ArticleBiostatistics (Oxford, England)2022
Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization.
Article in Biostatistics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 22 citations in OpenAlex.
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- A Generalized Bayesian Stochastic Block Model for Microbiome Community Detection.Statistics in medicine · 2025Article
- An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Multi-way overlapping clustering by Bayesian tensor decomposition.Statistics and its interface · 2024Article
- A Unified Bayesian Framework for Bi-overlapping-Clustering Multi-omics Data via Sparse Matrix Factorization.Statistics in biosciences · 2023Article
- Probiotics (Lactobacillus plantarum HNU082) Supplementation Relieves Ulcerative Colitis by Affecting Intestinal Barrier Functions, Immunity-Related Gene Expression, Gut Microbiota, and Metabolic Pathways in Mice.Microbiology spectrum · 2022Article
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- Host Genome-Metagenome Analyses Using Combinatorial Network Methods Reveal Key Metagenomic and Host Genetic Features for Methane Emission and Feed Efficiency in Cattle.Frontiers in genetics · 2022Article
- Healthcare Biclustering-Based Prediction on Gene Expression Dataset.BioMed research international · 2022Article
- Xylo-Oligosaccharides in Prevention of Hepatic Steatosis and Adipose Tissue Inflammation: Associating Taxonomic and Metabolomic Patterns in Fecal Microbiomes with Biclustering.International journal of environmental research and public health · 2021Article
- Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.Frontiers in microbiology · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
High-throughput sequencing technology provides unprecedented opportunities to quantitatively explore human gut microbiome and its relation to diseases. Microbiome data are compositional, sparse, noisy, and heterogeneous, which pose serious challenges for statistical modeling. We propose an identifiable Bayesian multinomial matrix factorization model to infer overlapping clusters on both microbes and hosts. The proposed method represents the observed over-dispersed zero-inflated count matrix as Dirichlet-multinomial mixtures on which latent cluster structures are built hierarchically. Under the Bayesian framework, the number of clusters is automatically determined and available information from a taxonomic rank tree of microbes is naturally incorporated, which greatly improves the interpretability of our findings. We demonstrate the utility of the proposed approach by comparing to alternative methods in simulations. An application to a human gut microbiome data set involving patients with inflammatory bowel disease reveals interesting clusters, which contain bacteria families Bacteroidaceae, Bifidobacteriaceae, Enterobacteriaceae, Fusobacteriaceae, Lachnospiraceae, Ruminococcaceae, Pasteurellaceae, and Porphyromonadaceae that are known to be related to the inflammatory bowel disease and its subtypes according to biological literature. Our findings can help generate potential hypotheses for future investigation of the heterogeneity of the human gut microbiome.
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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.