Multi-slice alignment in the mouse brain coronal dataset (alignment process)

We present our evaluation source code of the alignment process of the mouse brain coronal dataset in this section.

Data Preparation

First, we obtain the adata fromat data by st_datasets.

import scanpy as sc

from stMSA.utils import coor_transform, plotting
from stMSA.alignment import get_transform


adatas = sc.read_h5ad('backup/data/mouse_brain_coronal.h5ad')
adata_list = []
for i in range(7):
    adata_list.append(adatas[str(i) == adatas.obs['batch']])

/home/hanshu/anaconda3/envs/stpyg/lib/python3.9/site-packages/anndata/_core/anndata.py:1830: UserWarning: Observation names are not unique. To make them unique, call `.obs_names_make_unique`.
  utils.warn_names_duplicates("obs")

Transform matrix learning

Next, we learn the transform matrix by stMSA.utils.get_transform.

dst_id = 6
pyramidal_label_id = 4
src_id_list = list(range(4, 7))

M = get_transform(adata_list, dst_id, src_id_list, pyramidal_label_id)

>>> INFO: dst slice id: 6, src slice id: 4
>>> INFO: dst coordination shape: (124, 2)
>>> INFO: src coordination shape: (96, 2)
>>> INFO: current distance: 28.69993613238758

>>> INFO: dst slice id: 6, src slice id: 5
>>> INFO: dst coordination shape: (124, 2)
>>> INFO: src coordination shape: (130, 2)
>>> INFO: current distance: 26.266967812973796

Visualization

we visualized the alignment result in 2D (aerial view) and 3D views.

coor_list = [coor_transform(adata_list[src_id].obsm['spatial'], M[src_id]) for src_id in src_id_list]
label_list = [adata_list[src_id].obs['mclust'].to_numpy() for src_id in src_id_list]
plotting(coor_list, label_list, spot_size=3)

plotting(coor_list, label_list, dims='3d', spot_size=3)