title: Comparisons of distance methods for combining covariates and abundances in microbiome studies.
abstract: This article compares different methods for combining abundance data, phylogenetic trees and clinical covariates in a nonparametric setting. In particular we study the output from the principal coordinates analysis on UNIFRAC and WEIGHTED UNIFRAC distances and the output from a double principal coordinate analyses DPCOA using distances computed on the phylogenetic tree. We also present power comparisons for some of the standard tests of phylogenetic signal between different types of samples. These methods are compared both on simulated and real data sets. Our study shows that DPCoA is less robust to outliers, and more robust to small noisy fluctuations around zero.
authors.affiliation: Statistics Department, Stanford University, Stanford, CA 94305, USA. jfukuyama@stanford.edu
authors.forename: Julia
authors.initials: J
authors.lastname: Fukuyama
authors.forename: Paul J
authors.initials: PJ
authors.lastname: McMurdie
authors.forename: Les
authors.initials: L
authors.lastname: Dethlefsen
authors.forename: David A
authors.initials: DA
authors.lastname: Relman
authors.forename: Susan
authors.initials: S
authors.lastname: Holmes
mesh_terms: D000900:Anti-Bacterial Agents / Q000494:pharmacology; D002939:Ciprofloxacin / Q000494:pharmacology; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D006801:Humans; D007422:Intestines / Q000187:drug effects / Q000382:microbiology; D064307:Microbiota / Q000187:drug effects; D010802:Phylogeny; D025341:Principal Component Analysis; D018709:Statistics, Nonparametric
chemical_list: D000900:Anti-Bacterial Agents; D002939:Ciprofloxacin