biostudies-literatureXie QNational Institute of General Medical SciencesNIGMS NIH HHS132https://www.ebi.ac.uk/biostudies/studies/S-EPMC12093726biostudies-literatureUnknown26(1)<h4>Background</h4>Single cell Hi-C (scHi-C) techniques make it possible to study cell-to-cell variability, but excess of zeros are makes scHi-C matrices extremely sparse and difficult for downstream analyses. The observed zeros are a combination of two events: structural zeros for which two loci never interact due to underlying biological mechanisms, or dropouts (sampling zeros) where two loci interact but not captured due to insufficient sequencing depth. Although data quality improvement approaches have been proposed, little has been done to differentiate these two types of zeros, even though such a distinction can greatly benefit downstream analysis such as clustering.<h4>Results</h4>We propose scHiCSRS, a self-representation smoothing method that improves data quality, and a Gaussian BMC bioinformaticsscHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data.PMC12093726R01GM114142Lin SXie QMeng WfalsescHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data.<h4>Background</h4>Single cell Hi-C (scHi-C) techniques make it possible to study cell-to-cell variability, but excess of zeros are makes scHi-C matrices extremely sparse and difficult for downstream analyses. The observed zeros are a combination of two events: structural zeros for which two loci never interact due to underlying biological mechanisms, or dropouts (sampling zeros) where two loci interact but not captured due to insufficient sequencing depth. Although data quality improvement approaches have been proposed, little has been done to differentiate these two types of zeros, even though such a distinction can greatly benefit downstream analysis such as clustering.<h4>Results</h4>We propose scHiCSRS, a self-representation smoothing method that improves data quality, and a Gaussian 2025-01-01T00:00:00Z2025 May2026-06-02T17:55:06.41Z2026-04-18T03:12:19.818ZS-EPMC120937264039981010.1186/s12859-025-06147-8