GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.
Bioinformatics
; 39(9)2023 09 02.
Article
en En
| MEDLINE
| ID: mdl-37647650
ABSTRACT
MOTIVATION Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses. RESULTS:
We proposed a novel graph-based deep learning approach to impute methylation matrices based on locus-aware neighboring subgraphs with locus-aware encoding orienting on one cell type. Merely using the CpGs methylation matrix, the obtained GraphCpG outperforms previous methods on datasets containing more than hundreds of cells and achieves competitive performance on smaller datasets, with subgraphs of predicted sites visualized by retrievable bipartite graphs. Besides better imputation performance with increasing cell number, it significantly reduces computation time and demonstrates improvement in downstream analysis. AVAILABILITY AND IMPLEMENTATION The source code is freely available at https//github.com/yuzhong-deng/graphcpg.git.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Estudio de Asociación del Genoma Completo
/
Epigenoma
Tipo de estudio:
Prognostic_studies
Idioma:
En
Año:
2023
Tipo del documento:
Article