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Updated: Feb 6, 2026
09:09
Gene Expression Analyses in Human Follicles
Published on: February 17, 2023
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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.
Current Protocols
|February 4, 2026
Summary
Differential abundance analyses in sequencing data are improved by accounting for sample scale uncertainty. ALDEx2
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.
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