ArticleStat (International Statistical Institute)2018
A Zero-inflated Beta-binomial Model for Microbiome Data Analysis.
Article in Stat (International Statistical Institute), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Trial
- Enhanced modeling approaches for count data analysis with focus on substance use outcomes.Journal of behavioral medicine · 2026Article
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- A comparative analysis of mutual information methods for pairwise relationship detection in metagenomic data.BMC bioinformatics · 2024Article
- A Bayesian zero-inflated beta-binomial model for longitudinal data with group-specific changepoints.Statistics in medicine · 2024Article
- Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data.NAR genomics and bioinformatics · 2023Article
- Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations.Bioengineering (Basel, Switzerland) · 2023Article
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies.Current opinion in plant biology · 2023Review
- Accommodating multiple potential normalizations in microbiome associations studies.BMC bioinformatics · 2023Article
- Modeling County-Level Rare Disease Prevalence Using Bayesian Hierarchical Sampling Weighted Zero-Inflated Regression.Journal of data science : JDS · 2023Article
- Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Machine learning and deep learning applications in microbiome research.ISME communications · 2022Review
- Analyzing the overall effects of the microbiome abundance data with a Bayesian predictive value approach.Statistical methods in medical research · 2022Article
- A comprehensive evaluation of microbial differential abundance analysis methods: current status and potential solutions.Microbiome · 2022Article
- Improve the Colorectal Cancer Diagnosis Using Gut Microbiome Data.Frontiers in molecular biosciences · 2022Article
- Challenges and Opportunities in the Statistical Analysis of Multiplex Immunofluorescence Data.Cancers · 2021Review
- Modeling and analysis of site-specific mutations in cancer identifies known plus putative novel hotspots and bias due to contextual sequences.Computational and structural biotechnology journal · 2020Article
- A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.Frontiers in genetics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The microbiome is increasingly recognized as an important aspect of the health of host species, involved in many biological pathways and processes and potentially useful as health biomarkers. Taking advantage of high-throughput sequencing technologies, modern bacterial microbiome studies are metagenomic, interrogating thousands of taxa simultaneously. Several data analysis frameworks have been proposed for microbiome sequence read count data and determining the most significant features. However, there is still room for improvement. We introduce a zero-inflated beta-binomial (ZIBB) to model the distribution of microbiome count data and to determine association with a continuous or categorical phenotype of interest. The approach can exploit mean-variance relationships to improve power and adjust for covariates. The proposed method is a mixture model with two components: (i) a zero model accounting for excess zeros and (ii) a count model to capture the remaining component by beta-binomial regression, allowing for overdispersion effects. Simulation studies show that our proposed method effectively controls type I error and has higher power than competing methods to detect taxa associated with phenotype. An R package ZIBBSeqDiscovery is available on R CRAN.
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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.