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Related Experiment Video

Updated: Nov 17, 2025

Compost Microcosms as Microbially Diverse, Natural-like Environments for Microbiome Research in Caenorhabditis elegans
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Variable selection in microbiome compositional data analysis.

Antoni Susin1, Yiwen Wang2, Kim-Anh Lê Cao2

  • 1Mathematical Department, UPC-Barcelona Tech, 08028 Barcelona, Spain.

NAR Genomics and Bioinformatics
|February 12, 2021
PubMed
Summary
This summary is machine-generated.

This study compares three microbiome variable selection methods: selbal, clr-lasso, and coda-lasso. Coda-lasso is efficient for identifying associated taxa, while selbal excels at prediction but is computationally intensive.

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Area of Science:

  • Microbiome analysis
  • Compositional data analysis
  • Bioinformatics

Background:

  • Microbiome studies often overlook data's compositional nature, hindering microbial signature identification.
  • Existing compositional data analysis methods face challenges in software availability and result interpretation.

Purpose of the Study:

  • To evaluate three compositional variable selection methods: selbal, clr-lasso, and coda-lasso.
  • To highlight method interconnections and limitations of centered log-ratio transformation in microbiome analysis.

Main Methods:

  • Selbal: A forward selection approach for compositional balances.
  • Clr-lasso and Coda-lasso: Penalized regression models for compositional data.
  • Comparative analysis of their performance and interpretability.

Main Results:

  • Centered log-ratio transformation's lack of subcompositional consistency impacts clr-lasso signature transferability.
  • Coda-lasso offers computational efficiency for identifying associated microbial taxa.
  • Selbal provides parsimonious models with optimal prediction but requires significant computational resources.

Conclusions:

  • The choice of method depends on the research goal: coda-lasso for taxon association, selbal for prediction.
  • A reproducible vignette is provided to facilitate method application in microbiome research.