nichesphere.coloc.compute_sc_spatial_knn_coloc_matrix

nichesphere.coloc.compute_sc_spatial_knn_coloc_matrix#

nichesphere.coloc.compute_sc_spatial_knn_coloc_matrix(adata, cluster_col, k=5)#

Compute a kNN-based co-localization probability matrix between cell type pairs.

For each cell, the k nearest spatial neighbors are identified. The co-localization matrix counts how often each cluster appears in the neighborhood of each other cluster, then normalizes counts to obtain interaction probabilities.

Parameters:

  • adata (anndata.AnnData) – Spatial AnnData object. Must contain cell centroid coordinates in adata.obsm['spatial'] and a categorical cluster annotation column in adata.obs.

  • cluster_col (str) – Name of the categorical column in adata.obs containing cluster labels.

  • k (int, default 5) – Number of nearest spatial neighbors to consider per cell (excluding self).

Returns:

Symmetric co-localization probability matrix of shape (n_clusters, n_clusters). Entry (i, j) is the probability of finding cluster j in the k-nearest neighborhood of cluster i, normalized so that all entries sum to 1.

Return type:

pd.DataFrame

Notes

The cluster_col column must be of dtype category; cluster order follows adata.obs[cluster_col].cat.categories.

Examples

>>> coloc = compute_sc_spatial_knn_coloc_matrix(adata, cluster_col='cell_type', k=10)
>>> sns.heatmap(coloc, cmap='Blues')