Flexible aggregation of compositional predictors with shared effects for microbiome association analysis

Statistics > Methodology [Submitted on 3 Jun 2024 (v1), last revised 18 Jun 2026 (this version, v3)] Title:Flexible aggregation of compositional predictors with shared effects for microbiome association analysis View PDF HTML (experimental)Abstract:Ongoing advancements in microbiome profiling have provided unprecedented insights into the molecular dynamics of microbial communities, sparking a surge of interest in uncovering the microbiome's critical role in human health. Identifying microbial features linked to clinical outcomes, however, remains challenging due to the high-dimensional, sparse, and compositional nature of microbiome data. Additionally, many microbial taxa, although classified as distinct, may share functional roles, complicating traditional variable selection methods. To overcome these obstacles, we introduce Bayesian Regression with Agglomerated Compositional Effects (BRACE), a novel approach using a spike-and-cluster prior combining Bernoulli activity indicators, an Ewens exchangeable partition prior on the finite active set, and a projection-based constrained Gaussian prior on cluster effects to perform data-adaptive clustering and variable selection. The methodological innovation of our work lies in how we combine the Ewens partition prior with a projection-based constrained Gaussian on the cluster atoms to enforce the sum-to-zero constraint. BRACE groups microbial taxa with similar effects on the outcome, yielding more interpretable models while enabling effective dimension reduction. Through comprehensive simulations and a real-world application examining the influence of oral microbiome composition on insulin resistance, we demonstrate BRACE's superior performance over existing methods, particularly in identifying key features with shared effects on outcomes. Submission history From: Satabdi Saha [view email][v1] Mon, 3 Jun 2024 17:38:24 UTC (375 KB) [v2] Fri, 15 Nov 2024 17:24:07 UTC (444 KB) [v3] Thu, 18 Jun 2026 17:29:43 UTC (149 KB) Current browse context: stat.ME References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.