squidpy.gr.nhood_enrichment

squidpy.gr.nhood_enrichment(adata, cluster_key, library_key=None, connectivity_key=None, n_perms=1000, numba_parallel=False, seed=None, copy=False, n_jobs=None, backend='loky', show_progress_bar=True)[source]

Compute neighborhood enrichment by permutation test.

Parameters:

  • adata (AnnData | SpatialData) – Annotated data object.

  • cluster_key (str) – Key in anndata.AnnData.obs where clustering is stored.

  • library_key (Optional[str]) – If multiple library_id, column in anndata.AnnData.obs which stores mapping between library_id and obs.

  • connectivity_key (Optional[str]) – Key in anndata.AnnData.obsp where spatial connectivities are stored. Default is: anndata.AnnData.obsp ['spatial_connectivities'].

  • n_perms (int) – Number of permutations for the permutation test.

  • numba_parallel (bool) – Whether to use numba.prange or not. If None, it is determined automatically. For small datasets or small number of interactions, it’s recommended to set this to False.

  • seed (Optional[int]) – Random seed for reproducibility.

  • copy (bool) – If True, return the result, otherwise save it to the adata object.

  • n_jobs (Optional[int]) – Number of parallel jobs.

  • backend (str) – Parallelization backend to use. See joblib.Parallel for available options.

  • show_progress_bar (bool) – Whether to show the progress bar or not.

Return type:

tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]] | None

Returns:

: If copy = True, returns a tuple with the z-score and the enrichment count.

Otherwise, modifies the adata with the following keys: