An Adaptive Independence Test For Microbiome Community Data

Advances in sequencing technologies and bioinformatics tools have vastly improved our ability to collect and analyze data from complex microbial communities.A major goal of microbiome studies is to correlate the overall microbiome composition with clinical or environmental variables.The generalized Wald test is a parametric test for comparing microbiome populations between two or more groups of subjects.However,this method is not applicable for testing the association between the community composition and a continuous phenotype.Although multivariate non-parametric methods based on permutations are widely used in ecology studies,they lack interpretability and can be inefficient for analyzing microbiome data.We consider the problem of testing for independence between the microbial community composition and a continuous or many-valued variable.By partitioning the range of the variable into a few slices,we formulate the problem as a problem of comparing multiple groups of microbiome samples,with each group indexed by a slice.To model multivariate and over-dispersed count data,we use the Dirichlet-multinomial distribution.We propose an adaptive likelihood-ratio test by learning a good partition or slicing scheme from the data.A dynamic programming algorithm is developed for numerical optimization.Besides,we adopt a log-linear regression to allow covariates adjustments.We demonstrate the superiority of the proposed test by numerically comparing it with that of the generalized Wald test and other popular approaches on the same topic including PERMANOVA,the distance covariance test,and Mi RKAT.Besides,it could control type I error at the nominal level.We further apply it to two real examples: testing the independence of gut microbiome with age in three geographically distinct populations,and testing whether there is an association between BMI and microbiome community.The results show how the learned partition facilitates differential abundance analysis,and proper covariate adjustment is beneficial for the association test.