ArticleBioinformatics (Oxford, England)2023
GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Multi-omics-driven precision medicine.iMeta · 2026Review
- A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation.Briefings in bioinformatics · 2026Article
- MambaCpG: an accurate model for single-cell DNA methylation status imputation using mamba.Briefings in bioinformatics · 2025Article
- Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.Cell genomics · 2025Article
- Data-Driven Identification of Early Cancer-Associated Genes via Penalized Trans-Dimensional Hidden Markov Models.Biomolecules · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationSingle-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.
resultsWe 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.