{
  "id": 788581,
  "title": "Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization",
  "url": "https://urgent.news/2026/08/13/learning-discrete-cell-and-niche-codes-from-spatial-transcriptomics",
  "topic": "science",
  "section": "Science",
  "published": "2026-08-13T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.07.743490v1?rss=1"
  },
  "original_language": "en",
  "account": null,
  "summary": "Spatially-resolved transcriptomics (SRT) measures gene expression at single-cell resolution while preserving each cell's spatial location, enabling the joint study of cell identity and cellular niche, the recurring microenvironment that organises tissue function. Existing representation-learning methods typically capture only one of these axes at a time. We present SQUINT, a graph vector…",
  "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."
}