spateo.tools.spatial_degs

Spatial DEGs

Module Contents

Functions

moran_i(→ pandas.DataFrame)	Identify genes with strong spatial autocorrelation with Moran's I test.
cellbin_morani(→ pandas.DataFrame)	Calculate Moran's I score for each celltype (in segmented cell adata).

spateo.tools.spatial_degs.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.

Parameters

adata : AnnData

an Annodata object

genes : list or None (default: None)

The list of genes that will be used to subset the data for dimension reduction and clustering. If None, all genes will be used.

layer : str or None (default: None)

The layer that will be used to retrieve data for dimension reduction and clustering. If None, .X is used.

spatial_key : The key in .obsm that corresponds to the spatial coordinate of each cell.

x : ‘list’ or None(default: None)

x-coordinates of all buckets.

y : ‘list’ or None(default: None)

y-coordinates of all buckets.

z : ‘list’ or None(default: None)

z-coordinates of all buckets.

k : 'int' (defult=20)

Number of neighbors to use by default for kneighbors queries.

weighted : 'str'(defult='kernel')

Spatial weights, defult is None, ‘kernel’ is based on kernel functions.

permutations : int (default=999)

Number of random permutations for calculation of pseudo-p_values.

n_cores : int (default=30)

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.

Returns

A pandas DataFrame of the Moran’ I test results.

spateo.tools.spatial_degs.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.

Parameters

adata_cellbin : AnnData

An Annodata object for segmented cells.

binsize : int

The binsize used to summarize cell counts for each celltype.

cluster_key : str (default=”Celltype”)

The key in adata.obs including celltype labels.

Returns

A pandas DataFrame containing the Moran’ I score for celltypes.