squidpy.gr.spatial_neighbors_knn

squidpy.gr.spatial_neighbors_knn(data, *, spatial_key='spatial', elements_to_coordinate_systems=None, table_key=None, library_key=None, n_neighs=6, percentile=None, transform=None, set_diag=False, key_added='spatial', copy=False, n_jobs=1)[source]

Create a k-nearest-neighbor graph from spatial coordinates.

Each observation is connected to its n_neighs nearest observations in Euclidean space. This mode is typically most useful for continuous coordinates, where you want to control neighborhood size directly.

Parameters:

adata – Annotated data object.
spatial_key (str) – Key in anndata.AnnData.obsm where spatial coordinates are stored.
elements_to_coordinate_systems (dict[str, str] | None) – A dictionary mapping element names of the SpatialData object to coordinate systems. The elements can be either Shapes or Labels. For compatibility, the spatialdata table must annotate all regions keys. Must not be None if adata is a spatialdata.SpatialData.
table_key (str | None) – Key in spatialdata.SpatialData.tables where the spatialdata table is stored. Must not be None if adata is a spatialdata.SpatialData.
library_key (str | None) – If multiple library_id, column in anndata.AnnData.obs which stores mapping between library_id and obs.
n_neighs (int) – Number of nearest neighbors. Defaults to 6. Smaller values produce a sparser, more local graph; larger values connect broader neighborhoods.
percentile (float | None) – Percentile of the distances to use as threshold.
transform (str | Transform | None) – Adjacency matrix transform ('spectral', 'cosine', or None).
set_diag (bool) – Whether to set the diagonal of the connectivities to 1.0.
key_added (str) – Key which controls where the results are saved if copy = False.
copy (bool) – If True, return the result, otherwise save it to the adata object.
n_jobs (int) – Number of parallel jobs used to build the per-library graphs when library_key is set. Each library’s graph is computed independently, so this only has an effect for multi-library data. 1 (default) builds the graphs sequentially and does not change behavior; -1 uses all available CPUs. Has no effect when library_key is None. Speedup is sub-linear (memory-bandwidth bound), and process-based backends pay a one-time worker start-up cost, so parallelism mainly pays off for many large libraries.

Return type:

SpatialNeighborsResult | None

Returns:

If copy = True, returns a SpatialNeighborsResult with the spatial connectivities and distances matrices.

Otherwise, modifies the adata with the following keys:

anndata.AnnData.obsp ['{key_added}_connectivities'] - the spatial connectivities.

anndata.AnnData.obsp ['{key_added}_distances'] - the spatial distances.

anndata.AnnData.uns ['{key_added}'] - dict containing parameters.