title: A Method for Determining Taxonomical Contributions to Group Differences in Microbiomic Investigations.
abstract: Here we show how one can decompose the contribution of different levels of taxonomic classification in terms of their impact on differences in the microbiota when comparing two groups. First we demonstrate a difficulty in trying to estimate taxonomic effects at multiple levels simultaneously and demonstrate an approach to determining which taxa have differences in means that are identified. We then develop a model based on an approach that is popular in the RNA-Seq analysis literature and apply it to the problem of determining which taxa differ between two patient groups. This model provides a more powerful method than simpler alternatives. A Bayesian computational strategy is used to obtain exact inference. Simulation studies indicate that the procedure works as intended, and an application to the study of COPD demonstrates the method's practical utility. Software is provided for implementing the method.
authors.affiliation: 1 Department of Medicine, University of Minnesota , Minneapolis, Minnesota.
authors.forename: Alexa
authors.initials: A
authors.lastname: Pragman
authors.affiliation: 2 Department of Veterinary and Biomedical Sciences, University of Minnesota , St. Paul, Minnesota.
authors.forename: Richard
authors.initials: R
authors.lastname: Issacson
authors.affiliation: 3 Department of Medicine, VA Medical Center Minneapolis , Minnesota.
authors.forename: Christine
authors.initials: C
authors.lastname: Wendt
authors.affiliation: 4 Division of Biostatistics, University of Minnesota , Minneapolis, Minnesota.
authors.forename: Cavan
authors.initials: C
authors.lastname: Reilly
mesh_terms: D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D064307:Microbiota / Q000235:genetics*; D008957:Models, Genetic; D010802:Phylogeny; D029424:Pulmonary Disease, Chronic Obstructive / Q000382:microbiology; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
keywords: Bayesian modeling; COPD; microbiota; overdispersed counts