 A GLM†based latent variable ordination method for microbiome samplesMichael B. Sohn and Hongzhe Li Biometrics, 2018, vol. 74, issue 2, 448-457 Abstract: Distance†based ordination methods, such as principal coordinates analysis (PCoA), are widely used in the analysis of microbiome data. However, these methods are prone to pose a potential risk of misinterpretation about the compositional difference in samples across different populations if there is a difference in dispersion effects. Accounting for high sparsity and overdispersion of microbiome data, we propose a GLM†based Ordination Method for Microbiome Samples (GOMMS) in this article. This method uses a zero†inflated quasi–Poisson (ZIQP) latent factor model. An EM algorithm based on the quasi†likelihood is developed to estimate parameters. It performs comparatively to the distance†based approach when dispersion effects are negligible and consistently better when dispersion effects are strong, where the distance†based approach sometimes yields undesirable results. The estimated latent factors from GOMMS can be used to associate the microbiome community with covariates or outcomes using the standard multivariate tests, which can be investigated in future confirmatory experiments. We illustrate the method in simulations and an analysis of microbiome samples from nasopharynx and oropharynx. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:74:y:2018:i:2:p:448-457 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Biometrics from The International Biometric Society |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.