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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.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
dc.rightsAttribution 4.0 International
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.
prism.publicationNameScientific reports, volume 9, issue 1

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