squidpy.gr.spatial_autocorr(adata, connectivity_key='spatial_connectivities', genes=None, mode='moran', transformation=True, n_perms=None, two_tailed=False, corr_method='fdr_bh', layer=None, seed=None, use_raw=False, copy=False, n_jobs=None, backend='loky', show_progress_bar=True)[source]

Calculate Global Autocorrelation Statistic (Moran’s I or Geary’s C).

See [Rey and Anselin, 2010] for reference.

Return type



If copy = True, returns a pandas.DataFrame with the following keys:

  • ’I’ or ‘C’ - Moran’s I or Geary’s C statistic.

  • ’pval_norm’ - p-value under normality assumption.

  • ’var_norm’ - variance of ‘score’ under normality assumption.

  • ’{p_val}_{corr_method}’ - the corrected p-values if corr_method != None .

If n_perms != None is not None, additionally returns the following columns:

  • ’pval_z_sim’ - p-value based on standard normal approximation from permutations.

  • ’pval_sim’ - p-value based on permutations.

  • ’var_sim’ - variance of ‘score’ from permutations.

Otherwise, modifies the adata with the following key: