Source code for stlearn.plotting.deconvolution_plot

from typing import Optional, Union
from anndata import AnnData
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib as mpl
import numpy as np
import stlearn.plotting.utils as utils

[docs]def deconvolution_plot( adata: AnnData, library_id: str = None, use_label: str = "louvain", cluster: [int, str] = None, celltype: str = None, celltype_threshold: float = 0, data_alpha: float = 1.0, threshold: float = 0.0, cmap: str = "tab20", colors: list = None, # The colors to use for each label... tissue_alpha: float = 1.0, title: str = None, spot_size: 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]: """\ 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. Returns ------- Nothing """ # plt.rcParams['figure.dpi'] = dpi imagecol = adata.obs["imagecol"] imagerow = adata.obs["imagerow"] fig, ax = plt.subplots(figsize=figsize) label = adata.obsm["deconvolution"].T tmp = label.sum(axis=1) label_filter = label.loc[tmp[tmp >= np.quantile(tmp, threshold)].index] if cluster is not None: base = adata.obs[adata.obs[use_label] == str(cluster)][["imagecol", "imagerow"]] else: base = adata.obs[["imagecol", "imagerow"]] if celltype is not None: base = base.loc[ adata.obs_names[adata.obsm["deconvolution"][celltype] > celltype_threshold] ] label_filter_ = label_filter[base.index] if type(colors) == type(None): color_vals = list(range(0, len(label_filter_), 1)) my_norm = mpl.colors.Normalize(0, len(label_filter_)) my_cmap =, len(color_vals)) colors = my_cmap.colors for i, xy in enumerate(base.values): _ = ax.pie( label_filter_.T.iloc[i].values, colors=colors, center=(xy[0], xy[1]), radius=spot_size, frame=True, ) ax.autoscale() if library_id is None: library_id = list(adata.uns["spatial"].keys())[0] image = adata.uns["spatial"][library_id]["images"][ adata.uns["spatial"][library_id]["use_quality"] ] if show_donut: ax_pie = fig.add_axes([0.5, -0.4, 0.03, 0.5]) def my_autopct(pct): return ("%1.0f%%" % pct) if pct >= 4 else "" ax_pie.pie( label_filter_.sum(axis=1), colors=my_cmap.colors, radius=10, # frame=True, autopct=my_autopct, pctdistance=1.1, startangle=90, wedgeprops=dict(width=(3), edgecolor="w", antialiased=True), textprops={"fontsize": 5}, ) if show_legend == True: ax_cb = fig.add_axes([0.9, 0.25, 0.03, 0.5], axisbelow=False) cb = mpl.colorbar.ColorbarBase( ax_cb, cmap=my_cmap, norm=my_norm, ticks=color_vals ) loc = np.array(color_vals) + 0.5 cb.set_ticks(loc) cb.set_ticklabels(label_filter_.index) cb.outline.set_visible(False) # Overlay the tissue image ax.imshow( image, alpha=1, zorder=-1, ) ax.axis("off") if cropped: ax.set_xlim(imagecol.min() - margin, imagecol.max() + margin) ax.set_ylim(imagerow.min() - margin, imagerow.max() + margin) ax.set_ylim(ax.get_ylim()[::-1]) # plt.gca().invert_yaxis() if name is None: name = use_label if output is not None: fig.savefig(output + "/" + name, dpi=dpi, bbox_inches="tight", pad_inches=0) if show: