More Like this

1. Analyzing microbiome data with taxonomic misclassification using a zero-inflated Dirichlet-multinomial model

   https://doi.org/10.1186/s12859-025-06078-4  

   Koslovsky, Matthew D (December 2025, BMC Bioinformatics)

   Domany, Eytan; Setubal, Joao; Stoye, Jens; Vert, Jean-Philippe (Ed.)

   The human microbiome is the collection of microorganisms living on and inside of our bodies. A major aim of microbiome research is understanding the role microbial communities play in human health with the goal of designing personalized interventions that modulate the microbiome to treat or prevent disease. Microbiome data are challenging to analyze due to their high-dimensionality, overdispersion, and zero-inflation. Analysis is further complicated by the steps taken to collect and process microbiome samples. For example, sequencing instruments have a fixed capacity for the total number of reads delivered. It is therefore essential to treat microbial samples as compositional. Another complicating factor of modeling microbiome data is that taxa counts are subject to measurement error introduced at various stages of the measurement protocol. Advances in sequencing technology and preprocessing pipelines coupled with our growing knowledge of the human microbiome have reduced, but not eliminated, measurement error. Ignoring measurement error during analysis, though common in practice, can then lead to biased inference and curb reproducibility. We propose a Dirichlet-multinomial modeling framework for microbiome data with excess zeros and potential taxonomic misclassification. We demonstrate how accommodating taxonomic misclassification improves estimation performance and investigate differences in gut microbial composition between healthy and obese children. 

   more » « less
2. A Bayesian Functional Concurrent Zero-Inflated Dirichlet-Multinomial Regression Model with Application to Infant Microbiome

   Erlandson, Brody; Wilson, Ander; Koslovsky, Matthew D (May 2026, Biostatistics)

   The infant microbiome undergoes rapid changes in composition over time and is associated with long-term risks of conditions such as immune strength, allergy, asthma, and other health outcomes. Modeling the associations between exposures or treatments and microbial composition over time is essential for understanding the factors that drive these changes. Estimating these temporal dynamics has several challenges including: repeated measures, overdispersion, compositionality, high-dimensional parameter spaces, and zero-inflation. Many longitudinal regression models used in human microbiome research assume constant effects over time that cannot capture time-varying or functional effects of exposures, ignore the compositional structure of the data by modeling each taxon separately, and are not equipped to handle potential zero-inflation. Dirichlet-multinomial (DM) regression models inherently accommodate overdispersion and the compositional structure of the data and have been extended to account for excess zeros. However, existing DM-based regression models are unable to additionally handle repeated measures designs. To fill this gap, we propose a functional concurrent zero-inflated Dirichlet-multinomial (FunC-ZIDM) regression model which is designed to model time-varying relations between observed covariates and microbial taxa while accounting for zero-inflation, compositionality, and repeated measures. Through simulation, we demonstrate that the model can accurately estimate the underlying functional relations and scale to large compositional spaces. We apply our model to investigate time-varying associations between infant microbiome composition and observed covariates during the 11-week postnatal period. We found that alpha-diversity (i.e., the diversity of the microbiome within an individual) is positively associated with a higher gestational age and percentage of breast milk in the diet. We provide an accompanying R package and shiny app to implement the method and generate plots. 

   more » « less
3. Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization

   https://doi.org/10.1093/biostatistics/kxab002  

   Zhou, Fangting; He, Kejun; Li, Qiwei; Chapkin, Robert S; Ni, Yang (February 2021, Biostatistics)

   null (Ed.)

   Summary 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. 

   more » « less
4. 2-Way k-Means as a Model for Microbiome Samples

   Weston J. Jackson, Ipsita Agarwal (July 2017, Journal of healthcare engineering)

   icrobiome sequencing allows defining clusters of samples with shared composition. However, this paradigm poorly accounts for samples whose composition is a mixture of cluster- characterizing ones, and therefore lie in-between them in cluster space. This paper addresses unsupervised learning of 2-way clusters. It defines a mixture model that allows 2-way cluster assignment and describes a variant of generalized k-means for learning such a model. We demonstrate applicability to microbial 16S rDNA sequencing data from the Human Vaginal Microbiome Project. 

   more » « less
5. The role of the microbiome in human disease

   Flowers, L. (February 2023, Biomedical journal of scientific technical research)

   Since the invention of the microscope, scientists have described microbial communities on living and non-living matter. In terms of human-associated microbes, scientists have documented the beneficial effects of the microbiota for many decades. Prophylactic effects include protection from pathogens, digestion potential, and the production of essential vitamins. However, recent high-throughput methodologies and analytical advances have accelerated microbiome science and our understanding of microbial diversity in living organisms. The microbiome denotes the complex network of all the microorganisms and microbial genes located in specific biotic or abiotic environments. We now realize the enormous diversity and functionality of the microbiota in humans and the endless benefits to health and disease. Dysbiosis facilitates the manufacture of various proinflammatory mediators, biochemical imbalances, and colonization of microbes associated with disease outcomes. Additional work is necessary to determine whether changes in the human microbiome are due to anthropogenic, genetic, or environmental variations. This review will present microbiome research studies focusing on human disease. The findings documented in this article offer optimism on the profound role microorganisms play in supporting human health and how pharmaceutical interactions targeting specific microbes can decrease the incidence of human disease caused by the ecological disturbance of the normal microbiota. 

   more » « less