ArticlemSystems2022
Approximation of a Microbiome Composition Shift by a Change in a Single Balance Between Two Groups of Taxa.
Article in mSystems, 2022. 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.
- Human gut flagellome profiling using FlaPro reveals TLR5-related phenotype-specific alterations in IBD.Gut microbes · 2026Article
- Alcohol consumption in metabolic dysfunction-associated steatotic liver disease (MASLD): understanding the gut-liver crosstalk for clinical translation.Gut microbes · 2026Article
- A permutation test of differences between externally or internally defined groupings in compositional data sets.Statistical methods in medical research · 2026Article
- Selection and transmission of the gut microbiome alone can shift mammalian behavior.Nature communications · 2025Article
- Microbiome of the Proximal Small Intestine in Patients with Acute Pancreatitis.Diagnostics (Basel, Switzerland) · 2025Article
- Gut Microbiome in Post-COVID-19 Patients Is Linked to Immune and Cardiovascular Health Status but Not COVID-19 Severity.Microorganisms · 2023Article
- Stability of human gut microbiome: Comparison of ecological modelling and observational approaches.Computational and structural biotechnology journal · 2023Article
- Three approaches to supervised learning for compositional data with pairwise logratios.Journal of applied statistics · 2023Article
- Luminal and Tumor-Associated Gut Microbiome Features Linked to Precancerous Lesions Malignancy Risk: A Compositional Approach.Cancers · 2022Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Linking microbiome composition obtained from metagenomic or 16S rRNA sequencing to various factors poses a real challenge. The compositional approach to such data is well described: a so-called isometric log-ratio (ILR) transform provides correct treatment of relative abundances. Most existing compositional methods differ in the particular choice of the transform. Although this choice does not influence the prediction of a model, it determines the subset of balances between groups of microbial taxa subsequently used for interpreting the composition shifts. We propose a method to interpret these shifts independently of the initial choice of ILR coordinates by the nearest single-balance shift. We describe here application of the method to regression, classification, and principal balance analysis of compositional data. Analytical treatment and cross-validation show that the approach provides the least-squares estimate of a single-balance shift associated with a factor with possible adjustment for covariates. As for classification and principal balance analysis, the nearest balance method provides results comparable to other compositional tools. Its advantages are the absence of assumptions about the number of taxa included in the balance and its low computational cost. The method is implemented in the R package NearestBalance.
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