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Updated: Jul 11, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
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gmcoda: Graphical model for multiple compositional vectors in microbiome studies.

Huaying Fang1,2

  • 1Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China.

Bioinformatics (Oxford, England)
|November 17, 2023
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Summary
This summary is machine-generated.

We developed gmcoda, a statistical method for analyzing microbiome data. It estimates interactions between bacterial and fungal communities, revealing cross-domain relationships in ecosystems.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical modeling

Background:

  • Microbiome studies utilize high-throughput sequencing to quantify microbial abundances.
  • Sequencing data represents relative abundances, posing challenges as compositional data.
  • Current methods often analyze single microbial domains (e.g., bacteria) but not cross-domain interactions.

Purpose of the Study:

  • To develop a novel statistical method for analyzing interactions between multiple microbial compositional vectors.
  • To address the challenge of analyzing paired compositional data from bacteria and fungi.

Main Methods:

  • Proposed gmcoda, a method based on an additive logistic normal distribution.
  • Employed a majorization-minimization algorithm for optimization.
  • Validated through simulation studies and application to a real microbiome dataset.

Main Results:

  • gmcoda accurately estimates partial correlations between two compositional vectors.
  • The method successfully inferred cross-domain interactions between bacteria and fungi in a real-world dataset.
  • Identified potential ecological interactions between bacterial and fungal communities.

Conclusions:

  • gmcoda provides a robust statistical framework for analyzing multi-domain microbiome data.
  • The method enhances our understanding of complex microbial community interactions.
  • Facilitates the exploration of ecological relationships between different microbial taxa.