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Scale reliant mixed effects models enhance microbiome data analysis.

Kyle C McGovern1, Justin D Silverman2,3,4,5,6

  • 1Program in Bioinformatics and Genomics, Pennsylvania State University, University Park, PA, USA.

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|March 26, 2026
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Summary
This summary is machine-generated.

New scale-reliant mixed-effects models (SR-MEM) improve microbiome analysis by modeling uncertainty in microbial abundance. This approach enhances accuracy and reproducibility in complex studies, outperforming traditional normalization methods.

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • Linear models and linear mixed-effects models (MEMs) are common in microbiome research.
  • Existing MEMs struggle with compositional microbiome data, leading to high error rates.
  • Normalization methods in current MEMs rely on unrealistic assumptions about unmeasured biological scale.

Purpose of the Study:

  • Introduce scale-reliant mixed-effects models (SR-MEM) for robust microbiome data analysis.
  • Address limitations of existing methods in handling compositional data and unmeasured biological scale.
  • Provide a principled framework for mixed-effects modeling in complex microbiome studies.

Main Methods:

  • Developed SR-MEM by explicitly modeling uncertainty in the unmeasured biological scale.
  • Treated biological scale as a latent variable, avoiding normalization-based assumptions.
  • Incorporated external scale measurements or information from independent studies.

Main Results:

  • SR-MEM demonstrated consistent control of the false discovery rate.
  • Achieved comparable or higher statistical power than standard normalization or bias-correction methods.
  • Reanalyses of published datasets showed improved reproducibility and consistency with biological effects.

Conclusions:

  • SR-MEM offers a robust and practical framework for mixed-effects modeling of microbiome sequence count data.
  • By propagating scale uncertainty, SR-MEM enhances error control and reproducibility in longitudinal and hierarchical studies.
  • An implementation is available in the ALDEx3 R package.