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CpG transformer for imputation of single-cell methylomes

Gaetan De Waele (UGent) , Jim Clauwaert (UGent) , Gerben Menschaert (UGent) and Willem Waegeman (UGent)
(2022) BIOINFORMATICS. 38(3). p.597-603
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Abstract
Motivation: The adoption of current single-cell DNA methylation sequencing protocols is hindered by incomplete coverage, outlining the need for effective imputation techniques. The task of imputing single-cell (methylation) data requires models to build an understanding of underlying biological processes. Results: We adapt the transformer neural network architecture to operate on methylation matrices through combining axial attention with sliding window self-attention. The obtained CpG Transformer displays state-of-the-art performances on a wide range of scBS-seq and scRRBS-seq datasets. Furthermore, we demonstrate the interpretability of CpG Transformer and illustrate its rapid transfer learning properties, allowing practitioners to train models on new datasets with a limited computational and time budget.
Keywords
DNA METHYLATION, LANDSCAPES

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MLA
De Waele, Gaetan, et al. “CpG Transformer for Imputation of Single-Cell Methylomes.” BIOINFORMATICS, vol. 38, no. 3, 2022, pp. 597–603, doi:10.1093/bioinformatics/btab746.
APA
De Waele, G., Clauwaert, J., Menschaert, G., & Waegeman, W. (2022). CpG transformer for imputation of single-cell methylomes. BIOINFORMATICS, 38(3), 597–603. https://doi.org/10.1093/bioinformatics/btab746
Chicago author-date
De Waele, Gaetan, Jim Clauwaert, Gerben Menschaert, and Willem Waegeman. 2022. “CpG Transformer for Imputation of Single-Cell Methylomes.” BIOINFORMATICS 38 (3): 597–603. https://doi.org/10.1093/bioinformatics/btab746.
Chicago author-date (all authors)
De Waele, Gaetan, Jim Clauwaert, Gerben Menschaert, and Willem Waegeman. 2022. “CpG Transformer for Imputation of Single-Cell Methylomes.” BIOINFORMATICS 38 (3): 597–603. doi:10.1093/bioinformatics/btab746.
Vancouver
1.
De Waele G, Clauwaert J, Menschaert G, Waegeman W. CpG transformer for imputation of single-cell methylomes. BIOINFORMATICS. 2022;38(3):597–603.
IEEE
[1]
G. De Waele, J. Clauwaert, G. Menschaert, and W. Waegeman, “CpG transformer for imputation of single-cell methylomes,” BIOINFORMATICS, vol. 38, no. 3, pp. 597–603, 2022.
@article{8738803,
  abstract     = {{Motivation: The adoption of current single-cell DNA methylation sequencing protocols is hindered by incomplete coverage, outlining the need for effective imputation techniques. The task of imputing single-cell (methylation) data requires models to build an understanding of underlying biological processes.

Results: We adapt the transformer neural network architecture to operate on methylation matrices through combining axial attention with sliding window self-attention. The obtained CpG Transformer displays state-of-the-art performances on a wide range of scBS-seq and scRRBS-seq datasets. Furthermore, we demonstrate the interpretability of CpG Transformer and illustrate its rapid transfer learning properties, allowing practitioners to train models on new datasets with a limited computational and time budget.}},
  author       = {{De Waele, Gaetan and Clauwaert, Jim and Menschaert, Gerben and Waegeman, Willem}},
  issn         = {{1367-4803}},
  journal      = {{BIOINFORMATICS}},
  keywords     = {{DNA METHYLATION,LANDSCAPES}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{597--603}},
  title        = {{CpG transformer for imputation of single-cell methylomes}},
  url          = {{http://dx.doi.org/10.1093/bioinformatics/btab746}},
  volume       = {{38}},
  year         = {{2022}},
}

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