Contact

  • Academic
    mirandal@stanford.edu
    University - Student Department: Computer Science Position: Undergraduate
    University - Student Department: Computer Science Position: Graduate

Additional Info

  • Mail Code: 2259

All Publications

  • Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data. Nature communications Duren, Z., Lu, W. S., Arthur, J. G., Shah, P., Xin, J., Meschi, F., Li, M. L., Nemec, C. M., Yin, Y., Wong, W. H. 2021; 12 (1): 4763

    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.

    View details for DOI 10.1038/s41467-021-25089-2

    View details for PubMedID 34362918