{
  "abstract": "Targeted spatial transcriptomics requires selecting a gene panel before any experiment is run. Choosing well across tens of thousands of candidate genes under platform-specific probe, panel-size, and sensitivity constraints is difficult to do manually, and impossible to correct once a panel has been used. A growing number of computational tools to automate this selection have been designed, yet it remains unclear whether they are adopted in practice. Several factors could limit uptake: validation uncertainty about panel performance, workflow friction between a ranked gene list and a submission-ready panel, limited platform awareness in tools developed for dissociated single-cell data, and the cost advantage of vendor panels. We analyzed 18 computational tools for gene-panel selection from two angles: their citation landscape and their use across a targeted spatial transcriptomics corpus. Across 540 full-text-classified records, only 35 (6.5%) reported using the cited tool for panel design; most came from computational, benchmark, review, or non-spatial papers, or papers that cited but did not use it. In an independent corpus, only 34 of 479 (7.1%) involved a computational tool, alone or in a hybrid workflow; most used manually curated, premade vendor, or modified vendor panels. Both lines of evidence point the same way: computational selection is rarely part of the reported experimental workflow, and it more often supplemented manual curation than replaced it. Adoption is unlikely to turn on further benchmark gains alone; it will depend on reducing the validation uncertainty and workflow friction that keep users on established panel-design practice.",
  "abstract_shown": true,
  "affiliations": [
    "Katholieke Universiteit Leuven"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "A. CEYHUN"
    },
    {
      "name": "L. Venturelli"
    },
    {
      "name": "J. Jacobs"
    },
    {
      "affs": [
        0
      ],
      "name": "A. Sifrim"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2hm66jcmbdmb3.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.06.757034",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757034v1.full",
  "id": "2hm66jcmbdmb3",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757034v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221756042",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757034v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2hm66jcmbdmb3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:39Z",
      "native_id": "10.64898/2026.10.06.757034",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757034v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Lack of adoption of computational gene-panel-selection tools in targeted spatial transcriptomics",
  "title_text": "Lack of adoption of computational gene-panel-selection tools in targeted spatial transcriptomics",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:39Z",
  "url": "https://research.draconitic.com/paper/2hm66jcmbdmb3",
  "venue": "bioRxiv",
  "versions": []
}