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dc.contributor.authorArgelaguet, Ricard
dc.contributor.authorArnol, Damien
dc.contributor.authorBredikhin, Danila
dc.contributor.authorDeloro, Yonatan
dc.contributor.authorVelten, Britta
dc.contributor.authorMarioni, John C
dc.contributor.authorStegle, Oliver
dc.date.accessioned2021-01-26T00:31:42Z
dc.date.available2021-01-26T00:31:42Z
dc.date.issued2020-05-11
dc.identifier.issn1474-7596
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/316699
dc.description.abstractTechnological advances have enabled the profiling of multiple molecular layers at single-cell resolution, assaying cells from multiple samples or conditions. Consequently, there is a growing need for computational strategies to analyze data from complex experimental designs that include multiple data modalities and multiple groups of samples. We present Multi-Omics Factor Analysis v2 (MOFA+), a statistical framework for the comprehensive and scalable integration of single-cell multi-modal data. MOFA+ reconstructs a low-dimensional representation of the data using computationally efficient variational inference and supports flexible sparsity constraints, allowing to jointly model variation across multiple sample groups and data modalities.
dc.format.mediumElectronic
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectFrontal Lobe
dc.subjectAnimals
dc.subjectMice
dc.subjectFactor Analysis, Statistical
dc.subjectSequence Analysis, RNA
dc.subjectDNA Methylation
dc.subjectEmbryonic Development
dc.subjectSingle-Cell Analysis
dc.titleMOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data.
dc.typeArticle
prism.issueIdentifier1
prism.publicationDate2020
prism.publicationNameGenome Biol
prism.startingPage111
prism.volume21
dc.identifier.doi10.17863/CAM.63812
dcterms.dateAccepted2020-04-13
rioxxterms.versionofrecord10.1186/s13059-020-02015-1
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2020-05-11
dc.contributor.orcidMarioni, John [0000-0001-9092-0852]
dc.identifier.eissn1474-760X
rioxxterms.typeJournal Article/Review
pubs.funder-project-idCancer Research UK (22231)
cam.issuedOnline2020-05-11


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International