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

• Published in: Nature communications 2021-08, Vol.12 (1), p.4763-4763, Article 4763
• Main Authors: Duren, Zhana, Lu, Wenhui Sophia, Arthur, Joseph G., Shah, Preyas, Xin, Jingxue, Meschi, Francesca, Li, Miranda Lin, Nemec, Corey M., Yin, Yifeng, Wong, Wing Hung
• Format: Article
• Language: English
• Subjects: 38; 631/114/2114; 631/250/2502; Accessibility; Chromatin; Chronic lymphocytic leukemia; Clustering; Comparative analysis; Computer applications; Embedding; Gene expression; Gene regulation; Humanities and Social Sciences; Leukemia; Lymphatic leukemia; multidisciplinary; Networks; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-25089-2

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. Changes in cell state underlie the difference between health and disease. Here, the authors propose a computational framework for the integration of gene expression and chromatin-accessibility data from single cells to identify differences in gene regulation in cell types across two conditions.