Dataset Information

Pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis.

ABSTRACT: MOTIVATION:The human microbiome is notoriously variable across individuals, with a wide range of 'healthy' microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce. RESULTS:We introduce a paired UniFrac dissimilarity that summarizes within-individual (or within-pair) shifts in microbiome composition and then compares these compositional shifts across individuals (or pairs). This dissimilarity depends on a novel transformation of relative abundances, which we then extend to more than two time points and incorporate into several phylogenetic and non-phylogenetic dissimilarities. The data transformation and resulting dissimilarities may be used in a wide variety of downstream analyses, including ordination analysis and distance-based hypothesis testing. Simulations demonstrate that tests based on these dissimilarities retain appropriate type 1 error and high power. We apply the method in two real datasets. AVAILABILITY AND IMPLEMENTATION:The R package pldist is available on GitHub at https://github.com/aplantin/pldist. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Plantinga AM

PROVIDER: S-EPMC6761933 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis.

Plantinga Anna M AM Chen Jun J Jenq Robert R RR Wu Michael C MC

Bioinformatics (Oxford, England) 20191001 19

<h4>Motivation</h4>The human microbiome is notoriously variable across individuals, with a wide range of 'healthy' microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce.<h4>Results</h4>We introduce a paired UniFrac dissimilarity that summarizes within-individual (or within-pair ...[more]

PMID: 30863868

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Dataset Information

Pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis.

Publications

pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis.

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