ArticleNAR genomics and bioinformatics2020
Counts: an outstanding challenge for log-ratio analysis of compositional data in the molecular biosciences.
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 12 papers.
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12 citing papers in PubMed.
- Proportionality-based association metrics in count compositional data.NAR genomics and bioinformatics · 2026Article
- Score Matching for Differential Abundance Testing of Compositional High-Throughput Sequencing Data.Statistics in medicine · 2026Article
- Compositional data modeling of high-dimensional single cell RNA-seq (CoDA-hd): its advantages over commonly used normalization approaches.Journal of translational medicine · 2025Article
- Whole blood transcriptomics analysis of Indonesians reveals translocated and pathogenic microbiota in blood.PloS one · 2025Article
- Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome.Genome biology · 2024Article
- Bayesian inference of relative fitness on high-throughput pooled competition assays.PLoS computational biology · 2024Article
- Bayesian inference of relative fitness on high-throughput pooled competition assays.bioRxiv : the preprint server for biology · 2023Article
- Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data.NAR genomics and bioinformatics · 2023Article
- Article
- Increasing transparency and reproducibility in stroke-microbiota research: A toolbox for microbiota analysis.iScience · 2022Article
- A Simultaneous Feature Selection and Compositional Association Test for Detecting Sparse Associations in High-Dimensional Metagenomic Data.Frontiers in microbiology · 2022Article
- Editorial: Compositional data analysis and related methods applied to genomics-a first special issue fromNAR genomics and bioinformatics · 2020Article
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Authors and funding
3 authors.
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Abstract
Thanks to sequencing technology, modern molecular bioscience datasets are often compositions of counts, e.g. counts of amplicons, mRNAs, etc. While there is growing appreciation that compositional data need special analysis and interpretation, less well understood is the discrete nature of these count compositions (or, as we call them, lattice compositions) and the impact this has on statistical analysis, particularly log-ratio analysis (LRA) of pairwise association. While LRA methods are scale-invariant, count compositional data are not; consequently, the conclusions we draw from LRA of lattice compositions depend on the scale of counts involved. We know that additive variation affects the relative abundance of small counts more than large counts; here we show that additive (quantization) variation comes from the discrete nature of count data itself, as well as (biological) variation in the system under study and (technical) variation from measurement and analysis processes. Variation due to quantization is inevitable, but its impact on conclusions depends on the underlying scale and distribution of counts. We illustrate the different distributions of real molecular bioscience data from different experimental settings to show why it is vital to understand the distributional characteristics of count data before applying and drawing conclusions from compositional data analysis methods.
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