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.accessioned2020-02-03T01:45:59Z
dc.date.available2020-02-03T01:45:59Z
dc.date.issued2019-12-30
dc.identifier.issn2045-2322
dc.identifier.otherPMC6937257
dc.identifier.other31889137
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301615
dc.description.abstractIn 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.languageeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2045-2322
dc.sourcenlmid: 101563288
dc.titleUsing transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
dc.typeArticle
dc.date.updated2020-02-03T01:45:59Z
prism.publicationNameScientific reports, volume 9, issue 1
dc.identifier.doi10.17863/CAM.48685
rioxxterms.versionofrecord10.1038/s41598-019-56911-z
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

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