spateo.tools.spatial_correlation
================================

.. py:module:: spateo.tools.spatial_correlation


Functions
---------

.. autoapisummary::

   spateo.tools.spatial_correlation.spatial_bv_moran_obs_genes
   spateo.tools.spatial_correlation.spatial_bv_local_moran


Module Contents
---------------

.. py:function:: spatial_bv_moran_obs_genes(adata: anndata.AnnData, obs_key: str, connectivity_key: str = 'spatial_connectivities', genes: Union[str, int, Sequence[str], Sequence[int], None] = None, n_neighbors: int = 10, mode: str = 'moran', transformation: str = 'r', permutations: Optional[int] = 999, copy: bool = False) -> Optional[pandas.DataFrame]

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

   :param adata: AnnData object containing spatial data
   :param obs_key: Key in `adata.obs` for the variable
   :param connectivity_key: Key in `adata.obsp` for spatial connectivity matrix (default: 'spatial_connectivities')
   :param genes: Genes to calculate (names or indices). If None, use all genes.
   :param mode: Spatial correlation mode (only 'moran' supported)
   :param transformation: Weight transformation method ('r' for row-standardization)
   :param permutations: Number of permutations for significance testing
   :param copy: Return a DataFrame instead of storing in AnnData

   :returns: * If ``copy = True``, returns a :class:`pandas.DataFrame` with the following keys --

               I             : float
                               value of bivariate Moran's I
               sim           : array
                               (if permutations>0)
                               vector of I values for permuted samples
               p_sim         : float
                               (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_sim         : array
                               (if permutations>0)
                               standardized I based on permutations
               p_z_sim       : float
                               (if permutations>0)
                               p-value based on standard normal approximation from
                               permutations
             * Otherwise, modifies the ``adata`` with the following key --

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


.. py:function:: 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: Optional[int] = 999, copy: bool = False) -> Optional[pandas.DataFrame]

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

   :param adata: AnnData object containing spatial data
   :param feature1_key: Key in `adata.obs` for the first variable or gene_name
   :param feature2_key: Key in `adata.obs` for the seconda variable or gene_name
   :param connectivity_key: Key in `adata.obsp` for spatial connectivity matrix (default: 'spatial_connectivities')
   :param mode: Spatial correlation mode (only 'moran' supported)
   :param transformation: Weight transformation method ('r' for row-standardization)
   :param permutations: Number of permutations for significance testing
   :param copy: Return a DataFrame instead of storing in AnnData

   :returns: * If ``copy = True``, returns a :class:`pandas.DataFrame` with the following keys --

               I             : float
                               value of bivariate Moran's I
               q             : array
                               (if permutations>0) values indicate quandrant location 1 HH, 2 LH, 3 LL, 4 HL
               sim           : array
                               (if permutations>0)
                               vector of I values for permuted samples
               p_sim         : float
                               (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_sim         : array
                               (if permutations>0)
                               standardized I based on permutations
               p_z_sim       : float
                               (if permutations>0)
                               p-value based on standard normal approximation from
                               permutations
             * Otherwise, modifies the ``adata`` with the following key --

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