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/.