from typing import Optional
import numpy as np
import pandas as pd
from anndata import AnnData
from spatialtis_core import spatial_autocorr as autocorr
from spatialtis.abc import AnalysisBase
from spatialtis.utils import read_neighbors, doc
[docs]@doc
class spatial_autocorr(AnalysisBase):
"""Spatial auto-correlation for every markers
This is used measure the correlation of marker expression with spatial locations.
Moran's I is more for global spatial autocorrelation,
Geary's C is more for local spatial autocorrelation
Args:
data: {data}
method: "moran_i" or "geary_c" (Default: "moran_i")
pval: {pval}
two_tailed: Whether to use two tailed for p-value
layer_key: {layer_key}
**kwargs: {analysis_kwargs}
.. seealso:: :class:`spatialtis.somde`
"""
def __init__(
self,
data: AnnData,
method: str = "moran_i",
pval: float = 0.05,
two_tailed: bool = True,
layer_key: Optional[str] = None,
**kwargs,
):
super().__init__(data, display_name="Spatial auto-correlation", **kwargs)
track_ix = []
results_data = []
for roi_name, roi_data, markers, exp in self.roi_exp_iter(
layer_key=layer_key, desc=self.display_name
):
neighbors = read_neighbors(roi_data, self.neighbors_key)
labels = roi_data[self.cell_id_key]
results = autocorr(
exp.astype(np.float64),
neighbors,
labels=labels,
two_tailed=two_tailed,
pval=pval,
method=method,
)
markers = markers.to_numpy()
results = np.hstack([markers.reshape(-1, 1), results])
track_ix += [roi_name for _ in range(len(markers))]
results_data.append(results)
self.result = pd.concat(
[
pd.DataFrame(data=track_ix, columns=self.exp_obs),
pd.DataFrame(
data=np.concatenate(results_data),
columns=["marker", "pattern", "index_value", "pval"],
),
],
axis=1,
)
