Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
Hockley, James R F
Vidovic, Marina M-C
Scientific reports, volume 9, issue 1
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Mieth, B., Hockley, J. R. F., Görnitz, N., Vidovic, M. M., Müller, K., Gutteridge, A., & Ziemek, D. (2019). Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.. Scientific reports, volume 9, issue 1 https://doi.org/10.1038/s41598-019-56911-z
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.
External DOI: https://doi.org/10.1038/s41598-019-56911-z
This record's URL: https://www.repository.cam.ac.uk/handle/1810/301615
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/