from typing import Optional
import numpy as np
import pandas as pd
from anndata import AnnData
from spatialtis_core import points_bbox, spatial_distribution_pattern
from spatialtis.abc import AnalysisBase
from spatialtis.typing import Number, Tuple
from spatialtis.utils import doc, read_points
[docs]@doc
class cell_dispersion(AnalysisBase):
"""Cell distribution pattern
There are three type of distribution pattern (0 if no cells)
- Random (1)
- Regular (2)
- Cluster (3)
Three methods are provided
- `Index of Dispersion <../about/implementation.html#index-of-dispersion>`_
- `Morisita’s index of dispersion <../about/implementation.html#morisitas-index-of-dispersion>`_
- `Clark and Evans aggregation index <../about/implementation.html#clark-and-evans-aggregation-index>`_
Notice that clark evans' index usually failed to detect local aggregation.
+--------------------------------------+--------+---------+---------+
| | Random | Regular | Cluster |
+======================================+========+=========+=========+
| Index of dispersion: ID | ID = 1 | ID < 1 | ID > 1 |
+--------------------------------------+--------+---------+---------+
| Morisita’s index of dispersion: I | I = 1 | I < 1 | I > 1 |
+--------------------------------------+--------+---------+---------+
| Clark and Evans aggregation index: R | R = 1 | R > 1 | R < 1 |
+--------------------------------------+--------+---------+---------+
Args:
data: {adata}
method: "id", "morisita", and "clark_evans" (Default: "id")
min_cells: The minimum number of the specific type of cells in a ROI to perform analysis
pval: {pval}
r: :code:`method="id"`, determine diameter of sample window, should be in [0, 1], default is 0.1
this take 1/10 of the shortest side of the ROI as the diameter.
resample: :code:`method="id"`, the number of random permutations to perform
quad: :code:`method="morisita"`, {quad}
rect_size: :code:`method="morisita"`, {rect_size}
**kwargs: {analysis_kwargs}
"quad" is quadratic statistic, it cuts a ROI into few rectangles, quad=(10,10) means the ROI will have 10*10 grid.
"""
def __init__(
self,
data: AnnData,
method: str = "id",
min_cells: int = 10,
pval: float = 0.01,
r: Optional[Number] = None,
resample: int = 1000,
quad: Optional[Tuple[int, int]] = None,
rect_size: Optional[Number] = None,
**kwargs,
):
display_method = {
"id": "Index of dispersion",
"morisita": "Morisita index",
"clark_evans": "Clark evans index",
}
self.method = display_method[method]
super().__init__(data, **kwargs)
results_data = []
for roi_name, roi_data in self.roi_iter(desc="Cell dispersion"):
points = read_points(roi_data, self.centroid_key)
bbox = points_bbox(points)
new_df = pd.DataFrame(
dict(points=points, cell_types=roi_data[self.cell_type_key])
)
points_collections = []
cell_types = []
for c, g in new_df.groupby("cell_types"):
points_collections.append(g["points"])
cell_types.append(c)
result = spatial_distribution_pattern(
points_collections,
bbox,
method=method,
r=r,
resample=resample,
quad=quad,
rect_side=rect_size,
pval=pval,
min_cells=min_cells,
)
for c, pattern in zip(cell_types, result):
results_data.append([*roi_name, c, *pattern])
results_data = pd.DataFrame(
data=results_data,
columns=self.exp_obs + ["cell_type", "index_value", "pval", "pattern"],
)
self.params = dict(exp_obs=self.exp_obs)
self.result = results_data
