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]
Reduce technical noise by spatially smoothing all expression values using

spatial, morphological, and expression (SME) information.

This function modified ALL expression values by averaging each spot’s expression with weighted contributions from similar neighbors. It modifies ALL expression values to reduce technical noise across the entire dataset.

Parameters:

  • adata – Annotated data matrix.

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

  • weights (_WEIGHTING_MATRIX, default="weights_matrix_all") – Strategy for computing neighbor similarity weights: - “weights_matrix_all”: Combines spatial location (S) + morphological features (M) + gene expression correlation (E). - “weights_matrix_pd_gd”: Physical distance + gene expression correlation only. - “weights_matrix_pd_md”: Physical distance + morphological features only. - “weights_matrix_gd_md”: Gene expression + morphological features only. - “gene_expression_correlation”: Expression similarity only. - “physical_distance”: Spatial proximity only. - “morphological_distance”: Tissue morphology similarity only.

  • platform – Visium or Old_ST

  • copy – If True, return a copy instead of writing to adata. If False, modify adata in place and return None.

Return type:

AnnData or None