scHiCSRS: a self-representation smoothing method with Gaussian mixture model for imputing single cell Hi-C data

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 132 - 20
• Main Authors: Xie, Qing, Meng, Wang, Lin, Shili
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 21.05.2025; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Cells; Chromosomes; Clustering; Clustering (Computers); Data smoothing; Dropouts; Gaussian processes; Genomes; Humans; Methods; Neighborhoods; Neural networks; Normal Distribution; Probabilistic models; Quality control; Quality improvement; Representations; Reproducibility; Sampling; Sampling zeros; Simulation methods; Single-Cell Analysis - methods; Smoothing; Software; Sparsity; Spatial dependencies; Structural zeros; United States; Dropouts; Neighborhoods; Sampling zeros; Sparsity; Structural zeros
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06147-8

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. We propose scHiCSRS, a self-representation smoothing method that improves data quality, and a Gaussian mixture model that identifies structural zeros among observed zeros. scHiCSRS not only takes spatial dependencies of a scHi-C data matrix into account but also borrows information from similar single cells. Through an extensive set of simulation studies, we demonstrate the ability of scHiCSRS for identifying structural zeros with high sensitivity and for accurate imputation of dropout values in sampling zeros. Downstream analyses for three experimental datasets show that data improved from scHiCSRS yield more accurate clustering of cells than simply using observed data or improved data from comparison methods. In summary, scHiCSRS provides a valuable tool for identifying structural zeros and imputing dropouts. The resulted data are improved for downstream analysis, especially for understanding cell-to-cell variation through subtype clustering.