scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.
scMeFormer: a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia.
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scMeFormer：一种基于 Transformer 的深度学习模型，用于估算单细胞中的 DNA 甲基化状态，增强了精神分裂症表观遗传改变的检测。
DOI：
10.1101/2024.01.25.577200
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Han,Shizhong
中科院分区：
文献类型：
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作者：
Zhou,Jiyun;Luo,Chongyuan;Liu,Hanqing;Heffel,MatthewG;Straub,RichardE;Kleinman,JoelE;Hyde,ThomasM;Ecker,JosephR;Weinberger,DanielR;Han,Shizhong
DNA甲基化（DNAm）是一种重要的表观遗传标记，在基因调控、哺乳动物发育和各种人类疾病中起着关键作用。单细胞技术使得能够在单个细胞的DNA序列内的胞嘧啶处分析DNAm状态，但是它们通常受到CpG位点的有限覆盖的影响。在这项研究中，我们引入了scMeFormer，这是一种基于transformer的深度学习模型，旨在为单细胞中的每个CpG位点估算DNAm状态。通过全面的评估，我们证明了scMeFormer的上级性能相比，替代模型在四个单核DNAm数据集产生的不同技术。值得注意的是，scMeFormer表现出高保真的插补，即使在处理显著降低的覆盖率时，低至原始CpG位点的10%。此外，我们将scMeFormer应用于从四名精神分裂症患者和四名神经典型对照的前额叶皮层生成的单核DNAm数据集。这使得能够识别出数千个与精神分裂症相关的差异甲基化区域，这些区域在没有估算的情况下仍然无法检测到，并增加了我们对特定细胞类型内精神分裂症表观遗传改变的理解。我们的研究强调了深度学习在单细胞中估算DNAm状态的能力，我们希望scMeFormer成为单细胞DNAm研究的有价值的工具。
DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.