The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime

Abstract

MotivationIn recent years, single-cell RNA sequencing (scRNA-seq) has provided high-resolution snapshots of biological processes and has contributed to the understanding of cell dynamics. Trajectory inference has the potential to provide a quantitative representation of cell dynamics, and several trajectory inference algorithms have been developed. However, the downstream analysis of trajectory inference, such as the analysis of differentially expressed genes (DEG), remains challenging. ResultsIn this study, we introduce a Lomb-Scargle (LS) periodogram-based algorithm for identifying DEGs associated with pseudotime in a trajectory analysis. The algorithm is capable of analyzing any inferred trajectory, including tree structures with multiple branching points, leading to diverse cell types. We validated this approach using simulated data and real datasets, and our results showed that our approach was superior when performing DEG analysis on complex structured trajectories. Our approach will contribute to gene characterization in trajectory analysis and help gain deeper biological insights. AvailabilityAll code used in our proposed method can be found at https://github.com/hiuchi/LS. Contacthitoshi.iuchi@hamadalab.com Supplementary informationSupplementary data are available at Journal Name online.