Estimating the completeness of large-scale single-cell sequencing projects

Estimating the completeness of large-scale single-cell sequencing projects | 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 Analysis Estimating the completeness of large-scale single-cell sequencing projects Mitro Miihkinen, Yidian Chu, Sara Vakkilainen, Yevhen Akimov, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7298331/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract During embryonic development, cells undergo differentiation into highly specialized cell types. Capitalizing on single-cell RNA sequencing, many initiatives and substantial resources are cataloguing these differentiated cell types by their transcriptomic profiles. Despite the extensive efforts to profile various organs and their cellular compositions, we lack metrics to assess the completeness of the sequencing projects. In this cellular biodiversity analysis, we leveraged the increasingly available single-cell data together with statistical methods, originally developed for assessing the species richness of ecological communities, to estimate the cellular diversity of any organ based on current data from single-cell profiling technologies. Deriving from such cellular richness estimates, we established a practical statistical framework that enables reli- able assessment of the completeness of any large-scale single-cell profiling project, after which additional sequencing efforts do not anymore reveal new insights into an organ’s cellular compo- sition. Such estimates can serve as stoppage-points for the ongoing sequencing projects, hence guiding a more cost-efficient completion of the profiling of various human tissues. Biological sciences/Developmental biology/Differentiation Biological sciences/Ecology Statistical estimator Single-cell RNA sequencing Cellular biodiversity Log-normal distribution Chao2 estimator Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryfilesNatMethods.pdf Supplementary figures Cite Share Download PDF Status: Under Review 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. 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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-7298331","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Analysis","associatedPublications":[],"authors":[{"id":500904890,"identity":"89db1e30-eb50-4f1e-b97a-36271ce8ef46","order_by":0,"name":"Mitro 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