copy()

    # Subset the image of interest
    if subset is not None:
        data = data[data[imageid] == subset]

    # subset coordinates if needed
    if x_lim is not None:
        x1 = x_lim[0]
        if len(x_lim) < 2:
            x2 = max(data[x_coordinate])
        else:
            x2 = x_lim[1]
    if y_lim is not None:
        y1 = y_lim[0]
        if len(y_lim) < 2:
            y2 = min(data[y_coordinate])
        else:
            y2 = y_lim[1]

    # do the actuall subsetting
    # if x_lim is not None:
    #    data = data[data[x_coordinate] >= x1]
    #    data = data[data[x_coordinate] <= x2]
    # if y_lim is not None:
    #    data = data[data[y_coordinate] <= y1]
    #    data = data[data[y_coordinate] >= y2]

    # do the actuall subsetting
    if x_lim is not None:
        data = data[(data[x_coordinate] >= x1) & (data[x_coordinate] <= x2)]
    if y_lim is not None:
        data = data[(data[y_coordinate] >= y1) & (data[y_coordinate] <= y2)]

    # create an extra column with index information
    data['index_info'] = np.arange(data.shape[0])

    # generate the x and y coordinates
    points = data[[x_coordinate, y_coordinate]].values

    # invert the Y-axis
    if flip_y is True:
        points[:, 1] = max(points[:, 1]) - points[:, 1]

    # Generate colors
    if color_by is None:
        colors = np.repeat('#e5e5e5', len(data))
    #    elif color_by is None and colors is not None:
    #        if isinstance(colors,str):
    #            colors = np.repeat(colors, len(data))
    elif color_by is not None and colors is None:
        # auto color the samples
        if len(np.unique(data[color_by])) <= 9:
            c = sns.color_palette('Set1')[0 : len(np.unique(data[color_by]))]
        if len(np.unique(data[color_by])) > 9 and len(np.unique(data[color_by])) <= 20:
            c = sns.color_palette('tab20')[0 : len(np.unique(data[color_by]))]
        if len(np.unique(data[color_by])) > 20:
            # For large categories generate random colors
            np.random.seed(0)
            c = np.random.rand(len(np.unique(data[color_by])), 3).tolist()
        # merge colors with phenotypes/ categories of interest
        p = np.unique(data[color_by])
        c_p = dict(zip(p, c))
        # map to colors
        colors = list(map(c_p.get, list(data[color_by].values)))
    elif color_by is not None and colors is not None:
        # check if colors is a dictionary or a sns color scale
        if isinstance(colors, str):
            if len(sns.color_palette(colors)) < len(np.unique(data[color_by])):
                raise ValueError(
                    str(colors)
                    + ' includes a maximun of '
                    + str(len(sns.color_palette(colors)))
                    + ' colors, while your data need '
                    + str(len(np.unique(data[color_by])))
                    + ' colors'
                )
            else:
                c = sns.color_palette(colors)[0 : len(np.unique(data[color_by]))]
                # merge colors with phenotypes/ categories of interest
                p = np.unique(data[color_by])
                c_p = dict(zip(p, c))
        if isinstance(colors, dict):
            if len(colors) < len(np.unique(data[color_by])):
                raise ValueError(
                    'Color mapping is not provided for all categories. Please check'
                )
            else:
                c_p = colors
        # map to colors
        colors = list(map(c_p.get, list(data[color_by].values)))

    # create the voronoi object
    vor = Voronoi(points)

    # trim the object
    regions, vertices = voronoi_finite_polygons_2d(vor)

    # plotting
    pts = MultiPoint([Point(i) for i in points])
    mask = pts.convex_hull
    new_vertices = []
    if type(voronoi_alpha) != list:
        voronoi_alpha = [voronoi_alpha] * len(points)
    areas = []
    for i, (region, alph) in enumerate(zip(regions, voronoi_alpha)):
        polygon = vertices[region]
        shape = list(polygon.shape)
        shape[0] += 1
        p = Polygon(np.append(polygon, polygon[0]).reshape(*shape)).intersection(mask)
        areas += [p.area]
        if p.area < size_max:
            poly = np.array(
                list(zip(p.boundary.coords.xy[0][:-1], p.boundary.coords.xy[1][:-1]))
            )
            new_vertices.append(poly)
            if voronoi_edge_color == 'facecolor':
                plt.fill(
                    *zip(*poly),
                    alpha=alph,
                    edgecolor=colors[i],
                    linewidth=voronoi_line_width,
                    facecolor=colors[i],
                )
                plt.xticks([])
                plt.yticks([])
            else:
                plt.fill(
                    *zip(*poly),
                    alpha=alph,
                    edgecolor=voronoi_edge_color,
                    linewidth=voronoi_line_width,
                    facecolor=colors[i],
                )
                plt.xticks([])
                plt.yticks([])
                # plt.xlim([1097.5,1414.5])
                # plt.ylim([167.3,464.1])

    # Add scatter on top of the voronoi if user requests
    if overlay_points is not None:
        if overlay_points_categories is None:
            d = data
        if overlay_points_categories is not None:
            # convert to list if needed (cells to keep)
            if isinstance(overlay_points_categories, str):
                overlay_points_categories = [overlay_points_categories]
            # subset cells needed
            d = data[data[overlay_points].isin(overlay_points_categories)]
        if overlay_drop_categories is not None:
            # conver to list if needed (cells to drop)
            if isinstance(overlay_drop_categories, str):
                overlay_drop_categories = [overlay_drop_categories]
            # subset cells needed
            d = d[-d[overlay_points].isin(overlay_drop_categories)]

        # Find the x and y coordinates for the overlay category
        # points_scatter = d[[x_coordinate,y_coordinate]].values
        points_scatter = points[d.index_info.values]

        # invert the Y-axis
        # points_scatter[:,1] = max(points_scatter[:,1])-points_scatter[:,1]

        # Generate colors for the scatter plot
        if overlay_points_colors is None and color_by == overlay_points:
            # Borrow color from vornoi
            wanted_keys = np.unique(d[overlay_points])  # The keys to extract
            c_p_scatter = dict((k, c_p[k]) for k in wanted_keys if k in c_p)
        elif overlay_points_colors is None and color_by != overlay_points:
            # Randomly generate colors for all the categories in scatter plot
            # auto color the samples
            if len(np.unique(d[overlay_points])) <= 9:
                c_scatter = sns.color_palette('Set1')[
                    0 : len(np.unique(d[overlay_points]))
                ]
            if (
                len(np.unique(d[overlay_points])) > 9
                and len(np.unique(d[overlay_points])) <= 20
            ):
                c_scatter = sns.color_palette('tab20')[
                    0 : len(np.unique(d[overlay_points]))
                ]
            if len(np.unique(d[overlay_points])) > 20:
                # For large categories generate random colors
                np.random.seed(1)
                c_scatter = np.random.rand(
                    len(np.unique(d[overlay_points])), 3
                ).tolist()
            # merge colors with phenotypes/ categories of interest
            p_scatter = np.unique(d[overlay_points])
            c_p_scatter = dict(zip(p_scatter, c_scatter))
        elif overlay_points_colors is not None:
            # check if the overlay_points_colors is a pallete
            if isinstance(overlay_points_colors, str):
                try:
                    c_scatter = sns.color_palette(overlay_points_colors)[
                        0 : len(np.unique(d[overlay_points]))
                    ]
                    if len(sns.color_palette(overlay_points_colors)) < len(
                        np.unique(d[overlay_points])
                    ):
                        raise ValueError(
                            str(overlay_points_colors)
                            + ' pallete includes a maximun of '
                            + str(len(sns.color_palette(overlay_points_colors)))
                            + ' colors, while your data (overlay_points_colors) need '
                            + str(len(np.unique(d[overlay_points])))
                            + ' colors'
                        )
                except:
                    c_scatter = np.repeat(
                        overlay_points_colors, len(np.unique(d[overlay_points]))
                    )  # [overlay_points_colors]
                # create a dict
                p_scatter = np.unique(d[overlay_points])
                c_p_scatter = dict(zip(p_scatter, c_scatter))
            if isinstance(overlay_points_colors, dict):
                if len(overlay_points_colors) < len(np.unique(d[overlay_points])):
                    raise ValueError(
                        'Color mapping is not provided for all categories. Please check overlay_points_colors'
                    )
                else:
                    c_p_scatter = overlay_points_colors
        # map to colors
        colors_scatter = list(map(c_p_scatter.get, list(d[overlay_points].values)))

        # plt.scatter(x = points_scatter[:,0], y = points_scatter[:,1], s= overlay_point_size, alpha= overlay_point_alpha, c= colors_scatter, marker=overlay_point_shape)
        plt.scatter(
            x=points_scatter[:, 0],
            y=points_scatter[:, 1],
            s=overlay_point_size,
            alpha=overlay_point_alpha,
            c=colors_scatter,
            marker=overlay_point_shape,
            **kwargs,
        )
        plt.xticks([])
        plt.yticks([])

    if plot_legend is True:
        # Add legend to voronoi
        patchList = []
        for key in c_p:
            data_key = mpatches.Patch(color=c_p[key], label=key)
            patchList.append(data_key)

        first_legend = plt.legend(
            handles=patchList,
            bbox_to_anchor=(1.05, 1),
            loc=2,
            borderaxespad=0.0,
            prop={'size': legend_size},
        )

        try:
            plt.tight_layout()
        except:
            pass

        # Add the legend manually to the current Axes.
        ax = plt.gca().add_artist(first_legend)

        if overlay_points is not None:
            # Add legend to scatter
            patchList_scatter = []
            for key in c_p_scatter:
                data_key_scatter = mpatches.Patch(color=c_p_scatter[key], label=key)
                patchList_scatter.append(data_key_scatter)

            plt.legend(
                handles=patchList_scatter,
                bbox_to_anchor=(-0.05, 1),
                loc=1,
                borderaxespad=0.0,
                prop={'size': legend_size},
            )
            # plt.tight_layout()

    # Saving the figure if saveDir and fileName are provided
    if saveDir:
        if not os.path.exists(saveDir):
            os.makedirs(saveDir)
        full_path = os.path.join(saveDir, fileName)
        plt.savefig(full_path, dpi=300)
        plt.close()
        print(f"Saved plot to {full_path}")
    else:
        plt.show()