Improving data interpretability with new differential sample variance gene set tests

Improving data interpretability with new differential sample variance gene set tests | 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 Improving data interpretability with new differential sample variance gene set tests Yasir Rahmatallah, Galina Glazko This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4888767/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2025 Read the published version in BMC Bioinformatics → Version 1 posted 12 You are reading this latest preprint version Abstract Background Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes. Results We use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature. Conclusions The results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets. Gene set analysis differential variability minimum spanning tree Anderson-Darling Cramer-Von Mises Full Text Additional Declarations No competing interests reported. Supplementary Files AdditionalFile1.pdf Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2025 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 17 Sep, 2024 Reviews received at journal 06 Sep, 2024 Reviews received at journal 25 Aug, 2024 Reviews received at journal 23 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviewers invited by journal 18 Aug, 2024 Editor invited by journal 14 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 09 Aug, 2024 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. 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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-4888767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350580357,"identity":"101389b0-f3db-48f6-a261-0cdd8d8abb61","order_by":0,"name":"Yasir Rahmatallah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACNjDJxw5kfIAKSRChxYCBjZmBjXEGyVqYeYjRYnD78LNHN2r+yLMxMz97bNtmFy3fwHzwNg8+LefSzI1zjhkYtjGzmRvntiXnbjjAlmyNV8sZBjPpHDYDxjZmICO3jTl3AwOPmTR+LezfpHP+Gdi3MQMZlm31ufMb+L8R0MIDMtwgsY0ZyGBsO5zbcICHDa8WyTM8ZdK5fcbJQC1lkj3njuduOMxmbDkHjxa+M+zbpHO+ydn2s7dvk/hRVp07v7354Y03eLQoHMAQYsajHATkGwgoGAWjYBSMglHAAAB6dULMy4g5xQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yasir","middleName":"","lastName":"Rahmatallah","suffix":""},{"id":350580358,"identity":"55897053-e64f-4e03-a459-ff4d889f5c0b","order_by":1,"name":"Galina Glazko","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Galina","middleName":"","lastName":"Glazko","suffix":""}],"badges":[],"createdAt":"2024-08-09 18:54:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4888767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4888767/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12859-025-06117-0","type":"published","date":"2025-04-14T15:57:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81051495,"identity":"0262558f-2525-4b24-b0a7-bf6032348794","added_by":"auto","created_at":"2025-04-21 16:10:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1412707,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4888767/v1_covered_dea39651-b582-4aab-a2d7-bdae7d5c3007.pdf"},{"id":64156351,"identity":"38580bda-ba77-479c-80bf-b94e185af91a","added_by":"auto","created_at":"2024-09-09 05:25:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":669386,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4888767/v1/b2431aac52caa2bf207dce3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving data interpretability with new differential sample variance gene set tests","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":"Gene set analysis, differential variability, minimum spanning tree, Anderson-Darling, Cramer-Von Mises","lastPublishedDoi":"10.21203/rs.3.rs-4888767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4888767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.\u003c/p\u003e","manuscriptTitle":"Improving data interpretability with new differential sample variance gene set tests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-09 05:25:47","doi":"10.21203/rs.3.rs-4888767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-17T11:08:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-06T14:54:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-25T07:46:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-23T19:25:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274350894973255265380623211575796603937","date":"2024-08-19T19:24:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322550876896319902446639364085186390176","date":"2024-08-19T13:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305582236987535266456681969889071225449","date":"2024-08-18T19:31:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-18T18:56:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-14T11:18:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-13T03:03:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T03:02:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2024-08-09T18:52:32+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":"010e5596-df32-42f2-ad5d-8e594f65d45d","owner":[],"postedDate":"September 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-04-21T16:09:53+00:00","versionOfRecord":{"articleIdentity":"rs-4888767","link":"https://doi.org/10.1186/s12859-025-06117-0","journal":{"identity":"bmc-bioinformatics","isVorOnly":false,"title":"BMC Bioinformatics"},"publishedOn":"2025-04-14 15:57:51","publishedOnDateReadable":"April 14th, 2025"},"versionCreatedAt":"2024-09-09 05:25:47","video":"","vorDoi":"10.1186/s12859-025-06117-0","vorDoiUrl":"https://doi.org/10.1186/s12859-025-06117-0","workflowStages":[]},"version":"v1","identity":"rs-4888767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4888767","identity":"rs-4888767","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}