squidpy.gr.spatial_autocorr

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', attr='X', layer=None, seed=None, use_raw=False, copy=False, n_jobs=None, backend='loky', show_progress_bar=True)

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

See [Rey and Anselin, 2010] for reference.

Parameters:

• adata (AnnData | SpatialData) – Annotated data object.

• connectivity_key (str) – Key in anndata.AnnData.obsp where spatial connectivities are stored. Default is: anndata.AnnData.obsp ['spatial_connectivities'].

• genes (Union[str, int, Sequence[str], Sequence[int], None]) –

  Depending on the attr:

  • if attr = 'X', it corresponds to genes stored in anndata.AnnData.var_names. If None, it’s computed anndata.AnnData.var ['highly_variable'], if present. Otherwise, it’s computed for all genes.

  • if attr = 'obs', it corresponds to a list of columns in anndata.AnnData.obs. If None, use all numerical columns.

  • if attr = 'obsm', it corresponds to indices in anndata.AnnData.obsm ['{layer}']. If None, all indices are used.

• mode (Literal['moran', 'geary']) –

  Mode of score calculation:

  • ’moran’ - Moran’s I autocorrelation.

  • ’geary’ - Geary’s C autocorrelation.

• transformation (bool) – If True, weights in anndata.AnnData.obsp ['spatial_connectivities'] are row-normalized, advised for analytic p-value calculation.

• n_perms (Optional[int]) – Number of permutations for the permutation test. If None, only p-values under normality assumption are computed.

• two_tailed (bool) – If True, p-values are two-tailed, otherwise they are one-tailed.

• corr_method (str | None) – Correction method for multiple testing. See statsmodels.stats.multitest.multipletests() for valid options.

• use_raw (bool) – Whether to access anndata.AnnData.raw. Only used when attr = 'X'.

• layer (Optional[str]) – Depending on attr: Layer in anndata.AnnData.layers to use. If None, use anndata.AnnData.X.

• attr (Literal['obs', 'X', 'obsm']) – Which attribute of AnnData to access. See genes parameter for more information.

• seed (Optional[int]) – Random seed for reproducibility.

• copy (bool) – If True, return the result, otherwise save it to the adata object.

• n_jobs (Optional[int]) – Number of parallel jobs.

• backend (str) – Parallelization backend to use. See joblib.Parallel for available options.

• show_progress_bar (bool) – Whether to show the progress bar or not.

Return type:

DataFrame | None

Returns:

: 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, 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:

• anndata.AnnData.uns ['moranI'] - the above mentioned dataframe, if mode = 'moran'.

• anndata.AnnData.uns ['gearyC'] - the above mentioned dataframe, if mode = 'geary'.