ArticleNAR genomics and bioinformatics2020
An application of compositional data analysis to multiomic time-series data.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 34 citations in OpenAlex.
- Bacterial Communities as Modulators of Innate Immune Signalling: An In Vitro Perspective on Toll-Like Receptor Activation.Environmental microbiology reports · 2026Article
- Understanding functional diversity in public primary health care: a cluster analysis of utilization patterns.Frontiers in health services · 2026Article
- A taxon-specific measurement of disruption in a multi-modal study of microbiomes and metabolomes reveals system-wide dysbiosis preceding HIV-1 infection.Nature communications · 2025Article
- MiMeJF: Application of Coupled Matrix and Tensor Factorization (CMTF) for Enhanced Microbiome-Metabolome Multi-Omic Analysis.Metabolites · 2025Article
- Ecology of aerobic anoxygenic phototrophs on a fine-scale taxonomic resolution in Adriatic Sea unravelled by unsupervised neural network.Environmental microbiome · 2024Article
- Data-driven mathematical and visualization approaches for removing rare features for Compositional Data Analysis (CoDA).NAR genomics and bioinformatics · 2024Article
- Article
- Metatranscriptomics-guided genome-scale metabolic modeling of microbial communities.Cell reports methods · 2023Article
- Evaluation of DNA extraction methods and direct PCR in metabarcoding of mock and marine bacterial communities.Frontiers in microbiology · 2023Article
- The community ecology perspective of omics data.Microbiome · 2022Article
- Stem traits, compartments and tree species affect fungal communities on decaying wood.Environmental microbiology · 2022Article
- Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations withmSystems · 2022Article
- Compositional Data Analysis of Microbiome and Any-Omics Datasets: A Validation of the Additive Logratio Transformation.Frontiers in microbiology · 2021Article
- Compositional Data Analysis of Periodontal Disease Microbial Communities.Frontiers in microbiology · 2021Article
- Editorial: Compositional data analysis and related methods applied to genomics-a first special issue fromNAR genomics and bioinformatics · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compositional data analysis (CoDA) methods have increased in popularity as a new framework for analyzing next-generation sequencing (NGS) data. CoDA methods, such as the centered log-ratio (clr) transformation, adjust for the compositional nature of NGS counts, which is not addressed by traditional normalization methods. CoDA has only been sparsely applied to NGS data generated from microbial communities or to multiple 'omics' datasets. In this study, we applied CoDA methods to analyze NGS and untargeted metabolomic datasets obtained from bacterial and fungal communities. Specifically, we used clr transformation to reanalyze NGS amplicon and metabolomics data from a study investigating the effects of building material type, moisture and time on microbial and metabolomic diversity. Compared to analysis of untransformed data, analysis of clr-transformed data revealed novel relationships and stronger associations between sample conditions and microbial and metabolic community profiles.
Identifiers
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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.