Pareto Task Inference

set_obsm(adata, obsm_key, n_dimensions)	Sets the obsm key and dimensionality to be used as input for archetypal analysis (AA).
compute_archetypes(adata, n_archetypes[, ...])	Perform Archetypal Analysis (AA) on the input data.
compute_selection_metrics(adata[, min_k, ...])	Compute selection diagnostics for Archetypal Analysis (AA) across different archetype counts.
summarize_aa_metrics(adata, /, **filters)	Concatenate cached selection metrics across archetype counts for a single AA configuration.
compute_bootstrap_variance(adata, n_bootstrap)	Perform bootstrap sampling to compute archetypes and assess their stability.
compute_t_ratio(adata, /, *[, ...])	Compute the t-ratio from an AnnData object that contains archetypes.
get_aa_result(adata, /, *[, return_config])	Fuzzy-get: with no filters require exactly one result; with filters require exactly one match.
t_ratio_significance(adata, *[, n_iter, ...])	Assesses the significance of the polytope spanned by the archetypes by comparing the t-ratio of the original data to t-ratios computed from randomized datasets.