Abstract
Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling long-range genomic interactions; however, data from these technologies are prone to technical noise and biases that hinder downstream analysis. I will discuss normalization and denoising issues of scHi-C data by introducing a simple normalization approach named BandNorm and a deep generative modeling framework, scVI-3D, to account for scHi-C specific biases. I will also introduce single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data and illustrate how this approach enables integration of scHi-C data with other single cell data modalities.