ArticleBioinformatics (Oxford, England)2023
Compositional analysis of microbiome data using the linear decomposition model (LDM).
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From association to causation: a decision-aware framework for reproducible biomarker discovery and precision intervention design in the human gut microbiome.Briefings in bioinformatics · 2026Article
- Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT).Microbiome · 2026Article
- Assessing differential cell composition in single-cell studies using voomCLR.Bioinformatics (Oxford, England) · 2026Article
- CAFT: A Compositional Log-Linear Model for Microbiome Data with Zero Cells.bioRxiv : the preprint server for biology · 2025Article
- SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.Scientific reports · 2025Article
- Elementary methods provide more replicable results in microbial differential abundance analysis.Briefings in bioinformatics · 2025Article
- MIDASim: a fast and simple simulator for realistic microbiome data.Microbiome · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
summaryThere are compelling reasons to test compositional hypotheses about microbiome data. We present here linear decomposition model-centered log ratio (LDM-clr), an extension of our LDM approach to allow fitting linear models to centered-log-ratio-transformed taxa count data. As LDM-clr is implemented within the existing LDM program, this extension enjoys all the features supported by LDM, including a compositional analysis of differential abundance at both the taxon and community levels, while allowing for a wide range of covariates and study designs for either association or mediation analysis. AVAILABILITY AND IMPLEMENTATION: LDM-clr has been added to the R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.