CAT: a conditional association test for microbiome data using a permutation approach.

ABSTRACT: In microbiome analysis, researchers often seek to identify taxonomic features associated with an outcome of interest. However, microbiome features are intercorrelated and linked by phylogenetic relationships, making it challenging to assess the association between an individual feature and an outcome. This paper proposes a novel conditional association test, CAT, that can account for other features and phylogenetic relatedness when testing the association between a feature and an outcome. CAT adopts a permutation approach, measuring the importance of a feature in predicting the outcome by permuting operational taxonomic unit/amplicon sequence variant counts belonging to that feature from the data and quantifying how much the association with the outcome is weakened through the change in th

SUBMITTER: Shi Y 

PROVIDER: S-EPMC12362256 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature