Deep learning imputes DNA methylation states in single cells and enhances the detection of epigenetic alterations in schizophrenia.

ABSTRACT: DNA methylation (DNAm) is a key epigenetic mark with essential roles in gene regulation, mammalian development, and human diseases. Single-cell technologies enable profiling DNAm at cytosines in individual cells, but they often suffer from low coverage for CpG sites. We introduce scMeFormer, a transformer-based deep learning model for imputing DNAm states at each CpG site in single cells. Comprehensive evaluations across five single-nucleus DNAm datasets from human and mouse demonstrate scMeFormer's superior performance over alternative models, achieving high-fidelity imputation even with coverage reduced to 10% of original CpG sites. Applying scMeFormer to a single-nucleus DNAm dataset from the prefrontal cortex of patients with schizophrenia and controls identified thousands of schizophr

SUBMITTER: Zhou J 

PROVIDER: S-EPMC11960545 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature