import warnings
from typing import Optional
import numpy as np
import pandas as pd
from anndata import AnnData
from natsort import natsorted
from spatialtis_core import comb_bootstrap
from spatialtis.abc import AnalysisBase
from spatialtis.utils import NeighborsNotFoundError
from spatialtis.typing import Array
from spatialtis.utils import doc
from spatialtis.utils.io import read_neighbors
[docs]@doc
class spatial_enrichment(AnalysisBase):
"""`Profiling markers spatial enrichment <about/implementation.html#profiling-of-markers-co-expression>`_
using permutation test
Similar to neighborhood analysis which tells you the relationship between different type of cells.
This analysis tells you the spatial relationship between markers.
Args:
data: {adata}
threshold: The expression level to determine whether a marker is positive
layer_key: {layer_key}
selected_markers: {selected_markers}
resample: Number of times to perform resample
pval: {pval}
order: If False, (Cell_A, Cell_B) and (Cell_B, Cell_A) are the same interaction (Default: False)
**kwargs: {analysis_kwargs}
.. seealso:: :class:`spatialtis.cell_interaction`
"""
def __init__(
self,
data: AnnData,
threshold: Optional[float] = None,
layer_key: Optional[str] = None,
selected_markers: Optional[Array] = None,
resample: int = 500,
pval: float = 0.01,
order: bool = False,
**kwargs,
):
super().__init__(data, **kwargs)
self.params = {"order": order}
if not self.neighbors_exists:
raise NeighborsNotFoundError("Run `find_neighbors` first before continue.")
if (threshold is not None) & (layer_key is None):
layer_key = f"gt_{threshold}"
data.layers[layer_key] = (data.X.copy() >= threshold).astype(bool)
elif (threshold is not None) & (layer_key is not None):
warnings.warn(
"You specific both threshold and layers_key, "
"using user defined layers_key"
)
else:
layer_key = f"mean_cut"
data.layers[layer_key] = (data.X.copy() >= data.X.mean(axis=0)).astype(
bool
)
if selected_markers is None:
markers = self.markers
else:
markers = natsorted(pd.unique(selected_markers))
results_data = []
for roi_name, roi_data, mks, exp in self.roi_exp_iter(
selected_markers=markers,
layer_key=layer_key,
dtype=np.bool,
desc="Spatial enrichment",
):
neighbors = read_neighbors(roi_data, self.neighbors_key)
labels = roi_data[self.cell_id_key]
result = comb_bootstrap(
exp,
mks,
neighbors,
labels,
pval=pval,
order=order,
times=resample,
ignore_self=False,
)
for pairs in result:
results_data.append([*roi_name, *pairs])
df = pd.DataFrame(
data=results_data, columns=self.exp_obs + ["marker1", "marker2", "value"]
)
df = df.pivot_table(
values="value", index=self.exp_obs, columns=["marker1", "marker2"]
)
self.result = df
