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

## TL;DR

This paper introduces a new method using single-cell RNA sequencing data to study cell cycle regulation without chemical treatments.

## Contribution

A novel approach to identify cell cycle-regulated genes using pseudotime derivative analysis in single-cell RNA sequencing.

## Key findings

- The method identifies genes with significant velocity shifts during the cell cycle phases.
- It enables observation of gene regulatory behaviors like mRNA splicing and degradation rates.
- A merger method for technical replicates improves analysis robustness.

## Abstract

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.

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.

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