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DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data

Author

Listed:
  • Wanwen Zeng

    (Stanford University
    Tsinghua University)

  • Xi Chen

    (Stanford University)

  • Zhana Duren

    (Stanford University)

  • Yong Wang

    (CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
    Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences)

  • Rui Jiang

    (Tsinghua University)

  • Wing Hung Wong

    (Stanford University)

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.

Suggested Citation

  • Wanwen Zeng & Xi Chen & Zhana Duren & Yong Wang & Rui Jiang & Wing Hung Wong, 2019. "DC3 is a method for deconvolution and coupled clustering from bulk and single-cell genomics data," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-12547-1
    DOI: 10.1038/s41467-019-12547-1
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    Cited by:

    1. Shilu Zhang & Saptarshi Pyne & Stefan Pietrzak & Spencer Halberg & Sunnie Grace McCalla & Alireza Fotuhi Siahpirani & Rupa Sridharan & Sushmita Roy, 2023. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
    2. Kai Cao & Qiyu Gong & Yiguang Hong & Lin Wan, 2022. "A unified computational framework for single-cell data integration with optimal transport," Nature Communications, Nature, vol. 13(1), pages 1-15, December.

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