%matplotlib inline

Compute Moran’s I score

This example shows how to compute the Moran’s I global spatial auto-correlation statistics.

The Moran’s I global spatial auto-correlation statistics evaluates whether features (i.e. genes) shows a pattern that is clustered, dispersed or random in the tissue are under consideration.

See also

• See Compute co-occurrence probability and Compute Ripley’s statistics for other scores to describe spatial patterns.

• See Building spatial neighbors graph for general usage of squidpy.gr.spatial_neighbors().

import squidpy as sq

adata = sq.datasets.visium_hne_adata()
adata

AnnData object with n_obs × n_vars = 2688 × 18078
    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', 'rank_genes_groups', 'spatial', 'umap'
    obsm: 'X_pca', 'X_umap', 'spatial'
    varm: 'PCs'
    obsp: 'connectivities', 'distances'

We can compute the Moran’s I score with squidpy.gr.spatial_autocorr and mode = 'moran'. We first need to compute a spatial graph with squidpy.gr.spatial_neighbors. We will also subset the number of genes to evaluate.

genes = adata[:, adata.var.highly_variable].var_names.values[:100]
sq.gr.spatial_neighbors(adata)
sq.gr.spatial_autocorr(
    adata,
    mode="moran",
    genes=genes,
    n_perms=100,
    n_jobs=1,
)
adata.uns["moranI"].head(10)

	I	pval_norm	var_norm	pval_z_sim	pval_sim	var_sim	pval_norm_fdr_bh	pval_z_sim_fdr_bh	pval_sim_fdr_bh
3110035E14Rik	0.665132	0.0	0.000131	0.0	0.009901	0.000247	0.0	0.0	0.012074
Resp18	0.649582	0.0	0.000131	0.0	0.009901	0.000259	0.0	0.0	0.012074
1500015O10Rik	0.605940	0.0	0.000131	0.0	0.009901	0.000184	0.0	0.0	0.012074
Ecel1	0.570304	0.0	0.000131	0.0	0.009901	0.000222	0.0	0.0	0.012074
2010300C02Rik	0.539469	0.0	0.000131	0.0	0.009901	0.000242	0.0	0.0	0.012074
Scg2	0.476060	0.0	0.000131	0.0	0.009901	0.000174	0.0	0.0	0.012074
Ogfrl1	0.457945	0.0	0.000131	0.0	0.009901	0.000198	0.0	0.0	0.012074
Itm2c	0.451842	0.0	0.000131	0.0	0.009901	0.000178	0.0	0.0	0.012074
Tuba4a	0.451810	0.0	0.000131	0.0	0.009901	0.000189	0.0	0.0	0.012074
Satb2	0.429162	0.0	0.000131	0.0	0.009901	0.000200	0.0	0.0	0.012074

We can visualize some of those genes with squidpy.pl.spatial_scatter.

sq.pl.spatial_scatter(adata, color=["Resp18", "Tuba4a"])

We could’ve also passed mode = 'geary' to compute a closely related auto-correlation statistic, Geary’s C. See squidpy.gr.spatial_autocorr for more information.