Pseudotime derivative of single cell RNA-seq data identify genes with cell cycle-dependent expression
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. By utilizing the predicted and real expression of genes, we demonstrate the ability to sort cells based on their order in the cell cycle and subsequently calculate the velocity of individual genes. From the velocity of each gene, we create a method which identifies all genes which have significant positive and negative velocities, additionally we show the ability to observe gene regulatory behaviour such as mRNA splicing and degradation rates. We also include 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 genes throughout the cell cycle's phases.