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dc.contributor.authorNgo Trong, Trung
dc.contributor.authorMehtonen, Juha
dc.contributor.authorGonzález, Geraldo
dc.contributor.authorKramer, Roger
dc.contributor.authorHautamäki, Ville
dc.contributor.authorHeinäniemi, Merja
dc.date.accessioned2020-01-23T07:53:16Z
dc.date.available2020-01-23T07:53:16Z
dc.date.issued2019
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/7964
dc.description.abstractSingle-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single-cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian deep learning approach, where protein quantification, available as CITE-seq counts, from the same cells is used to constrain the learning process, thus forming a SemI-SUpervised generative Autoencoder (SISUA) model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture.
dc.language.isoenglanti
dc.publisherMary Ann Liebert Inc
dc.relation.ispartofseriesJournal of computational biology
dc.relation.urihttp://dx.doi.org/10.1089/cmb.2019.0337
dc.rightsCC BY http://creativecommons.org/licenses/by/4.0/
dc.subjectautoencoder
dc.subjectdeep learning
dc.subjectgenerative
dc.subjectprotein
dc.subjectsemisupervised
dc.subjectsingle-cell
dc.subjectvariational
dc.titleSemisupervised Generative Autoencoder for Single-Cell Data
dc.description.versionpublished version
dc.contributor.departmentSchool of Computing, activities
dc.contributor.departmentSchool of Medicine / Biomedicine
uef.solecris.id66953881en
dc.type.publicationTieteelliset aikakauslehtiartikkelit
dc.rights.accessrights© Authors
dc.relation.doi10.1089/cmb.2019.0337
dc.description.reviewstatuspeerReviewed
dc.relation.issn1066-5277
dc.relation.volume[Published online: 2 December 2019]
dc.rights.accesslevelopenAccess
dc.type.okmA1
uef.solecris.openaccessHybridijulkaisukanavassa ilmestynyt avoin julkaisu


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