Show simple item record

dc.contributor.authorMieth, Bettina
dc.contributor.authorHockley, James R. F.
dc.contributor.authorGörnitz, Nico
dc.contributor.authorVidovic, Marina M.-C.
dc.contributor.authorMüller, Klaus-Robert
dc.contributor.authorGutteridge, Alex
dc.contributor.authorZiemek, Daniel
dc.date.accessioned2020-12-29T16:18:00Z
dc.date.available2020-12-29T16:18:00Z
dc.date.issued2019-12-30
dc.date.submitted2019-04-29
dc.identifier.others41598-019-56911-z
dc.identifier.other56911
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/315640
dc.descriptionFunder: Technische Universität Berlin (TU Berlin); doi: https://doi.org/10.13039/501100006764
dc.descriptionFunder: - German Federal Ministry for Education and Research through the Berlin Big Data Centre (01IS14013A), the Berlin Center for Machine Learning (01IS18037I) and the TraMeExCo project (01IS18056A). Partial funding by DFG is acknowledged (EXC 2046/1, project-ID: 390685689) - Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779)
dc.descriptionFunder: GlaxoSmithKline (GlaxoSmithKline plc.); doi: https://doi.org/10.13039/100004330
dc.descriptionFunder: Pfizer (Pfizer Inc.); doi: https://doi.org/10.13039/100004319
dc.description.abstractAbstract: In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA.
dc.languageen
dc.publisherNature Publishing Group UK
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectArticle
dc.subject/631/114/2404
dc.subject/631/114/1305
dc.subject/631/208/514/1949
dc.subject/38
dc.subject/38/91
dc.subjectarticle
dc.titleUsing transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data
dc.typeArticle
dc.date.updated2020-12-29T16:18:00Z
prism.issueIdentifier1
prism.publicationNameScientific Reports
prism.volume9
dc.identifier.doi10.17863/CAM.62747
dcterms.dateAccepted2019-12-13
rioxxterms.versionofrecord10.1038/s41598-019-56911-z
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.identifier.eissn2045-2322


Files in this item

Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

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