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Elementary methods provide more replicable results in microbial differential abundance analysis.

Juho Pelto1,2, Kari Auranen2,3, Janne V Kujala2

  • 1Department of Computing, University of Turku, University of Turku, 20014, Finland.

Briefings in Bioinformatics
|March 26, 2025
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Summary

Differential abundance analysis (DAA) methods in microbiome studies vary in reproducibility. Nonparametric tests and regression models analyzing relative abundances, or logistic regression on presence/absence data, demonstrated superior consistency.

Keywords:
benchmarkingdifferential abundance analysismicrobiomereplicability

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Analysis

Background:

  • Differential abundance analysis (DAA) is crucial for microbiome research.
  • A lack of consensus exists regarding optimal DAA methods.
  • Reproducibility is a key, yet often unevaluated, performance metric.

Purpose of the Study:

  • To compare the reproducibility of 14 DAA methods.
  • To identify DAA methods that yield consistent results across datasets.
  • To guide the selection of robust DAA techniques in microbiome studies.

Main Methods:

  • Evaluated 14 DAA methods using data from 53 taxonomic profiling studies (16S rRNA and shotgun metagenomics).
  • Assessed method performance by examining result replication between random dataset partitions and across separate studies.
  • Considered both relative abundance and presence/absence data.

Main Results:

  • Significant variability in reproducibility was observed among DAA methods.
  • Some widely used methods produced inconsistent findings.
  • Nonparametric methods (Wilcoxon, ordinal regression) and linear models (regression, t-test) on relative abundances showed high consistency.
  • Logistic regression on presence/absence data also yielded comparable performance.

Conclusions:

  • Reproducibility is a critical benchmark for DAA method selection.
  • Nonparametric and linear models analyzing relative abundances are recommended for robust microbiome DAA.
  • Logistic regression on presence/absence data offers a viable alternative for DAA.