ArticleMicrobiome2024
Proportion-based normalizations outperform compositional data transformations in machine learning applications.
Article in Microbiome, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- EXPLANA: a user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies.Bioinformatics (Oxford, England) · 2026Article
- Machine learning-based classification of COVID-19 severity using respiratory microbiome profiles from shotgun metagenomic sequencing.Frontiers in bioinformatics · 2026Article
- Pollen Foraging by Bumble Bee Queens During a Critical Nesting Period Revealed by DNA Metabarcoding.Ecology and evolution · 2025Article
- Extensive fecal cast production and growth of Vibrionaceae in heat-stressed Atlantic salmon post-smolts.Scientific reports · 2025Article
- Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2.NAR genomics and bioinformatics · 2025Article
- Characteristics of Intestinal Barrier State and Immunoglobulin-Bound Fraction of Stool Microbiota in Advanced Melanoma Patients Undergoing Anti-PD-1 Therapy.International journal of molecular sciences · 2025Article
- Exploring the role of microbiome in cystic fibrosis clinical outcomes through a mediation analysis.mSystems · 2025Article
- Machine learning models reveal Saccharomyces yeasts are associated with poor piglet growth in early development.Journal of animal science · 2025Article
- Individual-based modeling unravels spatial and social interactions in bacterial communities.The ISME journal · 2025Review
- Absolute abundance unveils Basidiobolus as a cross-domain bridge indirectly bolstering gut microbiome homeostasis.The ISME journal · 2025Article
- UniCor and UniCorP: a novel metric and hierarchical feature selection algorithm for microbial community analysis.ISME communications · 2025Article
- Human DNA hijacking microbiota surveys: causes and consequences in colon related 16s rRNA amplicon sequencing.Gut microbiome (Cambridge, England) · 2025Article
- Characterization of bacteria colonizing the mucosal layer of the gastrointestinal tract of Atlantic salmon farmed in a warm water region.Frontiers in microbiology · 2025Article
- Investigating the establishment of the rumen and oral bacterial communities in beef cattle and assessing the applicability of using the oral bacterial community composition as a proxy for rumen bacterial community structure in cattle.Frontiers in microbiology · 2025Article
- Review and revamp of compositional data transformation: A new framework combining proportion conversion and contrast transformation.Computational and structural biotechnology journal · 2024Review
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3 authors.
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Abstract
backgroundNormalization, as a pre-processing step, can significantly affect the resolution of machine learning analysis for microbiome studies. There are countless options for normalization scheme selection. In this study, we examined compositionally aware algorithms including the additive log ratio (alr), the centered log ratio (clr), and a recent evolution of the isometric log ratio (ilr) in the form of balance trees made with the PhILR R package. We also looked at compositionally naïve transformations such as raw counts tables and several transformations that are based on relative abundance, such as proportions, the Hellinger transformation, and a transformation based on the logarithm of proportions (which we call "lognorm").
resultsIn our evaluation, we used 65 metadata variables culled from four publicly available datasets at the amplicon sequence variant (ASV) level with a random forest machine learning algorithm. We found that different common pre-processing steps in the creation of the balance trees made very little difference in overall performance. Overall, we found that the compositionally aware data transformations such as alr, clr, and ilr (PhILR) performed generally slightly worse or only as well as compositionally naïve transformations. However, relative abundance-based transformations outperformed most other transformations by a small but reliably statistically significant margin.
conclusionsOur results suggest that minimizing the complexity of transformations while correcting for read depth may be a generally preferable strategy in preparing data for machine learning compared to more sophisticated, but more complex, transformations that attempt to better correct for compositionality. Video Abstract.
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