mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data

mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data Xiaoxiu Tan, Feng Xue, Chenhong Zhang, Tao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4005898/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Alterations in human microbial communities are intricately linked to the onset and progression of diseases. Identifying the key microbes driving these community changes is crucial, as they may serve as valuable biomarkers for disease prevention, diagnosis, and treatment. However, there remains a need for further research to develop effective methods for addressing this critical task. This is primarily because defining the driver microbe requires consideration not only of each microbe’s individual contributions but also their interactions. Results This paper introduces a novel framework, called mbDriver, for identifying driver microbes based on microbiome abundance data collected at discrete time points. mbDriver comprises three main components: (1) data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution, (2) parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares, and (3) quantification of each microbe’s contribution to the community’s steady state by manipulating the causal graph implied by gLV equations. The performance of nonparametric spline-based denoising and regularized least squares estimation is comprehensively evaluated on simulated datasets, demonstrating superiority over existing methods. Furthermore, the practical applicability and effectiveness of mbDriver are showcased using a dietary fiber intervention dataset and an ulcerative colitis dataset. Notably, driver microbes identified in the dietary fiber intervention dataset exhibit significant effects on the abundances of short-chain fatty acids, while those identified in the ulcerative colitis dataset show a significant correlation with metabolism-related pathways. Conclusions mbDriver holds promise for studying the dynamics of human microbiota and predicting microbiota-based targets for disease treatment. Full Text Additional Declarations No competing interests reported. Supplementary Files SuppmbDriver.pdf Supplementarytable3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4005898","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":276122130,"identity":"65a9d0b6-36f0-4925-8421-cdaf752543cd","order_by":0,"name":"Xiaoxiu Tan","email":"","orcid":"","institution":"Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiu","middleName":"","lastName":"Tan","suffix":""},{"id":276122133,"identity":"6ca31ac3-bec5-4869-ba4b-53f2feecef61","order_by":1,"name":"Feng Xue","email":"","orcid":"","institution":"Shanghai Jiao Tong 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This is primarily because defining the driver microbe requires consideration not only of each microbe\u0026rsquo;s individual contributions but also their interactions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis paper introduces a novel framework, called mbDriver, for identifying driver microbes based on microbiome abundance data collected at discrete time points. mbDriver comprises three main components: (1) data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution, (2) parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares, and (3) quantification of each microbe\u0026rsquo;s contribution to the community\u0026rsquo;s steady state by manipulating the causal graph implied by gLV equations. 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