Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views

Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views | 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 Clustering of Cancer Data Based on Stiefel Manifold for Multiple Views Jing Tian, Jianping Zhao, Chun-hou Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-154286/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of these datasets has proved to be valuable for biological and medical research and helpful to reveal data structures from multiple collections. However, such data often have small sample size and high dimension. It is difficult to find a suitable integration method for structural analysis of multiple datasets. Results: In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, the optimization problem solved by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion: Particularly, our approach has better performs when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time. Bioinformatics Stiefel manifold multi-view clustering cancer data optimization model linear search algorithm Figures Figure 1 Figure 2 Figure 3 Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 14 Mar, 2021 Reviews received at journal 24 Feb, 2021 Reviewers agreed at journal 17 Feb, 2021 Reviewers agreed at journal 16 Feb, 2021 Reviewers invited by journal 10 Feb, 2021 Editor assigned by journal 10 Feb, 2021 Editor invited by journal 10 Feb, 2021 Submission checks completed at journal 10 Feb, 2021 First submitted to journal 24 Jan, 2021 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-154286","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":11241639,"identity":"4b9ebdf7-c5ec-4581-95a5-cf0db5fc0f38","order_by":0,"name":"Jing Tian","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Tian","suffix":""},{"id":11241640,"identity":"106f6c9b-54ea-488d-9fd3-1df7b0c43801","order_by":1,"name":"Jianping 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