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

Single-cell RNA sequencing 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, remains challenging. Here, we present scLS, a Lomb-Scargle periodogram-based framework for two differential expression tests: a dynamic expression test for pseudotime-associated variation and a shifted expression test for condition-dependent differences in pseudotime-indexed expression trajectories. Because scLS operates in the frequency domain, it does not require specification of an explicit regression model and can be applied to inferred tree-structured trajectories without explicit branch assignment. We validated this approach using simulated data and real datasets, and our results showed that scLS achieved competitive performance and complementary sensitivity to transient or complex pseudotime-associated patterns. Our approach provides a computationally efficient first-pass screening framework that can be combined with lineage-aware analyses for detailed biological interpretation.