Computational cell cycle analysis of single cell RNA-Seq data

The variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. Researchers from the University of Connecticut Health Center have developed SC1CC (SC1 – Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. The researchers also introduce a new robust metric, GSS (Gene Smoothness Score) for assessing the cell cycle based order of the cells.

Process Flow Diagram of SC1CC

This simplified process flow shows a typical sequence of steps to analyze scRNA-Seq data with SC1CC; three outcomes can be obtained, a) the order of cells according to their progression along the cell cycle phases as determined by SC1CC, b) a designation of dividing vs. non dividing cell clusters, and c) the cell cycle phase annotation for the identified cell cycle clusters.

Availability – SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.