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

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...