from typing import (
Optional, # Special
)
from anndata import AnnData
from stlearn.pl._docs import doc_spatial_base_plot, doc_subcluster_plot
from stlearn.pl.classes import SubClusterPlot
from stlearn.utils import _AxesSubplot, _docs_params
[docs]
@_docs_params(
spatial_base_plot=doc_spatial_base_plot, subcluster_plot=doc_subcluster_plot
)
def subcluster_plot(
adata: AnnData,
# plotting param
title: Optional["str"] = None,
figsize: tuple[float, float] | None = None,
cmap: str = "jet",
use_label: str | None = None,
list_clusters: list | None = None,
ax: _AxesSubplot | None = None,
show_plot: bool = True,
show_axis: bool = False,
show_image: bool = True,
show_color_bar: bool = True,
crop: bool = True,
margin: float = 100,
size: float = 5,
image_alpha: float = 1.0,
cell_alpha: float = 1.0,
fname: str | None = None,
dpi: int = 120,
# subcluster plot param
cluster: int = 0,
threshold_spots: int = 5,
text_box_size: float = 5,
bbox_to_anchor: tuple[float, float] | None = (1, 1),
) -> AnnData | None:
"""\
Allows the visualization of a subclustering results as the discretes values
of dot points in the Spatial transcriptomics array.
Parameters
-------------------------------------
{spatial_base_plot}
{subcluster_plot}
Examples
-------------------------------------
>>> import stlearn as st
>>> adata = st.datasets.visium_sge(sample_id="V1_Breast_Cancer_Block_A_Section_1")
>>> label = "leiden"
>>> cluster = 6
>>> st.pl.cluster_plot(adata, use_label = label, cluster = cluster)
"""
assert use_label is not None, "Please select `use_label` parameter"
assert (
use_label in adata.obs.columns
), "Please run `stlearn.spatial.cluster.localization` function!"
SubClusterPlot(
adata,
title=title,
figsize=figsize,
cmap=cmap,
use_label=use_label,
list_clusters=list_clusters,
ax=ax,
show_plot=show_plot,
show_axis=show_axis,
show_image=show_image,
show_color_bar=show_color_bar,
crop=crop,
margin=margin,
size=size,
image_alpha=image_alpha,
cell_alpha=cell_alpha,
fname=fname,
dpi=dpi,
text_box_size=text_box_size,
bbox_to_anchor=bbox_to_anchor,
cluster=cluster,
threshold_spots=threshold_spots,
)
return adata