mantispy.tl.moa_enrichment

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mantispy.tl.moa_enrichment#

mantispy.tl.moa_enrichment(adata, moa_key='Metadata_MOA', groupby='Metadata_Perturbation', k=10, metric='cosine', use_rep=None, key_added='moa_enrichment', copy=False)[source]#

Test which mechanisms are over-represented among each profile’s nearest neighbors.

For each profile and mechanism, a hypergeometric test asks whether the mechanism is more common among the k nearest neighbors than among all other profiles.

Parameters:

  • adata (AnnData) – Profiles to test, one row per treatment or per well.

  • moa_key (str (default: 'Metadata_MOA')) – obs column holding the mechanism labels.

  • groupby (str (default: 'Metadata_Perturbation')) – obs column naming each profile in the output table.

  • k (int (default: 10)) – Number of neighbors considered. Smaller values are more local and less powerful.

  • metric (str (default: 'cosine')) – As in nn_moa_classify().

  • use_rep (str | None (default: None)) – As in nn_moa_classify().

  • key_added (str (default: 'moa_enrichment')) – Name for the output table.

  • copy (bool (default: False)) – Return a modified copy instead of mutating in place.

Return type:

AnnData | None

Returns:

None, or the modified copy. Writes uns["mantispy"][key_added] with group, moa, n_neighbours, pvalue and qvalue, one row per annotated profile and per mechanism found among its neighbors.

Notes

Unannotated profiles are not tested, but they can be neighbors. They take up places among the k neighbors without adding to any mechanism’s count, and they are part of the population the test draws from. The profile itself is excluded from both its neighborhood and the population.

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