title: Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA.
abstract: BACKGROUND
  The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA.
  
  
  RESULTS
  We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.
authors.affiliation: Department of Medicine.
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Kelly
authors.affiliation: Department of Medicine.
authors.forename: Robert
authors.initials: R
authors.lastname: Gross
authors.affiliation: Department of Microbiology and.
authors.forename: Kyle
authors.initials: K
authors.lastname: Bittinger
authors.affiliation: Department of Microbiology and.
authors.forename: Scott
authors.initials: S
authors.lastname: Sherrill-Mix
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
authors.forename: James D
authors.initials: JD
authors.lastname: Lewis
authors.affiliation: Department of Medicine.
authors.forename: Ronald G
authors.initials: RG
authors.lastname: Collman
authors.affiliation: Department of Microbiology and.
authors.forename: Frederic D
authors.initials: FD
authors.lastname: Bushman
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li
mesh_terms: D006801:Humans; D064307:Microbiota; D015233:Models, Statistical; D015999:Multivariate Analysis; D018401:Sample Size; D012984:Software; D018709:Statistics, Nonparametric
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