{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng Y"],"funding":["NHGRI NIH HHS","national institutes of health"],"pagination":["222"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9575231"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_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"],"journal":["Genome biology"],"pubmed_title":["Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D."],"pmcid":["PMC9575231"],"funding_grant_id":["HG003747","R21 HG011371","R01 HG003747","HG011371"],"pubmed_authors":["Zheng Y","Shen S","Keles S"],"additional_accession":[]},"is_claimable":false,"name":"Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D.","description":"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","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2026-05-28T01:12:23.374Z","creation":"2024-10-18T22:31:48.082Z"},"accession":"S-EPMC9575231","cross_references":{"pubmed":["36253828"],"doi":["10.1186/s13059-022-02774-z"]}}