import numpy as np
import pandas as pd
from anndata import AnnData
from spatialtis_core import CellCombs
from spatialtis.abc import AnalysisBase
from spatialtis.utils import NeighborsNotFoundError
from spatialtis.utils import doc
from spatialtis.utils.io import read_neighbors
[docs]@doc
class cell_interaction(AnalysisBase):
"""`Profiling cell-cell interaction <about/implementation.html#profiling-of-cell-cell-interaction>`_
using permutation test
Neighborhood analysis tells you the relationship between different type of cells
- Association (1)
- Avoidance (-1)
- No relationship (0)
Args:
data: {adata}
method: "pval" and "zscore" (Default: "pval")
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.spatial_enrichment`
"""
def __init__(
self,
data: AnnData,
method: str = "pval",
resample: int = 1000,
pval: float = 0.01,
order: bool = False,
**kwargs,
):
if method == "pval":
self.method = "pseudo p-value"
else:
self.method = "z-score"
super().__init__(data, **kwargs)
self.params = {"order": order}
if not self.neighbors_exists:
raise NeighborsNotFoundError("Run `find_neighbors` first before continue.")
cc = CellCombs(self.cell_types, order)
results_data = []
roi_tracker = []
repeat_time = 0
for roi_name, roi_data in self.roi_iter(desc="Cell interaction"):
neighbors = read_neighbors(roi_data, self.neighbors_key)
labels = roi_data[self.cell_id_key]
cell_types = roi_data[self.cell_type_key]
result = cc.bootstrap(
cell_types,
neighbors,
labels,
times=resample,
pval=pval,
method=method,
ignore_self=True,
)
results_data += result
roi_tracker += [roi_name]
repeat_time = len(result)
df = pd.DataFrame(data=results_data, columns=["type1", "type2", "value"])
ix = pd.DataFrame(data=np.repeat(roi_tracker, repeat_time, axis=0), columns=self.exp_obs)
df = pd.concat([ix, df], axis=1)
df = df.pivot_table(values="value", index=self.exp_obs, columns=["type1", "type2"])
self.result = df
