Universal prediction of cell-cycle position using transfer learning.

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.

Results

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 use of transfer learning. We estimate a cell-cycle embedding using a fixed r

SUBMITTER: Zheng SC 

PROVIDER: S-EPMC8802487 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature