You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data
Contributor:
Mieth, Bettina;
Hockley, James R. F.;
Görnitz, Nico;
Vidovic, Marina M.-C.;
Müller, Klaus-Robert;
Gutteridge, Alex;
Ziemek, Daniel
Published:
Springer Science and Business Media LLC, 2019
Published in:
Scientific Reports, 9 (2019) 1
Language:
English
DOI:
10.1038/s41598-019-56911-z
ISSN:
2045-2322
Origination:
Footnote:
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 athttps://github.com/nicococo/scRNA.