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Updated: Jun 28, 2025
07:21
Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing
Published on: August 25, 2018
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Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis
Michelle Pistner Nixon1, Gregory B Gloor2, Justin D Silverman1,3,4
1College of Information Science and Technology, Pennsylvania State University, University Park, PA, USA.
Biorxiv : the Preprint Server for Biology
|April 15, 2024
Summary
Statistical normalizations in sequencing analysis can lead to errors. Scale models offer a robust alternative, improving accuracy and reducing false positives in differential abundance and expression studies.
Area of Science:
- Bioinformatics
- Computational Biology
- Statistical Modeling
Background:
- Statistical normalizations are standard for handling sample variation in sequencing data.
- Common normalizations rely on assumptions about biological system scale (e.g., microbial load).
- Violations of these assumptions can cause false positives and negatives in differential analyses.
Approach:
- Introduced scale models as a generalization of normalization methods.
- Scale models allow researchers to explicitly model potential errors in scale assumptions.
- Integrated scale models into the ALDEx2 software package.
Key Points:
- Scale models reduce false positives compared to traditional normalizations.
- The ALDEx2 software with scale models enhances analysis reproducibility.
- This approach significantly decreases false positive and false negative rates.
Conclusions:
- Scale models are recommended over normalizations for differential analyses in practical settings.
- This method provides a more robust framework for analyzing sequencing data.
- Improved accuracy in differential abundance and expression analysis is achieved.
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