ArticleBriefings in bioinformatics2023
Benchmarking differential abundance analysis methods for correlated microbiome sequencing data.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Personalized Diets Based on Microbial Characteristics between Patients with Superficial Gastritis and Atrophic Gastritis.Nutrients · 2023Pooled it
- Gut microbial dynamics and antibiotic resistance during rifaximin therapy in severe cirrhosis: a ProPILA-Rifax ancillary study.Frontiers in cellular and infection microbiology · 2026Trial
- metaFun: An analysis pipeline for metagenomic big data with fast and unified functional searches.Gut microbes · 2026Article
- Article
- Translating Gut Microbiota into Diagnostics: A Multidimensional Approach for the Diagnosis of Inflammatory Bowel Disease.Gut and liver · 2026Review
- A data-driven universal gut microbiome health assessment: a machine learning framework trained on large metagenomic data.Frontiers in microbiology · 2026Article
- SIPdb: a stable isotope probing database and analytical dashboard for linking amplicon sequences to microbial activity using reverse ecology.ISME communications · 2026Article
- Microbiome differential abundance methodologies to detect relevant taxa associated with chemotherapy toxicity rate in colorectal cancer.Bioinformatics advances · 2026Article
- Infection with gut parasites correlates with gut microbiome diversity across human populations in Africa.Gut microbes · 2025Article
- A new scenario of pathogen-microbiota interactions involving the oomycete Plasmopara viticola.FEMS microbiology ecology · 2025Article
- BMDD: A probabilistic framework for accurate imputation of zero-inflated microbiome sequencing data.PLoS computational biology · 2025Article
- Urinary tract infections in postmenopausal women revisited (UTIr): a prospective observational cohort study to explore the urobiomes of postmenopausal women with and without recurrent urinary tract infections.BMC infectious diseases · 2025Observational
- Article
- DiSC: a statistical tool for fast differential expression analysis of individual-level single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2025Article
- From air to insight: the evolution of airborne DNA sequencing technologies.Microbiology (Reading, England) · 2025Review
- Revealing microbial consortia that interfere with grapevine downy mildew through microbiome epidemiology.Environmental microbiome · 2025Article
- gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes.Computational and structural biotechnology journal · 2025Article
- Next-generation data filtering in the genomics era.Nature reviews. Genetics · 2024Review
- A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies.Genome biology · 2024Article
- The Beneficial Effects ofInternational journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.
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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.