Bayesian Estimation of Three-Dimensional Chromosomal Structure from Single-Cell Hi-C Data

J Comput Biol. 2019 Nov;26(11):1191-1202. doi: 10.1089/cmb.2019.0100. Epub 2019 Jun 18.

Abstract

The problem of three-dimensional (3D) chromosome structure inference from Hi-C data sets is important and challenging. While bulk Hi-C data sets contain contact information derived from millions of cells and can capture major structural features shared by the majority of cells in the sample, they do not provide information about local variability between cells. Single-cell Hi-C can overcome this problem, but contact matrices are generally very sparse, making structural inference more problematic. We have developed a Bayesian multiscale approach, named Structural Inference via Multiscale Bayesian Approach, to infer 3D structures of chromosomes from single-cell Hi-C while including the bulk Hi-C data and some regularization terms as a prior. We study the landscape of solutions for each single-cell Hi-C data set as a function of prior strength and demonstrate clustering of solutions using data from the same cell.

Keywords: Bayesian estimation; Hi-C; chromosome 3D structure.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem*
  • Chromosome Structures / genetics
  • Chromosome Structures / ultrastructure*
  • Chromosomes / genetics
  • Chromosomes / ultrastructure*
  • Imaging, Three-Dimensional / methods
  • Single-Cell Analysis / methods*