GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.

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

SUBMITTER: Deng Y 

PROVIDER: S-EPMC10516632 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature