Multi-view graph learning for deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data

Multi-view graph learning for deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq 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 Multi-view graph learning for deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data Boya Ji, Xiaoqi Wang, Xiang Wang, Liwen Xu, Shaoliang Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3937029/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 Cell-cell communications (CCCs) from multiple sender cells collaboratively affect downstream functional events in receiver cells, thus influencing cell phenotype and function. How to rank the importance of these CCCs and find the dominant ones in a specific downstream functional event has great significance for deciphering various physiological and pathogenic processes. To date, several computational methods have been developed to focus on the identification of cell types that communicate with enriched ligand-receptor interactions from single-cell RNA-seq (scRNA-seq) data, but to the best of our knowledge, all of them lack the ability to identify the communicating cell type pairs that play a major role in a specific downstream functional event, which we call it "dominant cell communication assembly (DCA)". Here, we proposed scDCA, a multi-view graph learning method for deciphering DCA from scRNA-seq data. scDCA is based on a multi-view CCC network by constructing different cell type combinations at single-cell resolution. Multi-view graph convolution network was further employed to reconstruct the expression pattern of target genes or the functional states of receiver cells. The DCA was subsequently identified by interpreting the model with the attention mechanism. scDCA was verified in a real scRNA-seq cohort of advanced renal cell carcinoma, accurately deciphering the DCA that affect the expression patterns of the critical immune genes and functional states of malignant cells. Furthermore, scDCA also accurately explored the alteration in cell communication under clinical intervention by comparing the DCA for certain cytotoxic factors between patients with and without immunotherapy. scDCA is free available at: https://github.com/pengsl-lab/scDCA.git. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Cell biology/Cell signalling cell-cell communication multi-view graph convolution network single-cell RNA sequencing deep learning model interpretability Full Text Additional Declarations There is NO Competing Interest. Supplementary Files supp.pdf 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. 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-3937029","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":277467311,"identity":"97a4ae37-9bb1-4338-992f-8a0687a2d7fa","order_by":0,"name":"Boya Ji","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYBACAwaGxMcQZgLxWpKNSdbCJk2aFnOJhGfVhTsOM/Cz5xgw/NxBhBbLGQlpt2eeOcwg2fPGgLH3DDEOuwHUwtt2GMjIMWBmbCNSSzFIiz1JWpjBtkgQq8Wy50GyNG9bOo/EmWcFB3uJ0WLOnpP4mbfNWo6/PXnjg5/EaGEQyEkAUTwg4gAxGhgY+I8TqXAUjIJRMApGLgAA7Ckzvt5v0Z4AAAAASUVORK5CYII=","orcid":"","institution":"Hunan University","correspondingAuthor":true,"prefix":"","firstName":"Boya","middleName":"","lastName":"Ji","suffix":""},{"id":277467312,"identity":"df575a08-40d6-4bcf-9dd5-ace8786ea148","order_by":1,"name":"Xiaoqi Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqi","middleName":"","lastName":"Wang","suffix":""},{"id":277467313,"identity":"1eb4da9d-be58-490b-969f-47e69b5b830a","order_by":2,"name":"Xiang Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Wang","suffix":""},{"id":277467314,"identity":"bb1ea4e0-598e-41d4-83db-ae5544f8bfdc","order_by":3,"name":"Liwen Xu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Liwen","middleName":"","lastName":"Xu","suffix":""},{"id":277467315,"identity":"e6e5585a-33b3-4c35-bc66-b4010ba05a73","order_by":4,"name":"Shaoliang Peng","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shaoliang","middleName":"","lastName":"Peng","suffix":""}],"badges":[],"createdAt":"2024-02-07 13:45:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3937029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3937029/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56184814,"identity":"328b02c4-40d4-4881-9125-109af5fceddc","added_by":"auto","created_at":"2024-05-09 15:09:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18314091,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937029/v1_covered_40b38f66-80d5-4935-b937-096f3e44a1da.pdf"},{"id":52472320,"identity":"23874744-08e5-4739-8bc3-5ff813163970","added_by":"auto","created_at":"2024-03-12 03:46:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2466633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"supp.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3937029/v1/915c77389de52262a20f6b49.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Multi-view graph learning for deciphering the dominant cell communication assembly of downstream functional events from single-cell RNA-seq data","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":"cell-cell communication, multi-view graph convolution network, single-cell RNA sequencing, deep learning, model interpretability","lastPublishedDoi":"10.21203/rs.3.rs-3937029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3937029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCell-cell communications (CCCs) from multiple sender cells collaboratively affect downstream functional events in receiver cells, thus influencing cell phenotype and function. 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