ArticleBMC bioinformatics2023
coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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45 citing papers in PubMed.
- Trial
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- CoDaLoMic: An R package for modeling microbiome compositional and longitudinal data.PLoS computational biology · 2026Article
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- Preliminary insights into gut microbiome shifts as screening proxy for MASLD disease progression.Scientific reports · 2026Article
- Metagenomic Sequencing Reveals Distinct Gut Microbiome Profiles in Therapy-Naïve de Novo Pediatric Inflammatory Bowel Disease.Inflammatory bowel diseases · 2026Article
- EXPLANA: a user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies.Bioinformatics (Oxford, England) · 2026Article
- BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features.Bioinformatics advances · 2026Article
- Sparse time-varying log-ratios for longitudinal high-throughput sequencing data.Frontiers in bioinformatics · 2026Article
- From farm to microbe: organic amendments and soil texture as drivers of soil Microbiome composition.Environmental microbiome · 2025Article
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- Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.Genome medicine · 2025Article
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- Microbial Contamination in Urban Marine Sediments: Source Identification Using Microbial Community Analysis and Fecal Indicator Bacteria.Microorganisms · 2025Article
- Bayesian compositional generalized linear mixed models for disease prediction using microbiome data.BMC bioinformatics · 2025Article
- Microbiota transplantation for cotton leaf curl disease suppression-core microbiome and transcriptome dynamics.Communications biology · 2025Article
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3 authors.
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Abstract
backgroundOne of the main challenges of microbiome analysis is its compositional nature that if ignored can lead to spurious results. Addressing the compositional structure of microbiome data is particularly critical in longitudinal studies where abundances measured at different times can correspond to different sub-compositions.
resultsWe developed coda4microbiome, a new R package for analyzing microbiome data within the Compositional Data Analysis (CoDA) framework in both, cross-sectional and longitudinal studies. The aim of coda4microbiome is prediction, more specifically, the method is designed to identify a model (microbial signature) containing the minimum number of features with the maximum predictive power. The algorithm relies on the analysis of log-ratios between pairs of components and variable selection is addressed through penalized regression on the "all-pairs log-ratio model", the model containing all possible pairwise log-ratios. For longitudinal data, the algorithm infers dynamic microbial signatures by performing penalized regression over the summary of the log-ratio trajectories (the area under these trajectories). In both, cross-sectional and longitudinal studies, the inferred microbial signature is expressed as the (weighted) balance between two groups of taxa, those that contribute positively to the microbial signature and those that contribute negatively. The package provides several graphical representations that facilitate the interpretation of the analysis and the identified microbial signatures. We illustrate the new method with data from a Crohn's disease study (cross-sectional data) and on the developing microbiome of infants (longitudinal data).
conclusionscoda4microbiome is a new algorithm for identification of microbial signatures in both, cross-sectional and longitudinal studies. The algorithm is implemented as an R package that is available at CRAN ( https://cran.r-project.org/web/packages/coda4microbiome/ ) and is accompanied with a vignette with a detailed description of the functions. The website of the project contains several tutorials: https://malucalle.github.io/coda4microbiome/.
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