stlearn.spatial.SME.pseudo_spot

stlearn.spatial.SME.pseudo_spot(adata: anndata._core.anndata.AnnData, tile_path: Union[pathlib.Path, str] = PosixPath('/tmp/tiles'), use_data: str = 'raw', crop_size: int = 'auto', platform: Literal[Visium, Old_ST] = 'Visium', weights: Literal[weights_matrix_all, weights_matrix_pd_gd, weights_matrix_pd_md, weights_matrix_gd_md, gene_expression_correlation, physical_distance, morphological_distance] = 'weights_matrix_all', copy: Literal[pseudo_spot_adata, combined_adata] = 'pseudo_spot_adata') → Optional[anndata._core.anndata.AnnData]

using spatial location (S), tissue morphological feature (M) and gene expression (E) information to impute gap between spots and increase resolution for gene detection

Parameters

  • adata – Annotated data matrix.

  • use_data – Input data, can be raw counts, log transformed data or dimension reduced space(X_pca and X_umap)

  • tile_path – Path to save spot image tiles

  • crop_size – Size of tiles if auto, automatically detect crop size

  • weights – Weighting matrix for imputation. if weights_matrix_all, matrix combined all information from spatial location (S), tissue morphological feature (M) and gene expression (E) if weights_matrix_pd_md, matrix combined information from spatial location (S), tissue morphological feature (M)

  • platform – Visium or Old_ST

  • copy – Return Anndata if pseudo_spot_adata, imputed Anndata if combined_adata, merged Anndata of original data imputed Anndata.

Returns

Return type

Anndata