Document Type
Article
Original Publication Date
2012
Journal/Book/Conference Title
PLOS ONE
DOI
10.1371/journal.pone.0052078
Date of Submission
June 2014
Abstract
This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of hypothesis and power/sample size calculations, and applies them for illustration to taxonomic abundance distribution and rank abundance distribution data using HMP Jumpstart data on 24 subjects for saliva, subgingival, and supragingival samples. Software for running these analyses is available.
Rights
Creative Commons Attribution License
Is Part Of
VCU Statistical Sciences and Operations Research Faculty Publications
Appendix S1 Measure of effect size. Introduction of a modified Cramer’s Q criterion such that it does not depend on the sample size when the test statistics takes into account the overdispersion
Comments
Originally published at http://dx.doi.org/10.1371/journal.pone.0052078