ArticleNature communications2022
Batch effects removal for microbiome data via conditional quantile regression.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 5 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
96 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Impact of Electronic Cigarette Use on the Oral Microbiota: A Systematic Review.Journal of clinical periodontology · 2026Pooled it
- The respiratory microbiome in pulmonary tuberculosis: a meta-analysis reveals niche-specific microbial and functional signatures.mSystems · 2026Pooled it
- Meta-analysis of gut microbiome reveals patterns of dysbiosis in colorectal cancer patients.Journal of medical microbiology · 2025Pooled it
- Systematic analyses uncover robust salivary microbial signatures and host-microbiome perturbations in oral squamous cell carcinoma.mSystems · 2025Pooled it
- Pooled it
- Microbiome-based therapeutics forGut microbes · 2026Review
- Article
- Humanizing Zophobas morio larvae microbiota for rapid screening of emerging decolonization strategies against multidrug-resistant bacteria.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Decoding the cancer microbiome: multi-omics, AI, and translational opportunities.Genome biology · 2026Review
- Microbiome and aging: Trajectories of microbiome age across human ecosystems and their systemic effects.iMeta · 2026Review
- Longitudinal Exploratory Analysis of Salivary Microbiota Profiles in Patients with Oral Squamous Cell Carcinoma Before and After Surgery: A Pilot Study.International journal of molecular sciences · 2026Article
- IntratumoralJournal for immunotherapy of cancer · 2026Article
- Large temperature excursions have modest impacts on community composition in the high diversity gut microbiome of omnivorous American cockroaches (Microbiology spectrum · 2026Article
- mBatchNet: an interactive web server for diagnosis, correction, and benchmarking of batch effects in microbiome data.Bioinformatics (Oxford, England) · 2026Article
- Prostate Cancer at the Microbial Crossroads: Illuminating a New Frontier in Precision Medicine.The world journal of men's health · 2026Review
- A polymer that reinforces luminal barrier function and attenuates inflammation in murine colitis.Research square · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- Alternating High-Fat and Polysaccharide Diets Modulates Gut Phage-Bacterial Interplay.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.Biomedical engineering online · 2026Review
- A workflow for statistical analysis and visualization of microbiome omics data using the R microeco package.Nature protocols · 2026Review
36 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
18 authors.
Funding
Abstract
Batch effects in microbiome data arise from differential processing of specimens and can lead to spurious findings and obscure true signals. Strategies designed for genomic data to mitigate batch effects usually fail to address the zero-inflated and over-dispersed microbiome data. Most strategies tailored for microbiome data are restricted to association testing or specialized study designs, failing to allow other analytic goals or general designs. Here, we develop the Conditional Quantile Regression (ConQuR) approach to remove microbiome batch effects using a two-part quantile regression model. ConQuR is a comprehensive method that accommodates the complex distributions of microbial read counts by non-parametric modeling, and it generates batch-removed zero-inflated read counts that can be used in and benefit usual subsequent analyses. We apply ConQuR to simulated and real microbiome datasets and demonstrate its advantages in removing batch effects while preserving the signals of interest.
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What OpenQuestion holds
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.