biostudies-literatureZheng JFundamental Research Funds for the Central UniversitiesSun Yat-sen UniversityNational Natural Science Foundation of ChinaNatural Science Foundation of Guangdong Provincebbad379https://www.ebi.ac.uk/biostudies/studies/S-EPMC10691963biostudies-literatureUnknown25(1)Single-cell Hi-C (scHi-C) technology enables the investigation of 3D chromatin structure variability across individual cells. However, the analysis of scHi-C data is challenged by a large number of missing values. Here, we present a scHi-C data imputation model HiC-SGL, based on Subgraph extraction and graph representation learning. HiC-SGL can also learn informative low-dimensional embeddings of cells. We demonstrate that our method surpasses existing methods in terms of imputation accuracy and clustering performance by various metrics.Briefings in bioinformaticsSubgraph extraction and graph representation learning for single cell Hi-C imputation and clustering.PMC106919632023A151501190723xkjc0036187239592249303Zheng JYang YDai ZfalseSubgraph extraction and graph representation learning for single cell Hi-C imputation and clustering.Single-cell Hi-C (scHi-C) technology enables the investigation of 3D chromatin structure variability across individual cells. However, the analysis of scHi-C data is challenged by a large number of missing values. Here, we present a scHi-C data imputation model HiC-SGL, based on Subgraph extraction and graph representation learning. HiC-SGL can also learn informative low-dimensional embeddings of cells. We demonstrate that our method surpasses existing methods in terms of imputation accuracy and clustering performance by various metrics.2023-01-01T00:00:00Z2023 Nov2026-05-28T21:34:57.22Z2025-04-19T20:22:07.822ZS-EPMC106919633804049410.1093/bib/bbad379