GraphCpG: Imputation of Single-cell Methylomes Based on Locus-aware Neighboring Subgraphs
Yuzhong Deng, Jianxiong Tang, Jiyang Zhang, Jianxiao Zou, Que Zhu, Shicai Fan- Computational Mathematics
- Computational Theory and Mathematics
- Computer Science Applications
- Molecular Biology
- Biochemistry
- Statistics and Probability
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 num, it significantly reduces computation time and demonstrates improvement in downstream analysis.
Availability
The source code is freely available at https://github.com/yuzhong-deng/graphcpg.git.
Supplementary information
Supplementary data are available at Bioinformatics online.