Affiliation:
1. Department of Statistics, University of California Riverside , Riverside, CA 92521, United States
Abstract
Abstract
Summary
Here, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level.
Availability and implementation
scHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff).
Funder
National Institute of Health
National Science Foundation
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability