stlearn.spatial.morphology.adjust

stlearn.spatial.morphology.adjust(adata: anndata._core.anndata.AnnData, use_data: str = 'X_pca', radius: float = 50.0, rates: int = 1, method='mean', copy: bool = False, similarity_matrix: Literal[cosine, euclidean, pearson, spearman] = 'cosine') → Optional[anndata._core.anndata.AnnData]

SME normalisation: Using spot location information and tissue morphological features to correct spot gene expression

Parameters

• adata – Annotated data matrix.

• use_data – Input date to be adjusted by morphological features. choose one from [“raw”, “X_pca”, “X_umap”]

• radius – Radius to select neighbour spots.

• rates – Strength for adjustment.

• method – Method for disk smoothing. choose one from [“means”, “median”]

• copy – Return a copy instead of writing to adata.

• similarity_matrix – Matrix to calculate morphological similarity of two spots choose one from [“cosine”, “euclidean”, “pearson”, “spearman”]

Returns

• Depending on copy, returns or updates adata with the following fields.

• **[use_data]_morphology** (adata.obsm field) – Add SME normalised gene expression matrix