stlearn.spatial.SME.SME_normalize

stlearn.spatial.SME.SME_normalize(adata: AnnData, use_data: str = 'raw', 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', platform: Literal['Visium', 'Old_ST'] = 'Visium', copy: bool = False) → AnnData | None[source]

using spatial location (S), tissue morphological feature (M) and gene expression (E) information to normalize data.

Parameters:

  • adata – Annotated data matrix.

  • use_data – Input data, can be raw counts or log transformed data

  • 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 a copy instead of writing to adata.

Return type:

Anndata