RT Journal Article
SR Electronic
T1 Universal prediction of cell cycle position using transfer learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.06.438463
DO 10.1101/2021.04.06.438463
A1 Shijie C. Zheng
A1 Genevieve Stein-O’Brien
A1 Jonathan J. Augustin
A1 Jared Slosberg
A1 Giovanni A. Carosso
A1 Briana Winer
A1 Gloria Shin
A1 Hans T. Bjornsson
A1 Loyal A. Goff
A1 Kasper D. Hansen
YR 2021
UL http://biorxiv.org/content/early/2021/04/06/2021.04.06.438463.abstract
AB 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 as 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. Here, we present tricycle, an R/Bioconductor package, to address this challenge by leveraging key features of the biology of the cell cycle, the mathematical properties of principal component analysis of periodic functions, and the ubiquitous applicability of transfer learning. We show that tricycle can predict any cell’s position in the cell cycle regardless of the cell type, species of origin, and even sequencing assay. The accuracy of tricycle compares favorably to gold-standard experimental assays which generally require specialized measurements in specifically constructed in vitro systems. Unlike gold-standard assays, tricycle is easily applicable to any single-cell RNA-seq dataset. Tricycle is highly scalable, universally accurate, and eminently pertinent for atlas-level data.Competing Interest StatementThe authors have declared no competing interest.