%matplotlib inline

Neighbors enrichment analysis

This example shows how to run the neighbors enrichment analysis routine.

It calculates an enrichment score based on proximity on the connectivity graph of cell clusters. The number of observed events is compared against \(N\) permutations and a z-score is computed.

See also

See {doc}`compute_spatial_neighbors` for general usage of
{func}`squidpy.gr.spatial_neighbors`.

import squidpy as sq

adata = sq.datasets.visium_fluo_adata()
adata

AnnData object with n_obs × n_vars = 2800 × 16562
    obs: 'in_tissue', 'array_row', 'array_col', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'pct_counts_in_top_50_genes', 'pct_counts_in_top_100_genes', 'pct_counts_in_top_200_genes', 'pct_counts_in_top_500_genes', 'total_counts_MT', 'log1p_total_counts_MT', 'pct_counts_MT', 'n_counts', 'leiden', 'cluster'
    var: 'gene_ids', 'feature_types', 'genome', 'MT', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'cluster_colors', 'hvg', 'leiden', 'leiden_colors', 'neighbors', 'pca', 'spatial', 'umap'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

This dataset contains cell type annotations in anndata.Anndata.obs which are used for calculation of the neighborhood enrichment. First, we need to compute a connectivity matrix from spatial coordinates.

sq.gr.spatial_neighbors(adata)

Then we can calculate the neighborhood enrichment score with squidpy.gr.nhood_enrichment().

sq.gr.nhood_enrichment(adata, cluster_key="cluster")

And visualize the results with squidpy.pl.nhood_enrichment().

sq.pl.nhood_enrichment(adata, cluster_key="cluster")