ArticlePloS one2021
Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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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
16 citing papers in PubMed, 22 citations in OpenAlex.
- Metabolite-Resolved Validation of Bacterial Organophosphate Transformation in Soil: A Critical Review of Selected Organophosphate Case Studies.Microorganisms · 2026Review
- Enzymatic Synergy-Driven Biotransformation Generates a Postbiotic-Rich Functional Matrix That Reprograms Gut Microbiota Metabolic Pathways Under Stress Conditions.International journal of molecular sciences · 2026Article
- N2SIMBA: from Network topology to SIMulation of interactions and BActerial abundance, using microbial consumer resource model.Frontiers in bioinformatics · 2026Article
- Genotype-dependent stability and specialization of arbuscular mycorrhizal fungal communities under drought in common bean.Frontiers in plant science · 2026Article
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- Elementary methods provide more replicable results in microbial differential abundance analysis.Briefings in bioinformatics · 2025Article
- Benchmarking Differential Abundance Tests for 16S microbiome sequencing data using simulated data based on experimental templates.PloS one · 2025Article
- Article
- Evaluation of biofilm assembly and microbial diversity on a freshwater, ferrous-hulled shipwreck.Applied and environmental microbiology · 2024Article
- Laser capture microdissection as a method for investigating the human hair follicle microbiome reveals region-specific differences in the bacteriome profile.BMC research notes · 2023Article
- Dietary intake of micronized avian eggshell membrane in aged mice reduces circulating inflammatory markers, increases microbiota diversity, and attenuates skeletal muscle aging.Frontiers in nutrition · 2023Article
- Over-optimism in unsupervised microbiome analysis: Insights from network learning and clustering.PLoS computational biology · 2023Article
- Short- and Long-Term Effects of a Prebiotic Intervention with Polyphenols Extracted from European Black Elderberry-Sustained Expansion ofJournal of personalized medicine · 2022Article
- Investigating differential abundance methods in microbiome data: A benchmark study.PLoS computational biology · 2022Review
- CombiningFrontiers in microbiology · 2022Article
- A Three-Day Intervention With Granola Containing Cereal Beta-Glucan Improves Glycemic Response and Changes the Gut Microbiota in Healthy Individuals: A Crossover Study.Frontiers in nutrition · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.
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Registered trials
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