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

Wanwen Zeng, Xi Chen, Zhana Duren, Yong Wang, Rui Jiang () and Wing Hung Wong ()
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Wanwen Zeng: Stanford University
Xi Chen: Stanford University
Zhana Duren: Stanford University
Yong Wang: CEMS, NCMIS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Rui Jiang: Tsinghua University
Wing Hung Wong: Stanford University

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12547-1

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