%matplotlib inline

Compute centrality scores

This example shows how to compute centrality scores, given a spatial graph and cell type annotation.

The scores calculated are closeness centrality, degree centrality and clustering coefficient with the following properties:

- closeness centrality - measure of how close the group is to other nodes.
- clustering coefficient - measure of the degree to which nodes cluster together.
- degree centrality - fraction of non-group members connected to group members.

All scores are descriptive statistics of the spatial graph.

See also

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

import squidpy as sq

adata = sq.datasets.imc()
adata

AnnData object with n_obs × n_vars = 4668 × 34
    obs: 'cell type'
    uns: 'cell type_colors'
    obsm: 'spatial'

This dataset contains cell type annotations in anndata.AnnData.obs, which are used for calculation of centrality scores. First, we need to compute a connectivity matrix from spatial coordinates. We can use squidpy.gr.spatial_neighbors() for this purpose.

sq.gr.spatial_neighbors(adata)

Centrality scores are calculated with squidpy.gr.centrality_scores().

sq.gr.centrality_scores(adata, "cell type")

And visualize results with squidpy.pl.centrality_scores().

sq.pl.centrality_scores(adata, "cell type")