import shutil
from ast import literal_eval
from pathlib import Path
from typing import Union
import pandas as pd
from anndata import AnnData
from spatialtis.abc import AnalysisBase
from spatialtis.utils import doc
[docs]@doc
class prepare_svca(AnalysisBase):
"""Prepare data for SVCA analysis
Spatial Variance Components Analysis: `SVCA <https://github.com/damienArnol/svca>`_
The input format is separated folder for each ROI with `expressions.txt` and `positions.txt`.
Args:
data: {adata}
out_dir: The directory to store the data
entry: The name of new folder to store the data
**kwargs: {analysis_kwargs}
"""
def __init__(
self,
data: AnnData,
out_dir: Union[Path, str],
entry: str = "svca_data",
**kwargs
):
super().__init__(data, task_name="prepare_svca", **kwargs)
groups = data.obs.groupby(self.exp_obs)
p = Path(out_dir) / entry
if p.exists():
shutil.rmtree(p)
p.mkdir(exist_ok=True)
for n, g in groups:
folder_name = "_".join([str(i) for i in n])
folder_path = p / folder_name
folder_path.mkdir()
expression_path = folder_path / "expressions.txt"
position_path = folder_path / "positions.txt"
# export to expressions.txt
pd.DataFrame(
data.X[[int(i) for i in g.index]], columns=data.var[self.marker_key]
).to_csv(expression_path, sep="\t", index=False)
# export to positions.txt
cents = []
for c in g[self.centroid_key]:
c = literal_eval(c)
cents.append([c[0], c[1]])
pd.DataFrame(cents).to_csv(
position_path, sep="\t", index=False, header=False
)
