partipy.compute_archetype_expression

partipy.compute_archetype_expression#

partipy.compute_archetype_expression(adata, layer=None, result_filters=None)#

Calculate a weighted average gene expression profile for each archetype.

This function computes the weighted average of gene expression across cells for each archetype. The weights should be based on the distance of cells to the archetypes, as computed by calculate_weights.

Parameters:

  • adata (anndata.AnnData) – An AnnData object containing the gene expression data and weights. The weights should be stored in adata.uns["AA_cell_weights"] keyed by the corresponding ArchetypeConfig.

  • layer (str, default None) – The layer of the AnnData object to use for gene expression. If None, adata.X is used. For Pareto analysis of AA data, z-scaled data is recommended.

  • result_filters (Mapping[str, Any] | None, default None) – Filters applied to ArchetypeConfig entries to select the optimization configuration whose weights should be used.

Return type:

DataFrame

Returns:

pd.DataFrame A DataFrame of shape (n_archetypes, n_genes) with weighted pseudobulk expression profiles.

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