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Updated: Jul 17, 2026
11:52
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
Published on: February 9, 2017
The Lomb-Scargle periodogram-based differentially expressed gene detection along pseudotime.
Hitoshi Iuchi1, Michiaki Hamada1,2,3
1Faculty of Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
Nucleic Acids Research
|July 16, 2026
Summary
We developed scLS, a novel framework for analyzing single-cell RNA sequencing data. This tool efficiently detects dynamic and condition-dependent gene expression changes along cell trajectories, improving biological interpretation.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into cellular processes and dynamics.
- Trajectory inference methods aim to quantitatively model cell dynamics but face challenges in downstream analysis, particularly for differential gene expression.
- Identifying genes that vary with pseudotime or differ between conditions along inferred cell trajectories is crucial for biological interpretation.
Purpose of the Study:
- To introduce scLS, a Lomb-Scargle periodogram-based framework for robust differential expression analysis in scRNA-seq data.
- To provide two novel tests: a dynamic expression test for pseudotime-associated variation and a shifted expression test for condition-dependent differences.
- To enable analysis of complex, tree-structured trajectories without requiring explicit branch assignment.
Main Methods:
- scLS utilizes the Lomb-Scargle periodogram for frequency-domain analysis of gene expression data.
- It performs differential expression testing without needing to specify explicit regression models.
- The framework is applicable to inferred tree-structured trajectories, accommodating complex cellular dynamics.
Main Results:
- scLS demonstrated competitive performance in differential expression analysis on both simulated and real scRNA-seq datasets.
- The method showed complementary sensitivity to transient or complex pseudotime-associated expression patterns.
- It provides a computationally efficient framework for initial screening of differentially expressed genes.
Conclusions:
- scLS offers an efficient and flexible approach for analyzing gene expression dynamics in single-cell data.
- The framework enhances the biological interpretation of cell trajectories by identifying key genes.
- scLS can be integrated with lineage-aware analyses for comprehensive understanding of cellular processes.
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