DeepNanoHi-C

Single-cell long-read concatemer sequencing (scNanoHi-C) technology provides unique insights into the higher-order chromatin structure across the genome in individual cells, crucial for understanding 3D genome organization. However, the lack of specialized analytical tools for scNanoHi-C data impedes progress, as existing methods, which primarily focus on scHi-C technologies, do not fully address the specific challenges of scNanoHi-C, such as sparsity, cell-specific variability, and complex chromatin interaction networks. Here, we introduce DeepNanoHi-C, a novel deep learning framework specifically designed for scNanoHi-C data, which leverages a multi-step autoencoder and a Sparse Gated Mixture of Experts (MoE) to accurately predict chromatin interactions by imputing sparse contact maps, thereby capturing cell-specific structural features. DeepNanoHi-C effectively captures complex global chromatin contact patterns through the multi-step autoencoder and dynamically selects the most appropriate expert from a pool of experts based on distinct chromatin contact patterns. Furthermore, DeepNanoHi-C integrates multi-scale predictions through a dual-channel prediction net, refining complex interaction information and facilitating comprehensive downstream analyses of chromatin architecture. Experimental validation shows that DeepNanoHi-C outperforms existing methods in distinguishing cell types and demonstrates robust performance in data imputation tasks. Additionally, the framework identifies single-cell 3D genome features, such as cell-specific topologically associating domain (TAD) boundaries, further confirming its ability to accurately model chromatin interactions. Beyond single-cell analysis, DeepNanoHi-C also uncovers conserved genomic structures across species, providing insights into the evolutionary conservation of chromatin organization.