spateo.tools.spatial_correlation

Functions

spatial_bv_moran_obs_genes(→ Optional[pandas.DataFrame])	Calculate global bivariate Moran's I between a spatial variable and gene expression
spatial_bv_local_moran(→ Optional[pandas.DataFrame])	Calculate global bivariate Moran's I between a spatial variable and gene expression

Module Contents

spateo.tools.spatial_correlation.spatial_bv_moran_obs_genes(adata: anndata.AnnData, obs_key: str, connectivity_key: str = 'spatial_connectivities', genes: str | int | Sequence[str] | Sequence[int] | None = None, n_neighbors: int = 10, mode: str = 'moran', transformation: str = 'r', permutations: int | None = 999, copy: bool = False) → pandas.DataFrame | None

Calculate global bivariate Moran’s I between a spatial variable and gene expression

Parameters:

adata

AnnData object containing spatial data

obs_key

Key in adata.obs for the variable

connectivity_key

Key in adata.obsp for spatial connectivity matrix (default: ‘spatial_connectivities’)

genes

Genes to calculate (names or indices). If None, use all genes.

mode

Spatial correlation mode (only ‘moran’ supported)

transformation

Weight transformation method (‘r’ for row-standardization)

permutations

Number of permutations for significance testing

copy

Return a DataFrame instead of storing in AnnData

Returns:

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

  Ifloat

  value of bivariate Moran’s I

  simarray

  (if permutations>0) vector of I values for permuted samples

  p_simfloat

  (if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed I is extreme it is either extremely high or extremely low

  z_simarray

  (if permutations>0) standardized I based on permutations

  p_z_simfloat

  (if permutations>0) p-value based on standard normal approximation from permutations

• Otherwise, modifies the adata with the following key –

  • anndata.AnnData.uns ['{obs_key}_gene_bv_moranI'] - the above mentioned dataframe``.

spateo.tools.spatial_correlation.spatial_bv_local_moran(adata: anndata.AnnData, feature1_key: str, feature2_key: str, connectivity_key: str = 'spatial_connectivities', n_neighbors: int = 10, mode: str = 'moran', transformation: str = 'r', permutations: int | None = 999, copy: bool = False) → pandas.DataFrame | None

Calculate global bivariate Moran’s I between a spatial variable and gene expression

Parameters:

adata

AnnData object containing spatial data

feature1_key

Key in adata.obs for the first variable or gene_name

feature2_key

Key in adata.obs for the seconda variable or gene_name

connectivity_key

Key in adata.obsp for spatial connectivity matrix (default: ‘spatial_connectivities’)

mode

Spatial correlation mode (only ‘moran’ supported)

transformation

Weight transformation method (‘r’ for row-standardization)

permutations

Number of permutations for significance testing

copy

Return a DataFrame instead of storing in AnnData

Returns:

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

  Ifloat

  value of bivariate Moran’s I

  qarray

  (if permutations>0) values indicate quandrant location 1 HH, 2 LH, 3 LL, 4 HL

  simarray

  (if permutations>0) vector of I values for permuted samples

  p_simfloat

  (if permutations>0) p-value based on permutations (one-sided) null: spatial randomness alternative: the observed I is extreme it is either extremely high or extremely low

  z_simarray

  (if permutations>0) standardized I based on permutations

  p_z_simfloat

  (if permutations>0) p-value based on standard normal approximation from permutations

• Otherwise, modifies the adata with the following key –

  • anndata.AnnData.uns ['{feature1_key}_{feature2_key}_bv_local_moranI'] - the above mentioned dataframe``.