spac.spatial_analysis.calculate_nearest_neighbor(adata, annotation, spatial_associated_table='spatial', imageid=None, label='spatial_distance', verbose=True)[source]

Computes the shortest distance from each cell to the nearest cell of each phenotype (via scimap.tl.spatial_distance) and stores the resulting DataFrame in adata.obsm[label].

Parameters:

  • adata (anndata.AnnData) – Annotated data matrix with spatial information.

  • annotation (str) – Column name in adata.obs containing cell annotationsi (i.e. phenotypes).

  • spatial_associated_table (str, optional) – Key in adata.obsm where spatial coordinates are stored. Default is ‘spatial’.

  • imageid (str, optional) – The column in adata.obs specifying image IDs. If None, a dummy image column is created temporarily. Spatial distances are computed across the entire dataseti as if it’s one image.

  • label (str, optional) – The key under which results are stored in adata.obsm. Default is ‘spatial_distance’.

  • verbose (bool, optional) – If True, prints progress messages. Default is True.

Returns:

Modifies adata in place by storing a DataFrame of spatial distances in adata.obsm[label].

Return type:

None

Example

For a dataset with two cells (CellA, CellB) both of the same phenotype “type1”, the output might look like:

>>> adata.obsm['spatial_distance']
       type1
CellA    0.0
CellB    0.0

For a dataset with two phenotypes “type1” and “type2”, the output might look like:

>>> adata.obsm['spatial_distance']
       type1     type2
CellA   0.00  1.414214
CellB  1.414214  0.00
Input:
adata.obs:

cell_type imageid type1 image1 type1 image1 type2 image1

adata.obsm[‘spatial’]:

[[0.0, 0.0], [1.0, 1.0], [2.0, 2.0]]

Output stored in adata.obsm[‘spatial_distance’]:

type1 type2

0 0.0 1.414 1 1.414 0.0 2 2.236 1.0

Raises:

ValueError – If spatial_associated_table is not found in adata.obsm. If spatial coordinates are missing or invalid.