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Multivariate approach for studying interactions between environmental variables and microbial communities.

Xinhui Wang1, Marinus J C Eijkemans, Jacco Wallinga

  • 1Department of Pediatric Immunology and Infectious Diseases, UMC Utrecht, Utrecht, The Netherlands.

Plos One
|November 29, 2012
PubMed
Summary
This summary is machine-generated.

Understanding the human microbiota requires studying environmental impacts. This study presents two multivariate methods to analyze interactions between environmental variables and microbial communities, aiding health and disease research.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Environmental Health

Background:

  • Human microbiota plays a crucial role in health and disease.
  • Microbiota composition is influenced by multiple environmental and epidemiological factors.
  • Multivariate methods are needed to analyze complex interactions between microbiota and environmental variables.

Purpose of the Study:

  • To develop and compare two multivariate approaches for analyzing the relationship between environmental variables and microbial community composition.
  • To detect, analyze, and visualize interactions between environmental factors and human microbiota.
  • To provide tools for understanding the role of microbiota in health and disease.

Main Methods:

  • Two multivariate approaches were employed: (i) Canonical Correlation Analysis (CCA) followed by multivariate regression, and (ii) Univariate/Bivariate regression followed by multivariate regression.
  • Both methods involved selecting correlating variables and microbiota members.
  • Heatmaps were used to visualize the effect size and direction of correlations.

Main Results:

  • Approach (i) identified fewer, stronger correlations, while approach (ii) detected a broader pattern of correlations.
  • Both methods successfully detected and visualized independent correlations between environmental variables and microbial communities.
  • The choice of method depends on dataset size and specific research hypotheses.

Conclusions:

  • Multivariate statistical methods are effective for analyzing complex interactions between environmental factors and human microbiota.
  • The two proposed approaches offer complementary insights into microbiota-environment relationships.
  • These methods can enhance our understanding of the human microbiota's role in health and disease.