Abstract
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.
| Original language | English |
|---|---|
| Article number | 20353 |
| Journal | Scientific reports |
| Volume | 9 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2019 Dec 1 |
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ASJC Scopus subject areas
- General
Cite this
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data. / Mieth, Bettina; Hockley, James R.F.; Görnitz, Nico; Vidovic, Marina M.C.; Müller, Klaus Robert; Gutteridge, Alex; Ziemek, Daniel.
In: Scientific reports, Vol. 9, No. 1, 20353, 01.12.2019.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data
AU - Mieth, Bettina
AU - Hockley, James R.F.
AU - Görnitz, Nico
AU - Vidovic, Marina M.C.
AU - Müller, Klaus Robert
AU - Gutteridge, Alex
AU - Ziemek, Daniel
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41598-019-56911-z
DO - 10.1038/s41598-019-56911-z
M3 - Article
C2 - 31889137
AN - SCOPUS:85077220581
VL - 9
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 20353
ER -