This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Single-cell RNA sequencing (scRNA-seq) is a method for measuring gene expression in individual cells, allowing observation of various cellular processes, including cell differentiation, the cell cycle and stimulus response, for each unique cell instead of averaging across millions of cells. It provides high-resolution snapshots of biological processes.
However, it does not track the same cell continuously over time. To address this limitation, trajectory inference approaches have been developed that computationally arrange cellular snapshots along an inferred developmental trajectory known as pseudotime. Recent advances in trajectory inference algorithms have enabled researchers to generate data sets containing hundreds of thousands or even millions of cells.
However, downstream analyses of these trajectories, particularly the identification of differentially expressed genes (DEGs), remain challenging. DEGs can have two patterns: genes whose expression changes dynamically over pseudotime and genes whose patterns are shifted between two conditions. Although dynamic expression analysis is more widely used, identifying shifted expression patterns is also important.
Additionally, cells can be distributed irregularly along pseudotime, and the inferred trajectory may contain multiple branches, for which conventional models require explicit regression models or branch assignments. It is essential to develop novel downstream analysis techniques that capture DEG patterns and handle complex cell trajectories. In a new study, a research team led by Assistant Professor Hitoshi Iuchi and Professor Michiaki Hamada from the Faculty of Science and Engineering at Waseda University in Japan developed a downstream analysis algorithm called scLS.
"scLS is a computational method for identifying pseudotime-associated genes from single-cell RNA sequencing data," explains Iuchi. "It reduces the need for arbitrary branch correspondence decisions and can be used to prioritize genes for more detailed biological interpretation." Their study was recently published in Nucleic Acids Research. Conventional trajectory analysis methods typically fit explicit regression models that describe gene expression as a smooth function of pseudotime, which may not adequately capture complex expression patterns. scLS, on the other hand, uses the Lomb–Scargle (LS) periodogram, a signal-processing technique designed for unevenly sampled data, to represent gene expression patterns in the frequency domain.
This enables the algorithm to analyze irregularly distributed pseudotime data and detect complex expression patterns in branching trajectories without requiring explicit regression models or predefined branch correspondence. scLS supports both the dynamic gene expression test and the shifted gene expression test. For both tests, the pseudotime-domain data for each gene are first converted to the frequency domain through the LS periodogram. For the dynamic gene expression test, the LS periodogram is used to evaluate false-alarm probabilities (FAPs) over a predefined frequency grid, which are then used to calculate the gene-level P-value, defined as the minimum FAP across the scanned frequencies.
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