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
[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",
tissue_alpha: float = 1.0,
title: str = None,
spot_size: Union[float, int] = 10,
show_axis: bool = False,
show_legend: bool = True,
cropped: bool = True,
margin: int = 100,
name: str = None,
dpi: int = 150,
output: str = None,
copy: bool = False,
) -> 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 clustering 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_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()
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]
color_vals = list(range(0, len(label_filter_), 1))
my_norm = mpl.colors.Normalize(0, len(label_filter_))
my_cmap = mpl.cm.get_cmap(cmap, len(color_vals))
for i, xy in enumerate(base.values):
_ = ax.pie(
label_filter_.T.iloc[i].values,
colors=my_cmap.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"]
]
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=5,
frame=True,
autopct=my_autopct,
pctdistance=1.1,
startangle=90,
wedgeprops=dict(width=(2), edgecolor="w", antialiased=True),
textprops={"fontsize": 5},
)
ax_pie.set_axis_off()
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)
cb.ax.tick_params(size=0)
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)
plt.show()