ArticleNature communications2019
DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Reference Regulatory Element-Guided Gene Expression Analysis for Mechanistic Inference of Gene Regulatory Networks.bioRxiv : the preprint server for biology · 2026Article
- Optimal transport for label transfer in single-cell multi-omics integration.Briefings in bioinformatics · 2026Article
- Decoding the Therapeutic Effects of Acupuncture in Hemorrhagic Stroke Using Single-Cell RNA Sequencing.CNS neuroscience & therapeutics · 2025Article
- Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon.Genome biology · 2025Article
- A comparison of integration methods for single-cell RNA sequencing data and ATAC sequencing data.Quantitative biology (Beijing, China) · 2025Review
- Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data.Nature biotechnology · 2025Article
- Integration of single cell multiomics data by deep transfer hypergraph neural network.Briefings in functional genomics · 2025Article
- Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.eLife · 2024Article
- Mitochondrial genetic variations in leukemia: a comprehensive overview.Blood science (Baltimore, Md.) · 2024Review
- Challenges and perspectives in computational deconvolution of genomics data.Nature methods · 2024Review
- Recent advances in exploring transcriptional regulatory landscape of crops.Frontiers in plant science · 2024Review
- Gene regulatory network inference in the era of single-cell multi-omics.Nature reviews. Genetics · 2023Review
- Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data.NPJ systems biology and applications · 2023Review
- Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets.Nature communications · 2023Article
- InferLoop: leveraging single-cell chromatin accessibility for the signal of chromatin loop.Briefings in bioinformatics · 2023Article
- GLUE multimodal single cell data.Precision clinical medicine · 2023Article
- HiChIPdb: a comprehensive database of HiChIP regulatory interactions.Nucleic acids research · 2023Article
- iPoLNG-An unsupervised model for the integrative analysis of single-cell multiomics data.Frontiers in genetics · 2023Article
- Current and future perspectives of single-cell multi-omics technologies in cardiovascular research.Nature cardiovascular research · 2023Review
- Article
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Authors and funding
6 authors.
Funding
Abstract
Characterizing and interpreting heterogeneous mixtures at the cellular level is a critical problem in genomics. Single-cell assays offer an opportunity to resolve cellular level heterogeneity, e.g., scRNA-seq enables single-cell expression profiling, and scATAC-seq identifies active regulatory elements. Furthermore, while scHi-C can measure the chromatin contacts (i.e., loops) between active regulatory elements to target genes in single cells, bulk HiChIP can measure such contacts in a higher resolution. In this work, we introduce DC3 (De-Convolution and Coupled-Clustering) as a method for the joint analysis of various bulk and single-cell data such as HiChIP, RNA-seq and ATAC-seq from the same heterogeneous cell population. DC3 can simultaneously identify distinct subpopulations, assign single cells to the subpopulations (i.e., clustering) and de-convolve the bulk data into subpopulation-specific data. The subpopulation-specific profiles of gene expression, chromatin accessibility and enhancer-promoter contact obtained by DC3 provide a comprehensive characterization of the gene regulatory system in each subpopulation.
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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.