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Incorporating Scale Uncertainty into Differential Expression Analyses Using ALDEx2.

Scott J Dos Santos1, Gregory B Gloor1

  • 1Department of Biochemistry, Schulich School of Medicine and Dentistry, Western University, Ontario, Canada.

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|February 4, 2026
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Summary
This summary is machine-generated.

Differential abundance analyses in sequencing data are improved by accounting for sample scale uncertainty. ALDEx2

Keywords:
ALDEx2RNA‐seqdifferential abundancedifferential expressionmetagenomics

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Differential abundance and expression analyses are standard for sequencing data.
  • Current methods often lack information on true sample scale, leading to technical variation misinterpretation.
  • Existing normalization techniques make flawed assumptions about biological scale, increasing false discovery rates.

Purpose of the Study:

  • To demonstrate incorporating scale models into differential expression analysis for RNA-seq, transcriptome, and metatranscriptome data.
  • To highlight the impact of scale modeling on analysis outcomes.
  • To present visualization methods for ALDEx2 outputs.

Main Methods:

  • Utilizing the ALDEx2 R package to build and apply scale models.
  • Performing differential expression analyses on bulk transcriptome and metatranscriptome datasets.
  • Applying principal component analysis for data visualization.

Main Results:

  • Scale models mitigate incorrect assumptions in normalization, reducing false discovery rates.
  • Incorporating scale models improves the accuracy of differential expression analysis.
  • ALDEx2 outputs can be effectively visualized using compositional principal component analysis.

Conclusions:

  • Accounting for sample scale uncertainty via scale models is crucial for accurate differential abundance and expression analyses.
  • ALDEx2 provides a framework for integrating scale modeling into standard bioinformatics workflows.
  • This approach enhances the reliability of findings from high-throughput sequencing data.