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Related Experiment Video

Updated: Jun 5, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

OneNet-One network to rule them all: Consensus network inference from microbiome data.

Camille Champion¹, Raphaëlle Momal¹, Emmanuelle Le Chatelier¹

¹Université Paris-Saclay, INRAE, MGP, Jouy-en-Josas, France.

Plos Computational Biology

|December 6, 2024

Summary

This summary is machine-generated.

Reconstructing microbial interaction networks is challenging. OneNet combines seven methods to create a consensus network, improving precision and identifying reproducible microbial guilds relevant to human health.

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

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Published on: July 9, 2021

Area of Science:

Microbial Ecology
Bioinformatics
Network Science

Background:

Modeling microbial interactions as reproducible networks is difficult.
Existing Gaussian Graphical Model methods produce inconsistent results.
Understanding direct microbial species interactions is key to community function.

Purpose of the Study:

To develop a consensus network inference method (OneNet).
To improve the accuracy and reproducibility of microbial network reconstruction.
To identify robust microbial interactions from abundance data.

Main Methods:

OneNet combines seven network inference methods using stability selection.
It utilizes edge selection frequencies to ensure reproducibility.
A modified stability selection framework tunes regularization parameters.

Main Results:

OneNet achieved higher precision than individual methods on synthetic data.
The method produced slightly sparser consensus networks.
Analysis of gut microbiome data revealed health-relevant microbial guilds.

Conclusions:

OneNet provides a robust approach for microbial network inference.
Consensus networks enhance the reliability of identified microbial interactions.
This method aids in understanding microbial community roles in human health.