peach.tl.archetype_exclusive_patterns

peach.tl.archetype_exclusive_patterns#

peach.tl.archetype_exclusive_patterns(adata, *, data_obsm_key='pathway_scores', obs_key='archetypes', test_method='mannwhitneyu', fdr_method='benjamini_hochberg', fdr_scope='global', min_effect_size=0.05, min_cells=10, use_pairwise=True, verbose=True, **kwargs)[source]#

Identify features exclusively high in single archetypes.

Finds genes or pathways specifically elevated in only one archetype compared to all others. Supports two methods:

  1. Pairwise (default): Tests each archetype vs every other archetype individually. Feature is exclusive if significantly higher vs ALL others. More stringent.

  2. 1-vs-all filtering: Tests each archetype vs all other cells. Feature is exclusive if significant in only ONE archetype’s test. More permissive, higher statistical power.

Parameters:

  • adata (AnnData) – Annotated data object with archetypal assignments.

  • data_obsm_key (str, default: "pathway_scores") – Key in adata.obsm for scores. Use None for gene expression.

  • obs_key (str, default: "archetypes") – Column in adata.obs with archetypal assignments.

  • test_method (str, default: "mannwhitneyu") – Statistical test method.

  • fdr_method (str, default: "benjamini_hochberg") – FDR correction method.

  • fdr_scope ({'global', 'per_archetype', 'none'}, default: 'global') – Scope of FDR correction.

  • min_effect_size (float, default: 0.05) – Minimum effect size (mean_diff for pathways, log_fc for genes).

  • min_cells (int, default: 10) – Minimum cells per archetype.

  • use_pairwise (bool, default: True) – If True, use rigorous pairwise comparisons. If False, use 1-vs-all filtering.

  • verbose (bool, default: True) – Print progress.

Returns:

Results with columns:

  • pathway/gene : Feature identifier

  • archetype : Exclusive archetype

  • mean_archetype : Mean in exclusive archetype

  • mean_other : Mean in other archetypes

  • mean_diff/log_fold_change : Effect size

  • exclusivity_score : Ratio vs max other archetype

  • pvalue, fdr_pvalue, significant

  • pattern_type : ‘exclusive’ or ‘exclusive_pairwise’

Return type:

pd.DataFrame

Examples

>>> # Pairwise method (more stringent)
>>> exclusive = pc.tl.archetype_exclusive_patterns(adata)
>>> # 1-vs-all method (more permissive)
>>> exclusive = pc.tl.archetype_exclusive_patterns(adata, use_pairwise=False)
>>> # Find markers for specific archetype
>>> arch3_markers = exclusive[exclusive["archetype"] == "archetype_3"]
>>> top_markers = arch3_markers.nlargest(10, "exclusivity_score")

See also

peach.tl.pattern_analysis

Comprehensive pattern analysis

peach._core.types.ExclusivePatternResult

Result row structure