A Dirichlet-multinomial mixed model for detecting differential abundance of mutational signatures

A Dirichlet-multinomial mixed model for detecting differential abundance of mutational signatures | 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 A Dirichlet-multinomial mixed model for detecting differential abundance of mutational signatures Lena Morrill Gavarro ́, Dominique-Laurent Couturier, Florian Markowetz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4700438/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Feb, 2025 Read the published version in BMC Bioinformatics → Version 1 posted 4 You are reading this latest preprint version Abstract Mutational processes of diverse origin leave their imprints in the genome during tumour evolution. These imprints are called mutational signatures and they have been characterised for point mutations, structural variants and copy number changes. Each signature has an exposure, or abundance, per sample, which indicates how much a process has contributed to the overall genomic change. Mutational processes are not static, and a better understanding of their dynamics is key to characterise tumour evolution and identify cancer weaknesses that can be exploited during treatment. However, the structure of the data typically collected in this context makes it difficult to test whether signature exposures differ between samples or time-points. In general, the data consist of (1) patient-dependent vectors of counts for each sample and clonality group (2) generated from a covariate-dependent and compositional vector of probabilities with (3) a possibly group-dependent over-dispersion level. To model these data, we build on the Dirichlet-multinomial model to be able to model multivariate overdispersed vectors of counts as well as within-sample dependence and positive correlations between signatures. To estimate the model parameters, we implement a maximum likelihood estimator with a Laplace approximation of the random effect high-dimensional integrals and assess its bias and coverage by means of Monte Carlo simulations. We apply our approach to characterise differences of mutational processes between clonal and subclonal mutations across 23 cancer types of the PCAWG cohort. We find ubiquitous differential abundance of clonal and subclonal signatures across cancer types, and higher dispersion of signatures in the subclonal group, indicating higher variability between patients at subclonal level, possibly due to the presence of different clones with distinct active mutational processes. Mutational signature analysis is an expanding field and we envision our framework to be used widely to detect global changes in mutational process activity. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Feb, 2025 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 15 Jul, 2024 Editor assigned by journal 13 Jul, 2024 Submission checks completed at journal 13 Jul, 2024 First submitted to journal 07 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. 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-4700438","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":327198915,"identity":"780461c7-46ae-45b8-bd13-f842eb7d14d0","order_by":0,"name":"Lena Morrill Gavarro ́","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Lena","middleName":"Morrill Gavarro","lastName":"́","suffix":""},{"id":327198916,"identity":"41a0f78f-be65-40ac-a3e6-1e55a0f3cb17","order_by":1,"name":"Dominique-Laurent Couturier","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Dominique-Laurent","middleName":"","lastName":"Couturier","suffix":""},{"id":327198917,"identity":"75c268cb-b19f-440f-ae22-bfc99a0518bd","order_by":2,"name":"Florian Markowetz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBAC9gb2g4//VMD5CYS18BzgSTbgOUOaFgYzCd42krSIHUiQkJxnJ6/bwPzwA2NbGhFapBMPGBhuSzbcdoDNWIKxLYewFnvphISExG0HGLcBXcjA2FZBWAuPdILBgYNzDthvO8D+jWgtho2NDQeAFvGAbCHCYTzSOcnMDMeSk7cd5imWSDhHlPfTj/9mqLGz3Xa8feOHD2XJhLUgADMDUbEyCkbBKBgFo4AYAAAiXzbJhOyCxAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Cambridge","correspondingAuthor":true,"prefix":"","firstName":"Florian","middleName":"","lastName":"Markowetz","suffix":""}],"badges":[],"createdAt":"2024-07-07 13:36:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4700438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4700438/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12859-025-06055-x","type":"published","date":"2025-02-18T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":77052673,"identity":"1fe7351c-275a-466c-bb8b-942c47b8ab91","added_by":"auto","created_at":"2025-02-24 16:22:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2419441,"visible":true,"origin":"","legend":"","description":"","filename":"BMCBioinformaticsMorrilletalv6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4700438/v1_covered_1333651a-03d8-46e1-aa15-93d22275db1b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Dirichlet-multinomial mixed model for detecting differential abundance of mutational signatures","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":"","lastPublishedDoi":"10.21203/rs.3.rs-4700438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4700438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mutational processes of diverse origin leave their imprints in the genome during tumour evolution. These imprints are called mutational signatures and they have been characterised for point mutations, structural variants and copy number changes. Each signature has an exposure, or abundance, per sample, which indicates how much a process has contributed to the overall genomic change. Mutational processes are not static, and a better understanding of their dynamics is key to characterise tumour evolution and identify cancer weaknesses that can be exploited during treatment. However, the structure of the data typically collected in this context makes it difficult to test whether signature exposures differ between samples or time-points. In general, the data consist of (1) patient-dependent vectors of counts for each sample and clonality group (2) generated from a covariate-dependent and compositional vector of probabilities with (3) a possibly group-dependent over-dispersion level. To model these data, we build on the Dirichlet-multinomial model to be able to model multivariate overdispersed vectors of counts as well as within-sample dependence and positive correlations between signatures. To estimate the model parameters, we implement a maximum likelihood estimator with a Laplace approximation of the random effect high-dimensional integrals and assess its bias and coverage by means of Monte Carlo simulations. We apply our approach to characterise differences of mutational processes between clonal and subclonal mutations across 23 cancer types of the PCAWG cohort. We find ubiquitous differential abundance of clonal and subclonal signatures across cancer types, and higher dispersion of signatures in the subclonal group, indicating higher variability between patients at subclonal level, possibly due to the presence of different clones with distinct active mutational processes. Mutational signature analysis is an expanding field and we envision our framework to be used widely to detect global changes in mutational process activity.","manuscriptTitle":"A Dirichlet-multinomial mixed model for detecting differential abundance of mutational signatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-06 04:27:54","doi":"10.21203/rs.3.rs-4700438/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-15T14:41:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-13T04:56:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-13T04:54:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2024-07-07T13:34:18+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":"6099f340-8a59-4183-bbbc-38995a082b91","owner":[],"postedDate":"August 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:03:46+00:00","versionOfRecord":{"articleIdentity":"rs-4700438","link":"https://doi.org/10.1186/s12859-025-06055-x","journal":{"identity":"bmc-bioinformatics","isVorOnly":false,"title":"BMC Bioinformatics"},"publishedOn":"2025-02-18 15:57:04","publishedOnDateReadable":"February 18th, 2025"},"versionCreatedAt":"2024-08-06 04:27:54","video":"","vorDoi":"10.1186/s12859-025-06055-x","vorDoiUrl":"https://doi.org/10.1186/s12859-025-06055-x","workflowStages":[]},"version":"v1","identity":"rs-4700438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4700438","identity":"rs-4700438","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}