scDFN: Enhancing single-cell RNA-seq Clustering with Deep Fusion Networks

scDFN: Enhancing single-cell RNA-seq Clustering with Deep Fusion Networks | 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 scDFN: Enhancing single-cell RNA-seq Clustering with Deep Fusion Networks Tianxiang Liu, Yue Bi, Xudong Guo, Quan Zou, Cangzhi Jia, Fuyi Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4356835/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 Single-cell RNA sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. Although clustering plays a key role in the subsequent analysis of single-cell transcriptomics, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. In this study, we introduced a novel deep learning-based algorithm for single-cell clustering, designated scFCN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scFCN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scFCN, as determined by better Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI) metrics. Additionally, scFCN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scFCN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics. The source code of scFCN is publicly available at https://github.com/11051911/scDFN . Single-cell RNA sequencing Clustering Deep Learning Clustering Full Text Additional Declarations No competing interests reported. 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. 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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-4356835","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299764571,"identity":"b0ce1fd9-2c4e-422a-b848-70de60d589d9","order_by":0,"name":"Tianxiang Liu","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Tianxiang","middleName":"","lastName":"Liu","suffix":""},{"id":299764573,"identity":"a5117404-f55b-4f3a-bdc2-c8a2bfb6fa59","order_by":1,"name":"Yue Bi","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Bi","suffix":""},{"id":299764575,"identity":"7315e051-6e42-46be-8b9d-6a1428a1e16e","order_by":2,"name":"Xudong Guo","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Guo","suffix":""},{"id":299764577,"identity":"b63b0bd9-1291-4485-8757-7583a866c22e","order_by":3,"name":"Quan Zou","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Quan","middleName":"","lastName":"Zou","suffix":""},{"id":299764578,"identity":"2b84e632-46c8-4ea8-bcfd-c58cc8666897","order_by":4,"name":"Cangzhi Jia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3PMQuCQBTA8SeCLtKtr6W+woUgQlBfRRFquaJPELXoUvRZIpDGwvXaHW0RAocc3bozGtPagu4/PDi4H3cPQKX61bQV6gboJ8zqo/cxMTz0viByWvQzQqJLcquOrtkxeZn6LAFiMgrV8T1BPp+4XS4+Zs0Orh8n0N0UVNvy94QCc+gglLvMYpSEpozqWthASOFQXxJS5DUZtxJkdnaWBJnxfAVbCKaFo61rkttil6mFPF+ctw2E7JhdVuEy6O+Ca1rGwx6Jgn1WNRCRgWIEr5Mlx6kRAOh3MUYtl1QqleqfewCQc0wY6ubBeAAAAABJRU5ErkJggg==","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":true,"prefix":"","firstName":"Cangzhi","middleName":"","lastName":"Jia","suffix":""},{"id":299764579,"identity":"57f1c4cc-b27a-4560-9d85-84272774b19f","order_by":5,"name":"Fuyi Li","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Fuyi","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-05-02 04:54:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4356835/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4356835/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56067141,"identity":"f73941f3-af54-4be7-9c1a-6c1a3055ad0c","added_by":"auto","created_at":"2024-05-08 06:34:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1380756,"visible":true,"origin":"","legend":"","description":"","filename":"scDFNSub.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4356835/v1_covered_4c0f34c3-d261-414e-baef-319128e2c618.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"scDFN: Enhancing single-cell RNA-seq Clustering with Deep Fusion Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Single-cell RNA sequencing, Clustering, Deep Learning, Clustering","lastPublishedDoi":"10.21203/rs.3.rs-4356835/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4356835/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. 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