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Related Experiment Video

Updated: Sep 24, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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BiomeHorizon: Visualizing Microbiome Time Series Data in R.

Isaac Fink1, Richard J Abdill2, Ran Blekhman2,3

  • 1Department of Computer Science, University of Chicago, Chicago, Illinois, USA.

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Visualizing host-associated microbial communities over time is challenging. The new BiomeHorizon R package uses horizon plots to display longitudinal microbiome data, aiding analysis of microbial changes and host health.

Keywords:
R packagemicrobiometime series

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Data Visualization

Background:

  • Host-associated microbial communities significantly impact host health and exhibit substantial temporal variation.
  • Visualizing the dynamics of thousands of taxa in longitudinal microbiome data presents a significant challenge.
  • Existing methods struggle to effectively display both proportional and absolute changes in microbial abundance over time across multiple subjects.

Purpose of the Study:

  • To develop an automated, open-source R package for visualizing longitudinal compositional microbiome data.
  • To address the need for a method that can visualize changes in multiple taxa across multiple subjects over time.
  • To provide a flexible and user-friendly tool for microbiome time series data analysis.

Main Methods:

  • Developed BiomeHorizon, an R package utilizing horizon plots for microbiome data visualization.
  • The package is designed to handle various data formats and accommodate different study designs (e.g., human health, wildlife).
  • Provides automated visualization of proportional and absolute changes in microbial relative abundance.

Main Results:

  • BiomeHorizon offers the first automated R package for visualizing longitudinal microbiome data using horizon plots.
  • The package facilitates the visualization of temporal dynamics in microbial communities.
  • It supports both regularly and irregularly sampled time series data.

Conclusions:

  • BiomeHorizon provides a novel and effective approach to visualizing complex longitudinal microbiome data.
  • The tool enhances the ability to link microbial community shifts with host health outcomes.
  • This package offers a user-friendly solution for researchers studying microbiome dynamics.