Correcting Scale Distortion in RNA Sequencing Data

Correcting Scale Distortion in RNA Sequencing 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Article Correcting Scale Distortion in RNA Sequencing Data Christopher Thron, Farhad Jafari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4745774/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2025 Read the published version in BMC Bioinformatics → Version 1 posted 4 You are reading this latest preprint version Abstract RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used in the clinical diagnostic space for cancer patients. While the information gained is intended to impact treatment decisions, numerous technical and quality issues remain. This includes inaccuracies in the dissemination of gene-gene relationships. For such reasons, clinical decisions are still mostly driven by DNA biomarkers, such as gene mutations or fusions. In this study, we aimed to correct for systemic bias based on RNA-sequencing platforms in order to improve our understanding of the gene-gene relationships. To do so, we examined standard pre-processed RNA-seq datasets obtained from three studies conducted by two consortium efforts including The Cancer Genome Atlas (TCGA) and Stand Up 2 Cancer (SU2C). We particularly examined the TCGA Bladder Cancer (n = 408) and Prostate Cancer (n = 498) studies as well as the SU2C Prostate Cancer study (n = 208). Using various statistical tests, in all datasets we detected expression-level dependent biases that differ from sample to sample. Using simulations, we show that these biases corrupt gene-gene correlation estimations and t-tests between subpopulations. To mitigate these biases, we introduce two different nonlinear transforms based on statistical considerations that correct these observed biases. We demonstrate that that these transforms effectively remove the observed per-sample biases, reduce sample-to-sample variance, and improve the characteristics of gene-gene correlation distributions. Using a novel simulation methodology that creates controlled diffferences between subpopulations, we show that these transforms reduce variability and slightly increase sensitivity of two population tests. Altogether, these results improve our capacity to understand gene-gene relationships, and may lead to novel ways to utilize the information derived from clinical tests. RSEM (RNA Sequence by Expectation Maximization) TPM (Transcripts Per Million) FPKM (Fragments Per Kilobase of exon per Million) Local Leveling PCA ROC Curves Populations. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2025 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 30 Jul, 2024 Editor assigned by journal 26 Jul, 2024 Submission checks completed at journal 26 Jul, 2024 First submitted to journal 15 Jul, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4745774","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333751367,"identity":"ebb95b83-70af-49a2-82b2-7d519d9e1663","order_by":0,"name":"Christopher Thron","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYDACCRBRAecyE9bBA9ZyxgBEMjYQr4WxjRQt9tK9Dx8Xzvsjrzsj+fkDhgrrxAaCtsgcNzaeuc3AcNuNNMMGhjPpRGiRSGOT5t1mwLjtRg5jA2PbYWK1zDGwh2j5R7SWBoNEiJYGYrTcSGM25jlmnLztzDPDGQnH0o0JamGfkcb4mKdGznbb8eQHHz7UWMsS1IIKEkhTPgpGwSgYBaMAFwAA/YI5tTy2IlYAAAAASUVORK5CYII=","orcid":"","institution":"Texas A\u0026M University-Central Texas","correspondingAuthor":true,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Thron","suffix":""},{"id":333751368,"identity":"7119b0c1-3baa-4f39-a219-0347f68a3391","order_by":1,"name":"Farhad Jafari","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Jafari","suffix":""}],"badges":[],"createdAt":"2024-07-15 22:26:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4745774/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4745774/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12859-025-06041-3","type":"published","date":"2025-01-28T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75351236,"identity":"1dbb76ba-a133-4b8f-9447-305bf9305306","added_by":"auto","created_at":"2025-02-03 16:08:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1191868,"visible":true,"origin":"","legend":"","description":"","filename":"NonlinearLocalLeveling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4745774/v1_covered_a68b3cdf-1df0-4eaf-856e-1017ea4f02c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correcting Scale Distortion in RNA Sequencing Data","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":"RSEM (RNA Sequence by Expectation Maximization), TPM (Transcripts Per Million), FPKM (Fragments Per Kilobase of exon per Million), Local Leveling, PCA, ROC Curves, Populations.","lastPublishedDoi":"10.21203/rs.3.rs-4745774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4745774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. 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