Constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data

Constrained conditional model for screening marker genes through integrated high-throughput transcriptome big 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 Research Article Constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data Xiaobei Zhou, Jing Wan, Na Lv, Ruiyang Ma, Wensu Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4858125/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 Currently, a large volume of high-throughput transcriptome data are accessible through various public databases. This brings urgent need to integrate transcriptome data sourced from numerous independent studies to extract common characteristics to precisely address specific research questions. Referring to this, we innovatively designed a model framework that leverages integrated transcriptome data with declarative constraints. Here the constraint is used to incorporate the prior knowledge (genes in certain biological pathways, functions or user-specified terms) into the model and drive the model prediction/decision to satisfy these constraints. Distinguishing from existing models or methods, this framework implies tailed non-parametric bootstrapping algorithm to generate millions of samples of p-value of independent analyses to avoid the normal procedure of integrating data with different distribution styles. The model was applied in 5 tumor and 5 non-tumor case studies using 81 downloaded transcriptome datasets with 10,647 samples. High percentage of the selected genes were accordant with published work, co-occurrence results and gene ontology results. Experimental validations including transwell invasion/migration confirmed the identified genes associated with cancer progression in prostate, liver, and endometrial cancer case studies. Therefore our model was effective in extracting marker genes under user-specified conditions and were possessing significance in further understanding specified situations. Constraints marker genes bootstrapping transcriptome data differential gene Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Full Text Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS8.xlsx TableS9.xlsx TableS10.xlsx TableS11.xlsx TableS12.xlsx TableS13.xlsx TableS14.xlsx TableS15.xlsx TableS16.xlsx TableS17.xlsx TableS18.xlsx TableS19.xlsx TableS20.xlsx TableS21.xlsx TableS22.xlsx TableS23.xlsx TableS24.xlsx FigureS1.tif FigureS2.tif FigureS4.tif FigureS5.tif FigureS6.tif FigureS7.tif FigureS8.tif FigureS9.tif FigureS10.tif FigureS11.tif FigureS12.tif FigureS13.tif 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-4858125","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336477049,"identity":"df4207f6-2bef-4987-b8ca-fb8b2e06b49d","order_by":0,"name":"Xiaobei Zhou","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobei","middleName":"","lastName":"Zhou","suffix":""},{"id":336477050,"identity":"d5912470-b250-4655-85bb-16ee4562b3dd","order_by":1,"name":"Jing Wan","email":"","orcid":"","institution":"China Medical 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Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFAC5oYDDBUQpgSRWhiBWs6QqoWBsY0ULQbnDzYe5p1nF21wgPngbR4GuzzCWm4kNhzm3Zacu+EAW7I1D0NyMUEtZjcYQVoOALXwmEnzMBxIbCCo5fxBoJY5IC3834jUcgDksAawLWzEabEH+uXgnGPJuTMPsxlbzjFIJqxFsv/w4Q9vauxy+443P7zxpsKOsBYEYAYRBsSrHwWjYBSMglGABwAAqO9AlE+O4RgAAAAASUVORK5CYII=","orcid":"","institution":"China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wensu","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-08-04 20:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4858125/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4858125/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64277681,"identity":"460eadea-f6cd-41c4-98e8-e0474e902309","added_by":"auto","created_at":"2024-09-11 06:59:04","extension":"png","order_by":1,"title":"Figure 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