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Related Experiment Video

Updated: May 23, 2025

Glycoproteomics of the Extracellular Matrix: A Method for Intact Glycopeptide Analysis Using Mass Spectrometry
14:02

Glycoproteomics of the Extracellular Matrix: A Method for Intact Glycopeptide Analysis Using Mass Spectrometry

Published on: April 21, 2017

12.2K

Quantifying compositional variability in microbial communities with FAVA.

Maike L Morrison1, Katherine S Xue1, Noah A Rosenberg1

  • 1Department of Biology, Stanford University, Stanford, CA 94305.

Proceedings of the National Academy of Sciences of the United States of America
|March 10, 2025
PubMed
Summary

FAVA is a new statistical framework to quantify microbiome variability across multiple samples. This method helps analyze changes in microbial communities over time, space, and across different hosts.

Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

124
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
124

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Ecology

Background:

  • Microbiome composition varies significantly across individuals, environments, and time points.
  • Existing statistical methods struggle to quantify heterogeneity across numerous microbiome samples simultaneously.
  • Understanding microbiome variability is crucial for ecological and health-related studies.

Purpose of the Study:

  • To introduce FAVA (F-based Assessment of Variability across vectors of relative Abundances), a novel framework for assessing compositional variability in microbiome data.
  • To provide a single, interpretable index (0-1) for quantifying microbiome heterogeneity across multiple samples.
  • To develop extensions for incorporating phylogenetic and spatial/temporal information.

Main Methods:

Keywords:
FSTcompositional variabilitymicrobial communitiesmicrobiomespopulation genetics

Related Experiment Videos

Last Updated: May 23, 2025

Glycoproteomics of the Extracellular Matrix: A Method for Intact Glycopeptide Analysis Using Mass Spectrometry
14:02

Glycoproteomics of the Extracellular Matrix: A Method for Intact Glycopeptide Analysis Using Mass Spectrometry

Published on: April 21, 2017

12.2K
  • FAVA utilizes the population-genetic statistic Fst, treating microbiome samples as populations and taxa as alleles.
  • The framework quantifies variability in taxonomic or functional relative abundances.
  • Extensions allow for the integration of phylogenetic relationships and sample metadata (spatial/temporal).

    • Main Results:

    • FAVA successfully quantifies compositional variability across diverse microbiome datasets.
    • The framework was applied to gastrointestinal microbiomes in ruminants, revealing variability changes along the GI tract.
    • FAVA quantified increased temporal variability in human gut microbiomes post-antibiotics and assessed recovery duration.

    Conclusions:

    • FAVA offers a robust and versatile method for characterizing microbiome variability.
    • The framework aids in comparing datasets with varying sample sizes and taxonomic resolutions.
    • FAVA, implemented as an R package, is suitable for microbiome analysis pipelines.