A GLM-Based Latent Variable Ordination Method for Microbiome Samples

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...

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Bibliographic Details
Published inBiometrics Vol. 74; no. 2; pp. 448 - 457
Main Authors Sohn, Michael B., Li, Hongzhe
Format Journal Article
LanguageEnglish
Published United States Wiley-Blackwell 01.06.2018
Blackwell Publishing Ltd
Subjects
16S sequencing
Algorithms
BIOMETRIC METHODOLOGY: DISCUSSION PAPER
biometry
Computer Simulation
Data Interpretation, Statistical
Data processing
Dispersion
Factor models
Humans
Microbiome
Microbiomes
Microbiota
multidimensional scaling
Nasopharynx
Nasopharynx - microbiology
Ordination
Oropharynx
Oropharynx - microbiology
Parameter estimation
Poisson Distribution
risk
Zero‐inflated models
Zero-inflated models
Factor models
16S sequencing
Microbiome
Online AccessGet full text
ISSN0006-341X
1541-0420
1541-0420
DOI10.1111/biom.12775

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Summary: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 G̲LM-based O̶rdination M̶ethod for M̶icrobiome S̶amples (GOMMS) in this article. This method uses a zero-inflated quasi-Poisson (ZIQP) latent factor model. An EM algorithm based on the quasilikelihood 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.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12775