import warnings
from typing import Optional, Union
import pandas as pd
from anndata import AnnData
from scipy.stats import entropy
from spatialtis_core import spatial_entropy
from spatialtis.abc import AnalysisBase
from spatialtis.typing import Array, Number
from spatialtis.utils import doc, read_points
[docs]@doc
class spatial_heterogeneity(AnalysisBase):
"""Evaluate tissue heterogeneity based on entropy
Entropy describes the amount of information.
- `Shannon entropy <../about/implementation.html#shannon-entropy>`_ (No spatial info included):\
To compare the difference within a group (eg. different samples from same tumor), Kullback–Leibler divergences\
for each sample within the group are computed, smaller value indicates less difference within group.
- `Leibovici entropy <../about/implementation.html#leibovici-entropy>`_:\
You can specific the distance threshold to determine co-occurrence events.
- `Altieri entropy <../about/implementation.html#altieri-entropy>`_:\
You can specific the distance interval to determine co-occurrence events.
Args:
data: {adata}
method: "shannon", "leibovici" and "altieri" (Default: "leibovici")
d: :code:`method="leibovici"`, The distance threshold to determine co-occurrence events
cut: :code:`method="altieri"`, Distance interval
**kwargs: {analysis_kwargs}
"""
def __init__(
self,
data: AnnData,
method: str = "leibovici",
d: Optional[int] = None,
cut: Union[int, Array, None] = None,
order: bool = False,
**kwargs,
):
methods_list = ["shannon", "altieri", "leibovici"]
if method not in methods_list:
raise ValueError(
f"Unknonw method: {method}," f"Available: {', '.join(methods_list)}."
)
super().__init__(
data,
method=f"{method.capitalize()} entropy",
**kwargs,
)
if method == "shannon":
df = self.type_counter()
if len(df.columns) == 1:
warnings.warn(
"No heterogeneity, you only have one type of cell.", UserWarning
)
else:
ent = [entropy(row) for _, row in df.iterrows()]
self.result = pd.DataFrame({"heterogeneity": ent}, index=df.index)
else:
points_collections = []
types_collections = []
track_ix = []
type_mapper = {t: i for i, t in enumerate(self.cell_types)}
for roi_name, roi_data in self.roi_iter(desc="Spatial heterogeneity"):
points_collections.append(read_points(roi_data, self.centroid_key))
types_collections.append(roi_data[self.cell_type_key].map(type_mapper))
track_ix.append(roi_name)
ent = spatial_entropy(
points_collections,
types_collections,
method=method,
d=d,
cut=cut,
order=order,
)
self.result = pd.DataFrame(
{"heterogeneity": ent},
index=pd.MultiIndex.from_tuples(track_ix, names=self.exp_obs),
)
