ArticleNature communications2020
Analysis of compositions of microbiomes with bias correction.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06869941 (Adipose Tissue Gene Expression and Metabolomics Links to the Gut Microbiome-brain Axis), which is not on this map. Cited by 1,295 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Adipose Tissue Gene Expression and Metabolomics Links to the Gut Microbiome-brain Axis (POINSETTIA Study)
Who cites it
1,295 citing papers in PubMed.
- Gut microbial diversity at baseline conditions the clinical, microbiome, and metabolic response to paraprobioticGut microbes · 2026Trial
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- Trial
- Longitudinal gut microbiome dynamics are associated with clinical outcome and toxicity during ibrutinib therapy.Gut microbes · 2026Article
- Beyond bacilli: integrating the microbiome into the TB research agenda.Gut microbes · 2026Review
- Antibiotic-associatedGut microbes · 2026Article
- Ecological restructuring of the nonbacterial fecal microbiome in obesity across human cohorts.Gut microbes · 2026Article
- Vitamin B6 produced by gut microbiome regulates host behavioral phenotypes through dopaminergic metabolism.Gut microbes · 2026Article
- A systematic assessment of the short- and long-term effects of commonly used breast cancer chemotherapeutics on the gut microbiome‒blood‒brain axis of female mice.Gut microbes · 2026Article
- Microbiota and head and neck cancers: Mechanistic insights and translational potential.Virulence · 2026Review
- Artificial intelligence in human microbiome research: from data integration to explainable translation.Archives of microbiology · 2026Review
- Distinct Microbiota and Functional Pathway Profiles Define Success and Failure in Regenerative Endodontic Treatment.International endodontic journal · 2026Observational
- Longitudinal Follow-Up of Gut and Salivary Microbiota in Children With Food Protein-Induced Enterocolitis Syndrome.Allergy · 2026Article
- The Adult Aedes aegypti Bacterial Microbiota is Robust to Infection with the Obligate Microsporidian Parasite Edhazardia aedis.Environmental microbiology · 2026Article
- Patterns of Functional Redundancy and Anaerobic Metabolic Potential Characterise the Skin Microbiome of a Critically Endangered Sawfish.Environmental microbiology · 2026Article
- Jasmonic and salicylic acid pathways shape the rhizosphere microbiome, affecting aphid herbivory and soil-mediated insect-plant interactions in a density-dependent manner.Plant & cell physiology · 2026Article
- Fast, flexible analysis of differences in cellular composition with crumblr.Nature communications · 2026Article
- Multi-kingdom gut microbiota analyses identify biomarkers of different types of pediatric short bowel syndrome.mSystems · 2026Article
- Inhaler Devices as Potential Bacterial Reservoirs: A 30-Day Microbiota Analysis in Non-Cystic Fibrosis Bronchiectasis.Biomedicines · 2026Article
- Article
1,235 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of "sampling fraction" and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement.
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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.