HiCImpute: A Bayesian hierarchical model for identifying structural zeros and enhancing single cell Hi-C data

• Published in: PLoS computational biology Vol. 18; no. 6; p. e1010129
• Main Authors: Xie, Qing, Han, Chenggong, Jin, Victor, Lin, Shili
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.06.2022; Public Library of Science (PLoS)
• Subjects: Bayes Theorem; Biology and Life Sciences; Chromatin; Chromosomes; Cluster Analysis; Computer and Information Sciences; Medicine and Health Sciences; Research and Analysis Methods; Spatial Analysis

Single cell Hi-C techniques enable one to study cell to cell variability in chromatin interactions. However, single cell Hi-C (scHi-C) data suffer severely from sparsity, that is, the existence of excess zeros due to insufficient sequencing depth. Complicating the matter further is the fact that not...