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ORIGINAL RESEARCH article

Front. Genet.
Sec. Statistical Genetics and Methodology
Volume 15 - 2024 | doi: 10.3389/fgene.2024.1480972
This article is part of the Research Topic Statistical Approaches, Applications, and Software for Longitudinal Microbiome Data Analysis and Microbiome Multi-Omics Data Integration View all 3 articles

Enhanced Visualization of Microbiome Data in Repeated Measures Designs

Provisionally accepted
Amarise Little Amarise Little 1*Rebecca A. Deek Rebecca A. Deek 2Angela Zhang Angela Zhang 3Ni Zhao Ni Zhao 4Wodan Ling Wodan Ling 5Michael Wu Michael Wu 1,3
  • 1 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, California, United States
  • 2 Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States
  • 3 Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, United States
  • 4 Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States
  • 5 Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, New York, United States

The final, formatted version of the article will be published soon.

    Repeated measures microbiome studies, including longitudinal and clustered designs, o↵er valuable insights into the dynamics of microbial communities and their associations with various health outcomes. However, visualizing such multivariate data poses significant challenges, particularly in distinguishing meaningful biological patterns from noise introduced by covariates and the complexities of repeated measures. In this study, we propose a framework to enhance the visualization of repeated measures microbiome data using Principal Coordinate Analysis (PCoA) adjusted for covariates through linear mixed models (LMM). Our method adjusts for confounding variables and accounts for the repeated measures structure of the data, enabling clearer identification of microbial community variations across time points or clusters. We demonstrate the utility of our approach through simulated scenarios and real datasets, showing that it e↵ectively mitigates the influence of nuisance covariates and highlights key axes of microbiome variation. This refined visualization technique provides a robust tool for researchers to explore and understand microbial community dynamics in repeated measures microbiome studies.

    Keywords: Longitudinal, Temporal, microbiome, PCoA, Visualization method, Kernel matrix

    Received: 14 Aug 2024; Accepted: 16 Oct 2024.

    Copyright: © 2024 Little, Deek, Zhang, Zhao, Ling and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Amarise Little, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, 98109-1024, California, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.