ArticleNature communications2025
Compositional data analysis enables statistical rigor in comparative glycomics.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Large glycomics datasets as a tool to understand the function of glycans.Nature chemical biology · 2026Review
- Review
- Understanding functional diversity in public primary health care: a cluster analysis of utilization patterns.Frontiers in health services · 2026Article
- Seal milk oligosaccharides rival human milk complexity and exhibit functional dynamics during lactation.Nature communications · 2025Article
- Uncertainty Modeling Outperforms Machine Learning for Microbiome Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- N-glycans in lung tissue specimens: a prospective target for enhanced cancer diagnosis and prognosis.Journal of translational medicine · 2025Article
- Twenty-Four-Hour Compositional Data Analysis in Healthcare: Clinical Potential and Future Directions.International journal of environmental research and public health · 2025Article
- Incorporating scale uncertainty in microbiome and gene expression analysis as an extension of normalization.Genome biology · 2025Article
- Bridging worlds: connecting glycan representations with glycoinformatics via Universal Input and a canonicalized nomenclature.Bioinformatics advances · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comparative glycomics data are compositional data, where measured glycans are parts of a whole, indicated by relative abundances. Applying traditional statistical analyses to these data often results in misleading conclusions, such as spurious "decreases" of glycans when other structures increase in abundance, or high false-positive rates for differential abundance. Our work introduces a compositional data analysis framework, tailored to comparative glycomics, to account for these data dependencies. We employ center log-ratio and additive log-ratio transformations, augmented with a scale uncertainty/information model, to introduce a statistically robust and sensitive data analysis pipeline. Applied to comparative glycomics datasets, including known glycan concentrations in defined mixtures, this approach controls false-positive rates and results in reproducible biological findings. Additionally, we present specialized analysis modalities: alpha- and beta-diversity analyze glycan distributions within and between samples, while cross-class glycan correlations shed light on previously undetected interdependencies. These approaches reveal insights into glycome variations that are critical to understanding roles of glycans in health and disease.
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