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Related Experiment Video

Updated: Jun 28, 2025

Tick Microbiome Characterization by Next-Generation 16S rRNA Amplicon Sequencing

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Published on: August 25, 2018

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis.

Michelle Pistner Nixon¹, Gregory B Gloor², Justin D Silverman^1,3,4

¹College of Information Science and Technology, Pennsylvania State University, University Park, PA, USA.

Biorxiv : the Preprint Server for Biology

|April 15, 2024

Summary

This summary is machine-generated.

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.

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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.