ArticleGenes2022
scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders.
Article in Genes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- Navigating the 3D genome at single-cell resolution: techniques, computation, and mechanistic landscapes.Briefings in bioinformatics · 2025Review
- Single-Cell Hi-C Technologies and Computational Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- UV-induced reorganization of 3D genome mediates DNA damage response.Nature communications · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- A mini-review of single-cell Hi-C embedding methods.Computational and structural biotechnology journal · 2024Review
- SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model.Briefings in bioinformatics · 2024Article
- [Advances in methods and applications of single-cell Hi-C data analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering.Computational and structural biotechnology journal · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins:
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.