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Multivariate Approach for Studying Interactions between Environmental Variables and Microbial Communities

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  • Xinhui Wang
  • Marinus J C Eijkemans
  • Jacco Wallinga
  • Giske Biesbroek
  • Krzysztof Trzciński
  • Elisabeth A M Sanders
  • Debby Bogaert

Abstract

To understand the role of human microbiota in health and disease, we need to study effects of environmental and other epidemiological variables on the composition of microbial communities. The composition of a microbial community may depend on multiple factors simultaneously. Therefore we need multivariate methods for detecting, analyzing and visualizing the interactions between environmental variables and microbial communities. We provide two different approaches for multivariate analysis of these complex combined datasets: (i) We select variables that correlate with overall microbiota composition and microbiota members that correlate with the metadata using canonical correlation analysis, determine independency of the observed correlations in a multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps; (ii) We select variables and microbiota members using univariate or bivariate regression analysis, followed by multivariate regression analysis, and visualize the effect size and direction of the observed correlations using heatmaps. We illustrate the results of both approaches using a dataset containing respiratory microbiota composition and accompanying metadata. The two different approaches provide slightly different results; with approach (i) using canonical correlation analysis to select determinants and microbiota members detecting fewer and stronger correlations only and approach (ii) using univariate or bivariate analyses to select determinants and microbiota members detecting a similar but broader pattern of correlations. The proposed approaches both detect and visualize independent correlations between multiple environmental variables and members of the microbial community. Depending on the size of the datasets and the hypothesis tested one can select the method of preference.

Suggested Citation

  • Xinhui Wang & Marinus J C Eijkemans & Jacco Wallinga & Giske Biesbroek & Krzysztof Trzciński & Elisabeth A M Sanders & Debby Bogaert, 2012. "Multivariate Approach for Studying Interactions between Environmental Variables and Microbial Communities," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-7, November.
  • Handle: RePEc:plo:pone00:0050267
    DOI: 10.1371/journal.pone.0050267
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    1. Peter J. Turnbaugh & Micah Hamady & Tanya Yatsunenko & Brandi L. Cantarel & Alexis Duncan & Ruth E. Ley & Mitchell L. Sogin & William J. Jones & Bruce A. Roe & Jason P. Affourtit & Michael Egholm & Be, 2009. "A core gut microbiome in obese and lean twins," Nature, Nature, vol. 457(7228), pages 480-484, January.
    2. Norman Cliff & David Krus, 1976. "Interpretation of canonical analysis: Rotated vs. unrotated solutions," Psychometrika, Springer;The Psychometric Society, vol. 41(1), pages 35-42, March.
    3. Peter J. Turnbaugh & Ruth E. Ley & Micah Hamady & Claire M. Fraser-Liggett & Rob Knight & Jeffrey I. Gordon, 2007. "The Human Microbiome Project," Nature, Nature, vol. 449(7164), pages 804-810, October.
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    1. Ekele Alih & Hong Choon Ong, 2015. "Cluster-based multivariate outlier identification and re-weighted regression in linear models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 938-955, May.

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