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Compositional data analysis enables statistical rigor in comparative glycomics
Alexander R Bennett1, Jon Lundstrøm2,3, Sayantani Chatterjee4
1Department of Medical Biochemistry, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden.
Nature Communications
|January 17, 2025
Summary
This study introduces a new compositional data analysis framework for comparative glycomics. It provides a robust pipeline to accurately analyze glycan data, controlling false-positive rates and revealing biological insights crucial for health and disease research.
Area of Science:
- Biochemistry
- Bioinformatics
- Systems Biology
Background:
- Comparative glycomics data are compositional, with relative abundances of glycans.
- Traditional statistical methods yield misleading conclusions due to data dependencies.
Purpose of the Study:
- To develop a compositional data analysis framework for comparative glycomics.
- To establish a statistically robust and sensitive data analysis pipeline for glycan data.
Main Methods:
- Application of center log-ratio and additive log-ratio transformations.
- Integration of a scale uncertainty/information model.
- Development of alpha- and beta-diversity analyses and cross-class glycan correlations.
Main Results:
- The framework controls false-positive rates in glycan analysis.
- Reproducible biological findings are achieved with the new pipeline.
- Specialized analyses reveal glycan interdependencies and variations.
Conclusions:
- The proposed framework offers a statistically sound approach for comparative glycomics.
- This method enhances the understanding of glycome variations in health and disease.
- It enables deeper insights into the functional roles of glycans.
