{
  "id": 8318445,
  "title": "Setting the SCENE for Interpretable Cell-Gene Embeddings in Single-Cell RNA-seq",
  "url": "https://urgent.news/2026/09/18/setting-the-scene-for-interpretable-cell-gene-embeddings-in-single",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-18T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.12.750699v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Single-cell RNA sequencing measures cellular states at high resolution, but sparse high-dimensional count data remain difficult to model interpretably. We introduce the Single-Cell Euclidean Network Embedding (SCENE), a probabilistic latent-distance model that jointly embeds cells and genes from Unique Molecular Identifier (UMI) counts. SCENE treats the count matrix as a weighted bipartite…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "bioRxiv",
        "title": "Interpretable spherical geometry of single-cell state transitions from dominant principal components",
        "url": "https://urgent.news/2026/09/18/interpretable-spherical-geometry-of-single-cell-state-transitions",
        "published": "2026-09-18T00:00:00.000Z"
      }
    ]
  },
  "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."
}