Universal prediction of cell-cycle position using transfer learning.

Journal: Genome biology
Published Date: Jan 31, 2022

Abstract

BACKGROUND: The cell cycle is a highly conserved, continuous process which controls faithful replication and division of cells. Single-cell technologies have enabled increasingly precise measurements of the cell cycle both as a biological process of interest and as a possible confounding factor. Despite its importance and conservation, there is no universally applicable approach to infer position in the cell cycle with high-resolution from single-cell RNA-seq data.

Authors

  • Shijie C Zheng
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.
  • Genevieve Stein-O'Brien
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Jonathan J Augustin
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Jared Slosberg
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Giovanni A Carosso
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Briana Winer
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Gloria Shin
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Hans T Bjornsson
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA.
  • Loyal A Goff
    Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, USA. loyalgoff@jhmi.edu.
  • Kasper D Hansen
    Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA. khansen@jhsph.edu.

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