CpG Transformer for imputation of single-cell methylomes.
Bioinformatics
; 38(3): 597-603, 2022 01 12.
Article
em En
| MEDLINE
| ID: mdl-34718418
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. AVAILABILITY AND IMPLEMENTATION CpG Transformer is freely available at https//github.com/gdewael/cpg-transformer. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Metilação de DNA
/
Epigenoma
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article