Using pseudotime derivative on single-cell RNA sequencing data to identify genes undergoing cell cycle regulation.

Abstract

Motivation: The cell cycle is a critical part of cellular life, one that has long been studied, both directly, and through its regulatory components. Commonly, cell cycle synchronization or selection experiments are performed in order to study the cell cycle, thus chemically modifying the cells, or selecting them for specific phases. We seek to develop a means to study the cell cycle through the use of single cell RNA sequencing, effectively circumventing the need for such experiments.

Results: We utilize a well-established pseudotime method, along with the predicted and real expression of genes to calculate the velocity of individual genes. We then utilize statistics and expected biological behaviour to identify genes with significant shifts in velocity within the pseudotime. Additionally, we show the ability to observe gene regulatory behaviour such as mRNA splicing and degradation rates. As many cell line based research utilize multiple replicates we implement a merger method for technical replicates to adjust for technical variations, creating a more robust analysis. In summary, our study develops a robust approach to map the velocities of individual, biologically, and statistically significant genes throughout the cell cycle's phases within a cell line experiment.

Availability and implementation: Data and code are available at: https://github.com/Ylefol/CC_vel.