Normalization and De-noising of Single-cell Hi-C Data with BandNorm and 3DVI

Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling long-range genomic interactions at the single-cell resolution; however, data from these technologies are prone to technical noise and bias that, when unaccounted for, hinder downstream analysis. Here we developed a fast band normalization approach, BandNorm, and a deep generative modeling framework, 3DVI, to explicitly account for scHi-C specific technical biases. We present robust performances of BandNorm and 3DVI compared to existing state-of-the-art methods. BandNorm is effective in separating cell types, identification of interaction features, and recovery of cell-cell relationship, whereas de-noising by 3DVI successfully enables 3D compartments and domains recovery, especially for rare cell types.