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Updated: Jun 24, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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Published on: September 25, 2021

CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data.

Irene Creus-Martí1, Andrés Moya2,3,4, Francisco J Santonja1

  • 1Department of Statistics and Operational Research, Universitat de València, Valencia, Spain.

Plos Computational Biology
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

CoDaLoMic is a new R package for analyzing longitudinal and compositional microbiome data. It models bacterial group interactions and temporal dynamics, offering a robust framework for microbiome research.

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Visualization of Gut Microbiota-host Interactions via Fluorescence In Situ Hybridization, Lectin Staining, and Imaging
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Area of Science:

  • Microbiome research
  • Computational biology
  • Statistical ecology

Background:

  • Microbiome data is often compositional and longitudinal, posing unique analytical challenges.
  • Existing methods may not fully capture complex interactions within microbial communities over time.
  • Understanding temporal dynamics is crucial for ecological insights and health-related studies.

Purpose of the Study:

  • To introduce CoDaLoMic, an R package for analyzing longitudinal and compositional microbiome datasets.
  • To provide tools for modeling interactions among bacterial groups and analyzing temporal dynamics.
  • To facilitate the study of microbiome variability in relation to host health.

Main Methods:

  • CoDaLoMic implements three specialized models for compositional and longitudinal microbiome data.
  • The package analyzes interactions among groups of bacteria, moving beyond pairwise relationships.
  • It supports the identification of taxa groups with similar temporal dynamics and relates variability to host status.

Main Results:

  • CoDaLoMic offers a robust framework for studying microbial community structure and function over time.
  • The package enables a more integrated and functionally meaningful view of the microbiome by considering group interactions.
  • Demonstrated utility with both real (Blatella germanica) and simulated (Lotka-Volterra) datasets.

Conclusions:

  • CoDaLoMic provides a powerful new R package for advanced microbiome data analysis.
  • The package enhances the understanding of microbial community dynamics and ecological relationships.
  • It supports comprehensive microbiome research by integrating compositional and longitudinal analyses.