SpatialDE.spatial_patterns

SpatialDE.spatial_patterns(adata, genes=None, normalized=False, spatial_key='spatial', layer=None, params=SpatialPatternParameters(), rng=np.random.default_rng(), copy=False)

Detect spatial patterns of gene expression and assign genes to patterns.

Uses a Gaussian process mixture. A Dirichlet process prior allows to automatically determine the number of distinct regions in the dataset.

Parameters:

  • adata (AnnData) – The annotated data matrix.

  • genes (Optional[List[str]]) – List of genes to base the analysis on. Defaults to all genes.

  • normalized – Whether the data are already normalized to an approximately Gaussian likelihood. If False, they will be normalized using the workflow from Svensson et al, 2018.

  • spatial_key – Key in adata.obsm where the spatial coordinates are stored.

  • layer (Optional[str]) – Name of the AnnData object layer to use. By default adata.X is used.

  • params (SpatialPatternParameters) – Parameters for the algorithm, e.g. prior distributions, spatial smoothness, etc.

  • rng (Generator) – Random number generator.

  • copy (bool) – Whether to return a copy of adata with results or write the results into adata in-place.

Return type:

Tuple[SpatialPatterns, Optional[AnnData]]

Returns:

A tuple. The first element is a SpatialPatterns, the second is None if copy == False or an AnnData object. Patterns will be in adata.obs["spatial_pattern_0"], …, adata.obs["spatial_pattern_n"].