Correlation measures in metagenomic data: the blessing of dimensionality

Correlation measures in metagenomic data: the blessing of dimensionality | 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 Article Correlation measures in metagenomic data: the blessing of dimensionality Alessandro Fuschi, Alessandra Merlotti, Thi Dong Binh Tran, Hoan Nguyen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5573104/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 Microbiome analysis has revolutionised our understanding of various biological processes, spanning human health, epidemiology (including antimicrobial resistance and horizontal gene transfer), as well as environmental and agricultural studies. At the heart of microbiome analysis lies the characterization of microbial communities through the quantification of microbial taxa and their dynamics. In the study of bacterial abundances, it is becoming more relevant to consider their relationship, to embed these data in the framework of network theory, allowing characterization of features like node relevance, pathway and community structure. In this study, we address the primary biases encountered in reconstructing networks through correlation measures, particularly considering the compositional nature of the data, within-sample diversity, and the presence of a high number of unobserved species. These factors can lead to inaccurate correlation estimates. To tackle these challenges, we employ simulated data to demonstrate how many of these issues can be mitigated by applying typical transformations designed for compositional data. These transformations enable the use of straightforward measures like Pearson's correlation to correctly identify positive and negative relationships among relative abundances, especially in high-dimensional data, without having any need for further corrections. However, some challenges persist, such as addressing data sparsity, as neglecting this aspect can result in an underestimation of negative correlations. Biological sciences/Systems biology/Computer science Physical sciences/Physics/Biological physics Full Text Additional Declarations No competing interests reported. Supplementary Files SUPPLblessingdimensionality.pdf 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. 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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-5573104","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":393268676,"identity":"16aba9b7-fad4-4cae-8207-e86f0afb6d84","order_by":0,"name":"Alessandro Fuschi","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Fuschi","suffix":""},{"id":393268677,"identity":"02d55289-f3c8-4521-b4d9-686b81711145","order_by":1,"name":"Alessandra Merlotti","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Alessandra","middleName":"","lastName":"Merlotti","suffix":""},{"id":393268678,"identity":"10ae79c9-e5da-48d2-abe6-fab3014db783","order_by":2,"name":"Thi Dong Binh Tran","email":"","orcid":"","institution":"The Jackson Laboratory for Genomic Medicine","correspondingAuthor":false,"prefix":"","firstName":"Thi","middleName":"Dong Binh","lastName":"Tran","suffix":""},{"id":393268679,"identity":"b0a4a981-6259-4318-b7d4-033808f64376","order_by":3,"name":"Hoan Nguyen","email":"","orcid":"","institution":"The Jackson Laboratory for Genomic Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hoan","middleName":"","lastName":"Nguyen","suffix":""},{"id":393268680,"identity":"0487408d-307d-4485-acf5-661741e899a8","order_by":4,"name":"George M. 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