Comparative Analysis of Six Correlation Metrics on Identifying DNA Co-Methylation Patterns

Comparative Analysis of Six Correlation Metrics on Identifying DNA Co-Methylation Patterns | 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 Case Report Comparative Analysis of Six Correlation Metrics on Identifying DNA Co-Methylation Patterns Mayla Ward, Neo Eloff, Shuying Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5883112/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 DNA methylation is an important epigenetic event and is significantly associated with cancer. Different genomic sites tend to be co-methylated. It is unclear which correlation metrics should be used to study co-methylation and how different metrics perform in identifying highly co-methylated (HCM) sites. It is also unclear the impact of different features of the data, e.g., outlier, low variance, and data transformation (from B to M = logit(B)). We therefore conducted comprehensive analyses of six metrics, Pearson, Spearman, Kendall, Hoeffding, Distance, and Maximal Information Coefficient (MIC). Key findings are summarized below. First, the runtime of the six metrics drastically differed and increased with sample size, especially for MIC. Spearman and Pearson were much faster (with no missing data). Second, the numbers of HCM sites identified by the six metrics were very different. Pearson and Distance both identified more HCM sites and had strong similarities. However, these metrics were susceptible to outliers and data transformation. They identified more highly co-methylated sites when using B values, but these sites tended to have outliers and lower variance. Third, Kendall and Hoeffding's scores were significantly lower than other metrics’ correlation coefficients, making it difficult to identify HCM sites without a proper cutoff. Fourth, MIC required a large sample size to perform properly. Although it may detect unique correlation patterns, it is difficult to interpret these patterns biologically. Finally, considering all factors together (runtime, outlier, low variance, and data transformation), Spearman is relatively better for co-methylation analysis with no missing data. Correlation Methylation Co-methylation Cancer Full Text Additional Declarations No competing interests reported. Supplementary Files Sup.File.v2.No.Documentation.Nov30.2024.5p.pdf 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-5883112","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":445808530,"identity":"158dd97c-b119-4a21-a051-82a2b73686db","order_by":0,"name":"Mayla Ward","email":"","orcid":"","institution":"Western Washington University","correspondingAuthor":false,"prefix":"","firstName":"Mayla","middleName":"","lastName":"Ward","suffix":""},{"id":445808532,"identity":"e63b9b1d-2c56-4312-8648-ddb7f6ce52c0","order_by":1,"name":"Neo Eloff","email":"","orcid":"","institution":"Texas State University","correspondingAuthor":false,"prefix":"","firstName":"Neo","middleName":"","lastName":"Eloff","suffix":""},{"id":445808536,"identity":"06cdc5ef-8f7c-47cc-a1c0-88eac2e72b0f","order_by":2,"name":"Shuying Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYLCCDwgmM3E6GGckMDDwQFQTqYWZhyQt8v1nzB7b/jhsb89+/uADhgrrxAZCWgxu5Jgb5ySkJfbwJDMbMJxJJ0KLBI+ZdE6CTQIPQzKbBGPbYcJaQA6TtkiQsOfhf8z+g/EfEVoYDuSYSTMk2DD2SCSzMTA2EKHF4EZamWRPGtAvNx4bSyQcSzcmwmGHt0n8sDlsz96f+PDDhxprWcIOQwEJpCkfBaNgFIyCUYALAAAMJzYbRPIsYwAAAABJRU5ErkJggg==","orcid":"","institution":"Texas State University","correspondingAuthor":true,"prefix":"","firstName":"Shuying","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-01-22 18:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5883112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5883112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82484419,"identity":"f78d5d41-2704-4ee1-850e-24fe49e7c8ef","added_by":"auto","created_at":"2025-05-12 04:46:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1530638,"visible":true,"origin":"","legend":"","description":"","filename":"2.NoColor.Cor.ms.35p.Apr16W.2025.v3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5883112/v1_covered_2eadeafa-95db-46e3-9412-6566363b9cbb.pdf"},{"id":81096106,"identity":"48d60229-acfd-48e1-967d-24f92c351a4a","added_by":"auto","created_at":"2025-04-22 08:00:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2674787,"visible":true,"origin":"","legend":"","description":"","filename":"Sup.File.v2.No.Documentation.Nov30.2024.5p.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5883112/v1/c1b924330c195a163b83ec66.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Analysis of Six Correlation Metrics on Identifying DNA Co-Methylation Patterns","fulltext":[],"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":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":"Correlation, Methylation, Co-methylation, Cancer","lastPublishedDoi":"10.21203/rs.3.rs-5883112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5883112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDNA methylation is an important epigenetic event and is significantly associated with cancer. Different genomic sites tend to be co-methylated. It is unclear which correlation metrics should be used to study co-methylation and how different metrics perform in identifying highly co-methylated (HCM) sites. It is also unclear the impact of different features of the data, e.g., outlier, low variance, and data transformation (from B to M\u0026thinsp;=\u0026thinsp;logit(B)). We therefore conducted comprehensive analyses of six metrics, Pearson, Spearman, Kendall, Hoeffding, Distance, and Maximal Information Coefficient (MIC). Key findings are summarized below. First, the runtime of the six metrics drastically differed and increased with sample size, especially for MIC. Spearman and Pearson were much faster (with no missing data). Second, the numbers of HCM sites identified by the six metrics were very different. Pearson and Distance both identified more HCM sites and had strong similarities. However, these metrics were susceptible to outliers and data transformation. They identified more highly co-methylated sites when using B values, but these sites tended to have outliers and lower variance. Third, Kendall and Hoeffding's scores were significantly lower than other metrics\u0026rsquo; correlation coefficients, making it difficult to identify HCM sites without a proper cutoff. Fourth, MIC required a large sample size to perform properly. Although it may detect unique correlation patterns, it is difficult to interpret these patterns biologically. Finally, considering all factors together (runtime, outlier, low variance, and data transformation), Spearman is relatively better for co-methylation analysis with no missing data.\u003c/p\u003e","manuscriptTitle":"Comparative Analysis of Six Correlation Metrics on Identifying DNA Co-Methylation Patterns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-22 08:00:50","doi":"10.21203/rs.3.rs-5883112/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"342d2df4-b55f-4f74-a7be-6dce3a90a9ad","owner":[],"postedDate":"April 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T04:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-22 08:00:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5883112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5883112","identity":"rs-5883112","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}