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Updated: Aug 15, 2025

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Benchmarking differential abundance analysis methods for correlated microbiome sequencing data.

Lu Yang1, Jun Chen1

  • 1Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55901, USA.

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|January 8, 2023
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Summary

Differential abundance analysis (DAA) tools for correlated microbiome data yield varied results. Linear model-based methods like LinDA, MaAsLin2, and LDM show superior robustness, with LinDA excelling under compositional effects.

Keywords:
differential abundance analysislongitudinalmatched-pairmetagenomicsmicrobiomerepeated sampling

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Area of Science:

  • Microbiome research
  • Statistical analysis
  • Bioinformatics

Background:

  • Differential abundance analysis (DAA) is crucial for identifying microbial biomarkers.
  • Microbiome studies often involve correlated samples (spatial, temporal).
  • Existing DAA tools for correlated data (DAA-c) produce inconsistent results.

Purpose of the Study:

  • To comprehensively evaluate existing DAA-c tools.
  • To identify the most robust and accurate DAA-c methods.
  • To provide best practice recommendations for microbiome data analysis.

Main Methods:

  • Real data-based simulations were used for evaluation.
  • Performance of various DAA-c tools was compared.
  • Methods included linear models (LinDA, MaAsLin2, LDM) and generalized linear models.

Main Results:

  • Linear model-based methods (LinDA, MaAsLin2, LDM) demonstrated greater robustness.
  • LinDA maintained performance even with strong compositional effects.
  • Significant discordance was observed among different DAA-c tools.

Conclusions:

  • Linear model-based approaches are recommended for DAA in correlated microbiome data.
  • LinDA is highlighted as a particularly reliable tool, especially for compositional data.
  • Standardized evaluation is needed to guide the selection of DAA-c tools.