spateo.tools.spatial_degs
=========================

.. py:module:: spateo.tools.spatial_degs

.. autoapi-nested-parse::

   Spatial DEGs



Functions
---------

.. autoapisummary::

   spateo.tools.spatial_degs.moran_i
   spateo.tools.spatial_degs.cellbin_morani


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

.. py:function:: moran_i(adata: anndata.AnnData, genes: Optional[List[str]] = None, layer: Optional[str] = None, spatial_key: str = 'spatial', model: Literal['2d', '3d'] = '2d', x: Optional[List[int]] = None, y: Optional[List[int]] = None, z: Optional[List[int]] = None, k: int = 5, weighted: Optional[List[str]] = None, permutations: int = 199, n_jobs: int = 1) -> pandas.DataFrame

   Identify genes with strong spatial autocorrelation with Moran's I test.
   This can be used to identify genes that are
   potentially related to cluster.

   :param adata: an Annodata object
   :type adata: :class:`~anndata.AnnData`
   :param genes: The list of genes that will be used to subset the data for dimension
                 reduction and clustering. If `None`, all genes will be used.
   :type genes: `list` or None (default: `None`)
   :param layer: The layer that will be used to retrieve data for dimension reduction
                 and clustering. If `None`, .X is used.
   :type layer: `str` or None (default: `None`)
   :param spatial_key:
   :type spatial_key: The key in ``.obsm`` that corresponds to the spatial coordinate of each cell.
   :param x: x-coordinates of all buckets.
   :type x: 'list' or None(default: `None`)
   :param y: y-coordinates of all buckets.
   :type y: 'list' or None(default: `None`)
   :param z: z-coordinates of all buckets.
   :type z: 'list' or None(default: `None`)
   :param k: Number of neighbors to use by default for kneighbors queries.
   :type k: 'int' (defult=20)
   :param weighted: Spatial weights, defult is None, 'kernel' is based on kernel functions.
   :type weighted: 'str'(defult='kernel')
   :param permutations: Number of random permutations for calculation of pseudo-p_values.
   :type permutations: `int` (default=999)
   :param n_cores: The maximum number of concurrently running jobs, If -1 all CPUs are used.
                   If 1 is given, no parallel computing code is used at all.
   :type n_cores: `int` (default=30)

   :rtype: A pandas DataFrame of the Moran' I test results.


.. py:function:: cellbin_morani(adata_cellbin: anndata.AnnData, binsize: int, cluster_key: str = 'Celltype') -> pandas.DataFrame

   Calculate Moran's I score for each celltype (in segmented cell adata).
   Since the presentation of cells are boolean values, this function first
   summarizes the number of each celltype using a given binsize, creating a
   spatial 2D matrix with cell counts. Then calculates Moran's I score on the
   matrix for spatial score for each celltype.

   :param adata_cellbin: An Annodata object for segmented cells.
   :type adata_cellbin: :class:`~anndata.AnnData`
   :param binsize: The binsize used to summarize cell counts for each celltype.
   :type binsize: int
   :param cluster_key: The key in adata.obs including celltype labels.
   :type cluster_key: `str` (default="Celltype")

   :rtype: A pandas DataFrame containing the Moran' I score for celltypes.