Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D.

ABSTRACT: Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling of long-range genomic interactions. However, data from these technologies are prone to technical noise and biases that hinder downstream analysis. We develop a normalization approach, BandNorm, and a deep generative modeling framework, scVI-3D, to account for scHi-C specific biases. In benchmarking experiments, BandNorm yields leading performances in a time and memory efficient manner for cell-type separation, identification of interacting loci, and recovery of cell-type relationships, while scVI-3D exhibits advantages for rare cell types and under high sparsity scenarios. Application of BandNorm coupled with gene-associating domain analysis reveals scRNA-seq validated sub-cell type identific

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC9575231 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature