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Statistical Models and Analysis of Microbiome Data from Mice and Humans

Part of the Physiology in Health and Disease book series (PIHD)

Abstract

After the initiation of the Human Microbiome Project in 2007, numerous statistical and bioinformatic tools for data analysis and computational methods were developed and applied to meet the needs of microbiome studies. One of the popular platforms is to implement the newly developed statistical and bioinformatic methods and models using R packages.

In this chapter, we introduce the widely used and newly developed statistical methods and models in the ecology and microbiome fields. We show readers how to use the current available statistical tools based on the R programming language to analyze microbiome data. Our purpose is to provide the analytical steps and tools to be implemented by microbiome researchers, who may not have advanced knowledge of statistical models and R programming language. Specifically, this chapter covers frequently used univariate and multivariate statistical models and visualization tools, in addition to alpha and beta metrics and R programming skills, using real data from mouse and human microbiome studies.

Keywords

  • Gut microbiome
  • Statistical methods
  • Statistical analysis
  • R package

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Acknowledgments

We would like to acknowledge the NIDDK/National Institutes of Health grant R01 DK105118 and DOD BC160450P1 to Jun Sun. We thank the two anonymous reviewers whose comments/suggestions helped to improve and clarify this manuscript.

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Correspondence to Yinglin Xia or Jun Sun .

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Xia, Y., Sun, J. (2018). Statistical Models and Analysis of Microbiome Data from Mice and Humans. In: Sun, J., Dudeja, P. (eds) Mechanisms Underlying Host-Microbiome Interactions in Pathophysiology of Human Diseases. Physiology in Health and Disease. Springer, Boston, MA. https://doi.org/10.1007/978-1-4939-7534-1_12

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