Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations

Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations | 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 Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations Reetika Sarkar, Sithija Manage, Xiaoli Gao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-923319/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 Background : High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including both the Lasso and MCP, and related methods. Result : In this paper, we perform a comparative study of regularization approaches for variable selection under different correlation structures, and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running of a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Conclusion : Both the simulation studies and high-dimensional genomic data analysis have demonstrated the advantage of the proposed rPGBS method over most commonly used regularization methods. In particular, the rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to recent work addressing variable selection with strong correlations. Moreover, the rPGBS is computationally efficient across various settings. Bioinformatics Bi-level sparsity High-dimensional data MCP Stable variable selection Strong correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Full Text Additional Declarations No competing interests reported. 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-923319","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":55101089,"identity":"234b438a-4863-48f8-bc99-16a67906b0da","order_by":0,"name":"Reetika Sarkar","email":"","orcid":"","institution":"University of North Carolina at Greensboro","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Reetika","middleName":"","lastName":"Sarkar","suffix":""},{"id":55101090,"identity":"5937285c-bb29-4ecf-828b-492e306b8dab","order_by":1,"name":"Sithija Manage","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sithija","middleName":"","lastName":"Manage","suffix":""},{"id":55101092,"identity":"269aa0b0-1e80-4896-b93a-2d9e19d801c4","order_by":2,"name":"Xiaoli Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYNCCChsog41oLWfSSNXC2HaYBC0GB9gvfvjAdj5xO/vxCwwfyg4T1iLZwFMsOYPnduLOnpwCxhnniNDCz8CTIM0jcTtxww2eBGbeNiK0sDHwJP/mMTgH0fKXGC38DOzHpHkSDgC1sB9gZiRGC9AvbJYzDiQbbziTw3Cw51w6YS3AEHt84+M/O9kNx48/fPCjzJqwFgb5NwZQFo/BASLUgwD7A3TGKBgFo2AUjAJUAAD32Dxbrni24gAAAABJRU5ErkJggg==","orcid":"","institution":"University of North Carolina at Greensboro","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2021-09-20 15:14:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-923319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-923319/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":14193007,"identity":"bf3834d8-32cf-42d5-b81c-8c7cf6f7bd3b","added_by":"auto","created_at":"2021-10-01 15:26:17","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271200,"visible":true,"origin":"","legend":"Boxplot of all 50 iterations under the PLasso setting in Example 2. Top left: total\naccuracy; Top right: total number of selected active variables; Bottom left: True positive rate;\nBottom right: True negative rate","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-923319/v1/9202a1a8261bb7f604a31ba3.jpg"},{"id":14193449,"identity":"cae6cf79-2807-4514-8973-3120645c31d5","added_by":"auto","created_at":"2021-10-01 15:29:16","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":144065,"visible":true,"origin":"","legend":"ROC curves of rPGBS across all simulation settings\n","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-923319/v1/a6eb1c665a27bcab621cf3d4.jpg"},{"id":14193004,"identity":"e92e462f-5473-4503-8471-dfe278a0727b","added_by":"auto","created_at":"2021-10-01 15:26:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1582464,"visible":true,"origin":"","legend":"Correlation Plot for GSE2990 dataset","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-923319/v1/d183fd5ca9a7648cd561d055.jpg"},{"id":14193006,"identity":"08c9e561-79b1-456c-a357-64805ff5116f","added_by":"auto","created_at":"2021-10-01 15:26:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1180774,"visible":true,"origin":"","legend":"Correlation Plot for Leukemia dataset","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-923319/v1/1a7ee283abb986dddfb4bd91.jpg"},{"id":14193451,"identity":"84db66f9-d455-4c4e-82d3-c9025cd936db","added_by":"auto","created_at":"2021-10-01 15:29:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1155176,"visible":true,"origin":"","legend":"","description":"","filename":"rPGBS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-923319/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-923319/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"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":"Bi-level sparsity, High-dimensional data, MCP, Stable variable selection, Strong correlation","lastPublishedDoi":"10.21203/rs.3.rs-923319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-923319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including both the Lasso and MCP, and related methods.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e: In this paper, we perform a comparative study of regularization approaches for variable selection under different correlation structures, and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running of a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Both the simulation studies and high-dimensional genomic data analysis have demonstrated the advantage of the proposed rPGBS method over most commonly used regularization methods. In particular, the rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to recent work addressing variable selection with strong correlations. 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