CAT: a conditional association test for microbiome data using a leave-out approach

TL;DR

This paper introduces CAT, a novel conditional association test for microbiome data that accounts for inter-feature correlations and phylogenetic relationships, improving the identification of features linked to outcomes.

Contribution

The paper presents a leave-out based conditional association test (CAT) that considers other features and phylogenetic relatedness, enhancing follow-up analysis in microbiome studies.

Findings

CAT effectively identifies associated features in simulated data.

Application to real microbiome data demonstrates CAT's utility in clinical outcome studies.

CAT outperforms marginal tests by accounting for feature interdependence.

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. Researchers have developed global tests for the association of microbiome profiles with outcomes using beta diversity metrics which offer robustness to extreme values and can incorporate information on the phylogenetic tree structure. Despite the popularity of global association testing, most existing methods for follow-up testing of individual features only consider the marginal effect and do not provide relevant information for the design of microbiome interventions. This paper proposes a novel conditional association test, CAT, which can account for other features and phylogenetic relatedness when testing the association between a feature and an outcome. CAT adopts a leave-out method, measuring the importance of a feature in predicting the outcome by removing that feature from the data and quantifying how much the association with the outcome is weakened through the change in the coefficient of determination. By leveraging global tests including PERMANOVA and MiRKAT-based methods, CAT allows association testing for continuous, binary, categorical, count, survival, and correlated outcomes. Our simulation and real data application results illustrate the potential of CAT to inform the design of microbiome interventions aimed at improving clinical outcomes.