A systematic benchmarking framework and dual-view optimization strategy for single-cell DNA methylation imputation

Single-cell DNA methylation (scDNAm) profiling is revolutionizing our understanding of epigenetic control of gene expression, but its accurate analysis is severely hindered by extreme data sparsity. While imputation methods have undergone remarkable development in recent years, a rigorous benchmark to guide method selection remains absent. We established the first systematic benchmarking framework for scDNAm imputation, subjecting five state-of-the-art methods to a comprehensive evaluation across 13 published experimental scDNAm datasets. Performance was systematically assessed across seven critical dimensions: accuracy, sensitivity to data characteristics, scalability, robustness to data splitting strategies, inter-dataset generalizability, convergence behavior, and computational efficiency. Through rigorous statistical analysis, we dissected the influence of intrinsic data attributes and model architectures on the fidelity of scDNAm imputation to provide guidance for selecting appropriate methods for given scenarios. Furthermore, based on the benchmark-identified limitations, we proposed a dual-view strategy to address the performance bottlenecks of existing methods: at the model view, we developed BridgeCpG, an ensemble strategy to integrate complementary modeling strengths to overcome single-model limitations; at the data view, we introduced an adaptive divide-and-conquer strategy to partition highly heterogeneous datasets into several homogeneous subsets amenable to accurate imputation, followed by aggregating the sub-results. This integrated framework, spanning both model and data views, delivers quantitative analyses, scenario-aware selection guidelines, and targeted innovative strategies, establishing a rigorous, enabling foundation for accurate, high-throughput, and scalable next-generation single-cell epigenomic analysis.