Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data

Zhana Duren, Wenhui Sophia Lu, Joseph G. Arthur, Preyas Shah, Jingxue Xin, Francesca Meschi, Miranda Lin Li, Corey M. Nemec, Yifeng Yin and Wing Hung Wong ()
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Zhana Duren: Clemson University
Wenhui Sophia Lu: Stanford University
Joseph G. Arthur: 10X Genomics, Inc.
Preyas Shah: 10X Genomics, Inc.
Jingxue Xin: Stanford University
Francesca Meschi: 10X Genomics, Inc.
Miranda Lin Li: Stanford University
Corey M. Nemec: 10X Genomics, Inc.
Yifeng Yin: 10X Genomics, Inc.
Wing Hung Wong: Stanford University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract The comparison of gene regulatory networks between diseased versus healthy individuals or between two different treatments is an important scientific problem. Here, we propose sc-compReg as a method for the comparative analysis of gene expression regulatory networks between two conditions using single cell gene expression (scRNA-seq) and single cell chromatin accessibility data (scATAC-seq). Our software, sc-compReg, can be used as a stand-alone package that provides joint clustering and embedding of the cells from both scRNA-seq and scATAC-seq, and the construction of differential regulatory networks across two conditions. We apply the method to compare the gene regulatory networks of an individual with chronic lymphocytic leukemia (CLL) versus a healthy control. The analysis reveals a tumor-specific B cell subpopulation in the CLL patient and identifies TOX2 as a potential regulator of this subpopulation.

Date: 2021
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DOI: 10.1038/s41467-021-25089-2

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