GraphCpG: Imputation of Single-cell Methylomes Based on Locus-aware Neighboring Subgraphs

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2023 Aug 30

PMID 37647650

Authors

Yuzhong Deng

Jianxiong Tang

Jiyang Zhang

Jianxiao Zou

Que Zhu

Shicai Fan

Affiliations

Soon will be listed here.

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.

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