scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data

scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell 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 Article scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data Ziqi Zhang, Xinye Zhao, Peng Qiu, Xiuwei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2969323/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographic groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effect and meaningful condition effect, while perturbation prediction methods solely focus on condition effect, resulting in inaccurate gene expression predictions due to unaccounted batch effect. Here we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effect from batch effect, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluated scDisInFact on both simulated and real datasets, and compared its performance with baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data. Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data mining Biological sciences/Computational biology and bioinformatics/Data integration Full Text Additional Declarations There is NO Competing Interest. Supplementary Files suppmaterial.pdf Supplementary Figures and Tables Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2024 Read the published version in Nature Communications → 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-2969323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":205151840,"identity":"cd80cea4-58e7-4586-854d-9d04c28e9f72","order_by":0,"name":"Ziqi Zhang","email":"","orcid":"https://orcid.org/0000-0002-8198-0260","institution":"Georgia Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Zhang","suffix":""},{"id":205151841,"identity":"2cb4341f-8114-405e-a99c-1f1502645757","order_by":1,"name":"Xinye Zhao","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinye","middleName":"","lastName":"Zhao","suffix":""},{"id":205151842,"identity":"767aa4e3-b16a-4e1c-b9d0-356f80d8e114","order_by":2,"name":"Peng Qiu","email":"","orcid":"https://orcid.org/0000-0003-3256-0734","institution":"Georgia Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Qiu","suffix":""},{"id":205151843,"identity":"a9c14c21-5e00-496b-9e48-8ee420deff5c","order_by":3,"name":"Xiuwei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3PMWvCQBTA8XccxOUk67tv8UrgRJTmqxQK7RKlkx0EKwi6XDvne/QLpGRwUbMKXQod63BjWlIwSXU8zVjw/iQHCe93xwG4XP8xXi1UPwCm/iXK12tIWAyA58mxinDRhNBSKDQPEHZa6/Sr/3M98VvrBMwotRI5E0rGBLyrh3e9wcstSj28YfHGTnwu1Lsg8CiJVDDQHGkriLfnduJVpCAQlO1U0NVPGFbk9wSpTynvXm4eBZ+Qp0hYEnaCyJn3+K0JibY7xZ6nSxmvInrTm3sroSx9pbzoh5RFgcmLse8vVlcf+ahnJYewvhey+d9ncm7+GDdQNJ11uVyuS2oPS2BMk/Za3jQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1713-772X","institution":"Georgia Institute of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiuwei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2023-05-23 05:00:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2969323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2969323/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-45227-w","type":"published","date":"2024-01-30T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50415573,"identity":"99751a33-e991-4b59-be67-23db00083af9","added_by":"auto","created_at":"2024-01-31 08:13:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3799946,"visible":true,"origin":"","legend":"Article File","description":"","filename":"scDisInFactmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2969323/v1_covered_8b515376-2a2b-4e87-beeb-80f294120051.pdf"},{"id":38744275,"identity":"f7eb64d9-0041-4a76-8290-91c26f49a62f","added_by":"auto","created_at":"2023-06-19 06:21:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6482764,"visible":true,"origin":"","legend":"Supplementary Figures and Tables","description":"","filename":"suppmaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2969323/v1/c61e6d3db22986fa0835a322.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2969323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2969323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Single-cell RNA-sequencing (scRNA-seq) has been widely used for disease studies, where sample batches are collected from donors under different conditions including demographic groups, disease stages, and drug treatments. It is worth noting that the differences among sample batches in such a study are a mixture of technical confounders caused by batch effect and biological variations caused by condition effect. However, current batch effect removal methods often eliminate both technical batch effect and meaningful condition effect, while perturbation prediction methods solely focus on condition effect, resulting in inaccurate gene expression predictions due to unaccounted batch effect.\r\n\r\nHere we introduce scDisInFact, a deep learning framework that models both batch effect and condition effect in scRNA-seq data. scDisInFact learns latent factors that disentangle condition effect from batch effect, enabling it to simultaneously perform three tasks: batch effect removal, condition-associated key gene detection, and perturbation prediction. We evaluated scDisInFact on both simulated and real datasets, and compared its performance with baseline methods for each task. Our results demonstrate that scDisInFact outperforms existing methods that focus on individual tasks, providing a more comprehensive and accurate approach for integrating and predicting multi-batch multi-condition single-cell RNA-sequencing data.","manuscriptTitle":"scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-19 06:21:28","doi":"10.21203/rs.3.rs-2969323/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d4e8d29d-ec93-4c61-923a-4d696a0709f2","owner":[],"postedDate":"June 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":21940040,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":21940041,"name":"Biological sciences/Computational biology and bioinformatics/Data mining"},{"id":21940042,"name":"Biological sciences/Computational biology and bioinformatics/Data integration"}],"tags":[],"updatedAt":"2024-01-31T08:13:11+00:00","versionOfRecord":{"articleIdentity":"rs-2969323","link":"https://doi.org/10.1038/s41467-024-45227-w","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-01-30 05:00:00","publishedOnDateReadable":"January 30th, 2024"},"versionCreatedAt":"2023-06-19 06:21:28","video":"","vorDoi":"10.1038/s41467-024-45227-w","vorDoiUrl":"https://doi.org/10.1038/s41467-024-45227-w","workflowStages":[]},"version":"v1","identity":"rs-2969323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2969323","identity":"rs-2969323","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}