Transcriptome Graph Transformer--A Graph Transformer-Based Unsupervised Model for Transcriptome Data Analysis

Transcriptome Graph Transformer--A Graph Transformer-Based Unsupervised Model for Transcriptome Data 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 Transcriptome Graph Transformer--A Graph Transformer-Based Unsupervised Model for Transcriptome Data Analysis Teng Long, Sachit Satyal, Jean Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8244458/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Rapidly growing transcriptomic datasets pose challenges for traditional analytical methods, which struggle with high dimensionality, heterogeneity, and nonlinear gene relationships. Existing deep learning models often require fixed-length inputs and fail to integrate biological network information. Methods: We introduce Transcriptome Graph Transformer (TGT), an unsupervised graph Transformer framework that constructs a heterogeneous gene--pathway graph using expression data, STRING interactions, and GO/KEGG/Reactome pathway annotations. TGT is pretrained with a masked-node prediction task and fine-tuned for disease classification, biomarker discovery, and zero-shot clustering of single-cell and spatial transcriptomics. Results: TGT achieves superior performance across Alzheimer's disease, cancer, acute kidney injury, and COVID-19 datasets, outperforming state-of-the-art baselines. The model generalizes well across platforms and yields biologically meaningful gene and pathway importance scores consistent with known disease mechanisms. Conclusion: TGT provides an effective and generalizable approach for transcriptomic representation learning by integrating biological network knowledge with graph Transformer architectures. Its strong performance highlights its utility for broad transcriptomic applications and precision medicine. Graph transformer Gene expression analysis Unsupervised learning Biomarker discovery Disease classification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Feb, 2026 Reviews received at journal 17 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 18 Dec, 2025 Editor invited by journal 17 Dec, 2025 Submission checks completed at journal 17 Dec, 2025 First submitted to journal 17 Dec, 2025 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-8244458","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571535299,"identity":"410cb7fa-335c-41ec-8c4f-a37cd9a82581","order_by":0,"name":"Teng Long","email":"","orcid":"","institution":"The University of Texas at Arlington","correspondingAuthor":false,"prefix":"","firstName":"Teng","middleName":"","lastName":"Long","suffix":""},{"id":571535301,"identity":"7f938933-557d-452c-b124-99e711436082","order_by":1,"name":"Sachit Satyal","email":"","orcid":"","institution":"The University of Texas at 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Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Graph transformer, Gene expression analysis, Unsupervised learning, Biomarker discovery, Disease classification","lastPublishedDoi":"10.21203/rs.3.rs-8244458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8244458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eRapidly growing transcriptomic datasets pose challenges for traditional analytical methods, which struggle with high dimensionality, heterogeneity, and nonlinear gene relationships. Existing deep learning models often require fixed-length inputs and fail to integrate biological network information.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe introduce Transcriptome Graph Transformer (TGT), an unsupervised graph Transformer framework that constructs a heterogeneous gene--pathway graph using expression data, STRING interactions, and GO/KEGG/Reactome pathway annotations. TGT is pretrained with a masked-node prediction task and fine-tuned for disease classification, biomarker discovery, and zero-shot clustering of single-cell and spatial transcriptomics.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eTGT achieves superior performance across Alzheimer's disease, cancer, acute kidney injury, and COVID-19 datasets, outperforming state-of-the-art baselines. The model generalizes well across platforms and yields biologically meaningful gene and pathway importance scores consistent with known disease mechanisms.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e\u003cp\u003eTGT provides an effective and generalizable approach for transcriptomic representation learning by integrating biological network knowledge with graph Transformer architectures. Its strong performance highlights its utility for broad transcriptomic applications and precision medicine.\u003c/p\u003e","manuscriptTitle":"Transcriptome Graph Transformer--A Graph Transformer-Based Unsupervised Model for Transcriptome Data Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 16:45:53","doi":"10.21203/rs.3.rs-8244458/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-03T10:50:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-18T02:26:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T15:21:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315929828850552331552381993051603422713","date":"2026-01-13T01:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50707140783840920381497493973289850663","date":"2026-01-08T13:53:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T15:52:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-18T14:24:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-17T14:07:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-17T06:00:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2025-12-17T05:57:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca6b7680-df2f-4545-9d85-160aec067548","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T07:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 16:45:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8244458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8244458","identity":"rs-8244458","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}