{
  "id": 4963669,
  "title": "Algorithm improves detection of differentially expressed genes in large single-cell trajectory data sets",
  "url": "https://urgent.news/2026/09/01/algorithm-improves-detection-of-differentially-expressed-genes-in",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-01T23:20:01.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-09-algorithm-differentially-genes-large-cell.html"
  },
  "original_language": "en",
  "account": "Single-cell RNA sequencing (scRNA-seq) technology enables researchers to observe gene expression in individual cells, providing high-resolution snapshots of various cellular processes. However, traditional trajectory analysis methods may struggle to capture complex expression patterns due to the irregular distribution of cells along pseudotime and the presence of multiple branches. A research team from Waseda University in Japan has developed a new algorithm called scLS that addresses these challenges. scLS utilizes the Lomb–Scargle (LS) periodogram, a signal-processing technique, to represent gene expression patterns in the frequency domain, making it well-suited for analyzing irregularly distributed pseudotime data and detecting complex expression patterns in branching trajectories. The algorithm supports both dynamic gene expression tests and shifted gene expression tests, allowing researchers to prioritize genes for further biological interpretation. scLS demonstrates competitive performance in detecting pseudotime-dependent dynamics in branching trajectories while being more computationally efficient than conventional methods. Although it cannot localize expression dynamics to specific branches or lineages, scLS can serve as a valuable first-pass screening tool and be complemented by lineage-aware analyses for more detailed biological interpretation. The method has potential applications in various fields, including differentiation, development, immune activation, cellular reprogramming, disease progression, and drug response.",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}