Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix | 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 Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix Chenwei Tang, Quan Sun, Xinyue Zeng, Gang Li, Xiaoyu Yang, Fei Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4979032/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Genome Biology → Version 1 posted 14 You are reading this latest preprint version Abstract Cell type-specific (CTS) analysis is crucial for uncovering biological insights hidden in bulk tissue data, yet single-cell (sc) or single-nuclei (sn) approaches are often cost-prohibitive for large samples. We introduce EPIC-unmix, a novel two-step empirical Bayesian method combining reference sc/sn and bulk RNA-seq data to improve CTS inference, accounting for the difference between reference and target datasets. Under comprehensive simulations, EPIC-unmix outperformed alternative methods in accuracy. Applied to Alzheimer's disease (AD) brain RNA-seq data, EPIC-unmix identified multiple differentially expressed genes in a CTS manner, and empowered CTS eQTL analysis. Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementalFigures.pdf SupplementalTablefinal.xlsx Cite Share Download PDF Status: Published Journal Publication published 20 Nov, 2025 Read the published version in Genome Biology → Version 1 posted Editorial decision: Revision requested 24 Mar, 2025 Reviews received at journal 14 Mar, 2025 Reviews received at journal 09 Mar, 2025 Reviews received at journal 06 Mar, 2025 Reviewers agreed at journal 23 Feb, 2025 Reviewers agreed at journal 23 Feb, 2025 Reviewers agreed at journal 18 Feb, 2025 Reviewers agreed at journal 17 Sep, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers invited by journal 12 Sep, 2024 Editor invited by journal 02 Sep, 2024 Editor assigned by journal 30 Aug, 2024 Submission checks completed at journal 27 Aug, 2024 First submitted to journal 26 Aug, 2024 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-4979032","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":357490519,"identity":"8031fe74-34de-4dee-8df9-7b13fea82284","order_by":0,"name":"Chenwei Tang","email":"","orcid":"","institution":"University of North Carolina at Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Chenwei","middleName":"","lastName":"Tang","suffix":""},{"id":357490524,"identity":"48f9c66e-d40c-4ec3-b444-f871ce417088","order_by":1,"name":"Quan Sun","email":"","orcid":"","institution":"University of North Carolina at Chapel 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