ArticleBioinformatics (Oxford, England)2022
scGAD: single-cell gene associating domain scores for exploratory analysis of scHi-C data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Spatial chromatin architecture and accessibility co-profiling of mammalian tissues.Nature methods · 2026Article
- A 3D genome atlas of human tonsil and the role of loop extrusion in B cell somatic hypermutation.Science (New York, N.Y.) · 2026Article
- A comprehensive benchmark of single-cell Hi-C embedding tools.Nature communications · 2025Article
- Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues.Nature biotechnology · 2025Article
- Single cell multiomics reveals drivers of metabolic dysfunction-associated steatohepatitis.medRxiv : the preprint server for health sciences · 2025Article
- Single cell multiomics and 3D genome architecture reveal novel pathways of human heart failure.medRxiv : the preprint server for health sciences · 2025Article
- ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Joint tensor modeling of single cell 3D genome and epigenetic data with Muscle.Journal of the American Statistical Association · 2024Article
- Normalization and de-noising of single-cell Hi-C data with BandNorm and scVI-3D.Genome biology · 2022Article
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Authors and funding
3 authors.
Funding
Abstract
summaryQuantitative tools are needed to leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell data modalities. We present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. Low-dimensional projections with scGAD capture clustering of cells based on their 3D structures. Significant chromatin interactions within and between cell types can be identified with scGAD. We further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data. AVAILABILITY AND IMPLEMENTATION: scGAD is part of the BandNorm R package at https://sshen82.github.io/BandNorm/articles/scGAD-tutorial.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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