ArticleJournal of computational biology : a journal of computational molecular cell biology2016
Deconvolution of Ensemble Chromatin Interaction Data Reveals the Latent Mixing Structures in Cell Subpopulations.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Integrating heterogeneity into topologically associating domain boundary prediction in large genomic context in human.NAR genomics and bioinformatics · 2026Article
- Systematic evaluation of the heterogeneity of topologically associating domain boundaries in a large genomic context in humans.NAR genomics and bioinformatics · 2026Article
- Assessing reproducibility of Hi-C chromatin interactions using stratum-adjusted irreproducible discovery rate.Bioinformatics (Oxford, England) · 2026Article
- A comparative analysis of topological domain callers over RNA-associated interactome.BMC bioinformatics · 2026Article
- OMNIS: a spatially informed multi-omics deep-learning framework for tumor recurrence prediction and primary-metastatic tumor differentiation title page.Frontiers in artificial intelligence · 2026Article
- Performance evaluation of large language models in the diagnosis of emergency internal medicine diseases: a retrospective study.Frontiers in public health · 2026Article
- Analysis of chromatin structure reveals the connection between sQTLs and the splicing of distant genes.Scientific reports · 2025Article
- Joint analysis of sQTL and Hi-C reveals spatial proximity between sQTLs and target genes in cancer tissues.Scientific reports · 2025Article
- Advancements and future directions in single-cell Hi-C based 3D chromatin modeling.Computational and structural biotechnology journal · 2024Review
- Dynamics of chromosome organization in a minimal bacterial cell.Frontiers in cell and developmental biology · 2023Article
- ProbC: joint modeling of epigenome and transcriptome effects in 3D genome.BMC genomics · 2022Article
- THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data.PLoS genetics · 2022Article
- Understanding the function of regulatory DNA interactions in the interpretation of non-coding GWAS variants.Frontiers in cell and developmental biology · 2022Review
- Bayesian inference of chromatin structure ensembles from population-averaged contact data.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Three-dimensional genome structures of single sensory neurons in mouse visual and olfactory systems.Nature structural & molecular biology · 2019Article
- TAD fusion score: discovery and ranking the contribution of deletions to genome structure.Genome biology · 2019Article
- Semi-nonparametric modeling of topological domain formation from epigenetic data.Algorithms for molecular biology : AMB · 2019Article
- Identification of hierarchical chromatin domains.Bioinformatics (Oxford, England) · 2016Article
- Deconvolution of Ensemble Chromatin Interaction Data Reveals the Latent Mixing Structures in Cell Subpopulations.Journal of computational biology : a journal of computational molecular cell biology · 2016Article
- 4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?Genome biology · 2016Review
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3 authors.
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Abstract
Chromosome conformation capture (3C) experiments provide a window into the spatial packing of a genome in three dimensions within the cell. This structure has been shown to be correlated with gene regulation, cancer mutations, and other genomic functions. However, 3C provides mixed measurements on a population of typically millions of cells, each with a different genome structure due to the fluidity of the genome and differing cell states. Here, we present several algorithms to deconvolve these measured 3C matrices into estimations of the contact matrices for each subpopulation of cells and relative densities of each subpopulation. We formulate the problem as that of choosing matrices and densities that minimize the Frobenius distance between the observed 3C matrix and the weighted sum of the estimated subpopulation matrices. Results on HeLa 5C and mouse and bacteria Hi-C data demonstrate the methods' effectiveness. We also show that domain boundaries from deconvolved matrices are often more enriched or depleted for regulatory chromatin markers when compared to boundaries from convolved matrices.
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