Integrating Microbiome Data Visualization into FAIRDatabase using Edge Functions

Integrating Microbiome Data Visualization into FAIRDatabase using Edge Functions | 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 Research Article Integrating Microbiome Data Visualization into FAIRDatabase using Edge Functions Roman van Eldijk, Shivam Kumar, Vivek Sheraton M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8681047/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 research continues to grow, so does the volume of data it produces. Yet privacy constraints on human-associated samples and the compositional nature of sequencing outputs make quick exploratory analysis difficult. This study extends the FAIRDatabase, an open-source, privacy-compliant infrastructure for microbiome data, with a visualization module designed to tackle both challenges. The module performs composition-aware beta diversity analysis using centered log-ratio transformation and the Aitchison distance metric. All computations are run within Supabase edge functions, which makes sure that sensitive data never leave the secure environment. To guide the design, requirements were derived from prior work and literature, covering compositional data analysis, beta diversity visualization, and principles for clear data interpretation. The resulting tool supports interactive heatmaps and Principal Coordinates Analysis (PCoA) plots, with options for metadata-based colouring, variance explained labels, and colour palettes chosen for accessibility and interpretability. In order evaluate the module, three domain experts were to perform tasks and give feedback, which resulted in a mean System Usability Scale score of 84.2. They valued being able to quickly explore data without downloading files or facing contractual obstacles. Overall, this work shows that edge functions can support composition-aware microbiome analysis without compromising data security. It offers a starting point for building privacy-preserving visualization tools in research areas where data sensitivity is a significant concern. Computational Biology Microbiome Data Compositional Data Analysis Edge Functions Beta Diversity FAIR principles Supabase Full Text Additional Declarations The authors declare no competing interests. Supplementary Files supp.pdf Supplementary 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. 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