DECODE: Deep learning-based common deconvolution framework for various omics data

DECODE: Deep learning-based common deconvolution framework for various omics 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 Article DECODE: Deep learning-based common deconvolution framework for various omics data Yadong Wang, Tianyi Zhao, Renjie Liu, Yuzhi Sun, Liyuan Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6004616/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Nature Methods → Version 1 posted You are reading this latest preprint version Abstract Deconvolution algorithm enables estimation of cell type abundances from tissue-level data, providing a crucial way for exploring plentiful cohort data at the cellular level. However, most deconvolution algorithms are specifically designed for single-omics data, thereby limiting their generalizability and scalability for multiomics data from different cohorts. A deconvolution algorithm applicable to various omics data can use cell abundance as a bridge to improve the comparability of different cohorts. Here, we developed DECODE, a universal deconvolution framework of both cell type and cell state designed for transcriptomics, proteomics and metabolomics data, which seamlessly integrates diverse multiomics tissue datasets in cellular level. DECODE fills the gap in metabolomics deconvolution and significantly outperformed state-of-the-art methods on different omics data across donors, disease conditions, healthy states, and measurement platforms. In addition, DECODE exhibits high robustness in scenarios that are closer to real applications, that it can accurately deconvolve known cell types even when the reference single-cell data incomplete all cell types of target tissue. DECODE will serve as a powerful tool for the fully extending multiomics cohort data into cellular level. Biological sciences/Computational biology and bioinformatics/Proteome informatics Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.pdf Supplementary Figures for Experiment Results Cite Share Download PDF Status: Published Journal Publication published 02 Mar, 2026 Read the published version in Nature Methods → 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-6004616","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":420288571,"identity":"1dfbc3e3-f693-4877-b5a4-f7dbe02a7422","order_by":0,"name":"Yadong Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYFCCBBBhk8AGonhI0JKWwMZGopbDCQxEazE4nmP4ueDX+Tw++QbGB2/bGOTNCWo588ZYembf7WKgw5gN57YxGO5sIKTlRu4Gad6e24ltbAxs0rxtDAkGBwhr2fybt+ccSAv7b2K1bJPm+XEAbAszUVokz7z/Zs3bkAzUktgsOeechOEGQlr4jqcl3+b5Y5c4v/nwwQ9vymzkCdqiAFLA2AZiMjYACQkC6oFAHqSO4Q9hhaNgFIyCUTCCAQA5CEDDlOsZfQAAAABJRU5ErkJggg==","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yadong","middleName":"","lastName":"Wang","suffix":""},{"id":420288572,"identity":"cc022532-15f4-4b3c-b03f-d61d546ec644","order_by":1,"name":"Tianyi Zhao","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Tianyi","middleName":"","lastName":"Zhao","suffix":""},{"id":420288573,"identity":"874d9b61-8899-4384-a33b-10f35a886350","order_by":2,"name":"Renjie Liu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Renjie","middleName":"","lastName":"Liu","suffix":""},{"id":420288574,"identity":"33ee5364-1262-4e04-974e-bdd1979cfcc9","order_by":3,"name":"Yuzhi Sun","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuzhi","middleName":"","lastName":"Sun","suffix":""},{"id":420288575,"identity":"91b25601-64dd-4e57-ada1-8524d9b34ebf","order_by":4,"name":"Liyuan Zhang","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Liyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":420288576,"identity":"edbe9914-ca4e-48d2-9ee8-7fbb95d103c3","order_by":5,"name":"Ruibang Luo","email":"","orcid":"https://orcid.org/0000-0001-9711-6533","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Ruibang","middleName":"","lastName":"Luo","suffix":""},{"id":420288577,"identity":"a01dc778-5e4e-4792-a81b-c44daa147355","order_by":6,"name":"Liang Cheng","email":"","orcid":"","institution":"Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-02-11 07:20:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6004616/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6004616/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41592-026-03007-y","type":"published","date":"2026-03-02T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103810810,"identity":"488f3890-7d7a-4fd8-a0ab-4fcfa57b9c90","added_by":"auto","created_at":"2026-03-03 08:12:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1785139,"visible":true,"origin":"","legend":"Article File","description":"","filename":"DECODE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6004616/v1_covered_528d96c9-2f5a-4d3c-b79c-3397118fa60d.pdf"},{"id":77189047,"identity":"5fff0c49-e382-47aa-88ca-905defaff40c","added_by":"auto","created_at":"2025-02-26 04:39:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1096916,"visible":true,"origin":"","legend":"Supplementary Figures for Experiment Results","description":"","filename":"Supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6004616/v1/ea3f8111a6f3255583cb7895.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"DECODE: Deep learning-based common deconvolution framework for various omics data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6004616/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6004616/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Deconvolution algorithm enables estimation of cell type abundances from tissue-level data, providing a crucial way for exploring plentiful cohort data at the cellular level. However, most deconvolution algorithms are specifically designed for single-omics data, thereby limiting their generalizability and scalability for multiomics data from different cohorts. A deconvolution algorithm applicable to various omics data can use cell abundance as a bridge to improve the comparability of different cohorts. Here, we developed DECODE, a universal deconvolution framework of both cell type and cell state designed for transcriptomics, proteomics and metabolomics data, which seamlessly integrates diverse multiomics tissue datasets in cellular level. DECODE fills the gap in metabolomics deconvolution and significantly outperformed state-of-the-art methods on different omics data across donors, disease conditions, healthy states, and measurement platforms. In addition, DECODE exhibits high robustness in scenarios that are closer to real applications, that it can accurately deconvolve known cell types even when the reference single-cell data incomplete all cell types of target tissue. DECODE will serve as a powerful tool for the fully extending multiomics cohort data into cellular level.","manuscriptTitle":"DECODE: Deep learning-based common deconvolution framework for various omics data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-26 04:39:50","doi":"10.21203/rs.3.rs-6004616/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-methods","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nmeth","sideBox":"Learn more about [Nature Methods](http://www.nature.com/nmeth)","snPcode":"","submissionUrl":"","title":"Nature Methods","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eedf787c-f3bc-46cb-9e89-6636d8719f84","owner":[],"postedDate":"February 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":44803180,"name":"Biological sciences/Computational biology and bioinformatics/Proteome informatics"},{"id":44803181,"name":"Biological sciences/Computational biology and bioinformatics/Machine learning"}],"tags":[],"updatedAt":"2026-03-03T08:12:02+00:00","versionOfRecord":{"articleIdentity":"rs-6004616","link":"https://doi.org/10.1038/s41592-026-03007-y","journal":{"identity":"nature-methods","isVorOnly":false,"title":"Nature Methods"},"publishedOn":"2026-03-02 05:00:00","publishedOnDateReadable":"March 2nd, 2026"},"versionCreatedAt":"2025-02-26 04:39:50","video":"","vorDoi":"10.1038/s41592-026-03007-y","vorDoiUrl":"https://doi.org/10.1038/s41592-026-03007-y","workflowStages":[]},"version":"v1","identity":"rs-6004616","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6004616","identity":"rs-6004616","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}