Multiscale Cell-Cell Interactive Spatial Transcriptomics Analysis

Multiscale Cell-Cell Interactive Spatial Transcriptomics Analysis | 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 Multiscale Cell-Cell Interactive Spatial Transcriptomics Analysis Sean Cottrell, Guo-Wei Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5743704/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 Spatial transcriptomics data analysis integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer cell-type dynamics, and detect gene expression patterns within tissues. However, the current spatial transcriptomics analysis neglects the multiscale cell-cell interactions that are crucial in biology. To fill this gap, we propose multiscale cell-cell interactive spatial transcriptomics (MCIST) analysis. MCIST combines the advantages of an ensemble of multiscale topological representations of cell-cell interactions in the gene expression space with those of cutting edge spatial deep learning techniques. We validate MCIST by a comparison of 14 cutting edge methods on a huge collection of 37 benchmark spatial transcriptomics datasets. We demonstrate that MCIST yields superior performance in spatial domain detection. It achieves the best clustering score on 23/37 datasets and is among the top three methods on 33/37 datasets, whereas the second best method scored only 6/37 and 17/37 on these measures, respectively. In terms of overall performance with regards to a quantitative metric, MCIST offers over an 11\% improvement to the previous state-of-the-art in spatial domain detection. Additionally, MCIST offers multiscale insights with respect to trajectory inference, differentially expressed gene detection, and signaling pathway enrichment analysis. Our MCIST sheds valuable light on the multiscale perspective in spatial transcriptomics. Bioinformatics Spatial Transcriptomics Multiscale cell-cell interactions Persistent Laplacian Deep Learning Full Text Additional Declarations The authors declare no competing interests. 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. 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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-5743704","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396278946,"identity":"931aecce-1c3e-4f2c-a602-1a108506bf07","order_by":0,"name":"Sean Cottrell","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sean","middleName":"","lastName":"Cottrell","suffix":""},{"id":396278947,"identity":"92e75400-4779-4146-8e86-b09ebbfe94a1","order_by":1,"name":"Guo-Wei Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYFACxgaGDxCWAfFaGGeQqIWBgZmHJC0Gx5ubX9vm1CU2sDdvkyBOy5mDbda52w4nNvAcKyNOi9mNxDbj3G0HEhskcsyI1HL/YZux5Tagw+TfEKvlBmPzY8ZtzEBbeIjUYn8msY2xd9th4zaetGILorRIth9//OHntjrZfvbDG28QpQUI2MDuYSNWOQgwfyBF9SgYBaNgFIxAAACV+i/6MOdakwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Guo-Wei","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-12-31 20:10:43","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5743704/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5743704/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72857538,"identity":"90eeb6f8-13a4-4167-866e-42dec3f46960","added_by":"auto","created_at":"2025-01-03 02:36:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6202266,"visible":true,"origin":"","legend":"","description":"","filename":"mcser.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5743704/v1_covered_31f9006e-f0ba-436d-b4af-999f2a4a55be.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMultiscale Cell-Cell Interactive Spatial Transcriptomics Analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Michigan State University","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":"Spatial Transcriptomics, Multiscale cell-cell interactions, Persistent Laplacian, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-5743704/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5743704/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpatial transcriptomics data analysis \u0026nbsp;integrates gene expression profiles with their corresponding spatial locations to identify spatial domains, infer \u0026nbsp;cell-type dynamics, and detect \u0026nbsp;gene expression patterns within tissues. However, the current spatial transcriptomics \u0026nbsp;analysis neglects the multiscale cell-cell interactions that are crucial in biology. To fill this gap, we propose \u0026nbsp;multiscale cell-cell interactive spatial transcriptomics (MCIST) analysis. MCIST combines the advantages of an ensemble of multiscale topological representations of cell-cell interactions in the gene expression space \u0026nbsp;with those of cutting edge spatial deep learning techniques. We validate MCIST by a comparison of 14 cutting edge methods on a huge collection of 37 benchmark spatial transcriptomics datasets. We demonstrate that MCIST yields superior performance in spatial domain detection. It \u0026nbsp;achieves the best clustering score on 23/37 datasets and is among the top three methods on 33/37 datasets, whereas the second best method scored only 6/37 and 17/37 on these measures, respectively. 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