title: An adaptive association test for microbiome data.
abstract: There is increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. Although existing methods have identified many associations, a proper choice of a phylogenetic distance is critical for the power of these methods. To assess an overall association between the composition of a microbial community and an outcome of interest, we present a novel multivariate testing method called aMiSPU, that is joint and highly adaptive over all observed taxa and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real-data analyses demonstrated that the aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates. The R package MiSPU is available at https://github.com/ChongWu-Biostat/MiSPU and CRAN.
authors.affiliation: Division of Biostatistics, University of Minnesota, 420 Delaware St. SE, Minneapolis, 55455, USA.
authors.forename: Chong
authors.initials: C
authors.lastname: Wu
authors.affiliation: Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First St. SW, Rochester, 55905, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Chen
authors.affiliation: Division of Biostatistics, University of Minnesota, 420 Delaware St. SE, Minneapolis, 55455, USA.
authors.forename: Junghi
authors.initials: J
authors.lastname: Kim
authors.affiliation: Division of Biostatistics, University of Minnesota, 420 Delaware St. SE, Minneapolis, 55455, USA. weip@biostat.umn.edu.
authors.forename: Wei
authors.initials: W
authors.lastname: Pan
mesh_terms: D001419:Bacteria / Q000145:classification*; D019295:Computational Biology / Q000379:methods*; D056726:Genetic Association Studies; D006801:Humans; D064307:Microbiota; D010802:Phylogeny; D064878:Web Browser