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Getting the latest healthcare news for you
Getting the latest healthcare news for you
A research team from Waseda University has developed scLS, a new computational algorithm that improves how scientists analyze gene expression patterns in single-cell RNA sequencing data. By borrowing a signal-processing technique from astronomy, scLS handles complex, branching cell trajectories more efficiently than conventional methods — no messy manual branch assignments required. The tool could accelerate research in cell development, disease progression, and drug response.
Researchers at Waseda University in Japan have unveiled scLS, a new computational algorithm designed to improve the analysis of gene expression data from single-cell RNA sequencing (scRNA-seq). While scRNA-seq offers high-resolution snapshots of individual cell activity, tracking how gene expression changes over time — a process called trajectory analysis — has remained computationally challenging, especially with datasets now reaching millions of cells.
scLS tackles this by using the Lomb–Scargle (LS) periodogram, a signal-processing technique originally developed for unevenly spaced astronomical data. Instead of forcing gene expression into rigid regression models, scLS converts expression patterns into the frequency domain, allowing it to detect complex dynamics in branching cell trajectories without requiring predefined branch assignments. Published in Nucleic Acids Research (July 2026), scLS showed competitive accuracy and superior computational efficiency compared to existing tools.
The algorithm supports two key analyses: identifying genes that change dynamically over pseudotime, and detecting genes whose expression patterns shift between two conditions (e.g., healthy vs. diseased cells).
Key Takeaways:
Why it matters: As single-cell datasets grow larger and more complex, tools like scLS that are both accurate and computationally efficient will be essential for unlocking the biological insights hidden within them.
