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

Author

Listed:
  • Zhana Duren

    (Clemson University
    Clemson University
    Stanford University)

  • Wenhui Sophia Lu

    (Stanford University)

  • Joseph G. Arthur

    (10X Genomics, Inc.)

  • Preyas Shah

    (10X Genomics, Inc.)

  • Jingxue Xin

    (Stanford University
    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
    Stanford University
    Stanford University)

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.

Suggested Citation

  • Zhana Duren & Wenhui Sophia Lu & Joseph G. Arthur & Preyas Shah & Jingxue Xin & Francesca Meschi & Miranda Lin Li & Corey M. Nemec & Yifeng Yin & Wing Hung Wong, 2021. "Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25089-2
    DOI: 10.1038/s41467-021-25089-2
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    Cited by:

    1. Yang Xu & Rachel Patton McCord, 2022. "Diagonal integration of multimodal single-cell data: potential pitfalls and paths forward," Nature Communications, Nature, vol. 13(1), pages 1-4, December.

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