{
  "abstract": "Motivation. Single-cell atlas annotations are routinely used as ground truth without validation. We present AnnoAudit, a marker-based protocol with four convergent checks and an ambient-aware discrimination step (D1) that audits labels before re-analysis. Results. We applied AnnoAudit to labels generated by our own labelling pipeline for a public TBI single-cell dataset (CEREBRI, GSE269748). The audit exposed a permissive fallback rule that assigns a glutamatergic identity to any pan-neuronal-marker-positive cell lacking a subtype marker, manufacturing a 45,051-cell population that four independent reference-mapping models assign predominantly to choroid plexus (46.2%), astrocytes (22.4%) and microglia/macrophage (10.7%), with only 1.2% receiving any neuronal assignment (all inhibitory) and none assigned to an excitatory class. Marker- and depth-based controls show that the count matrices we analysed contain no neuronal-level expression of canonical neuronal genes (maximum Snap25 count 14 across 342,857 cells; Slc17a7 >= 5 in 5 cells), with transthyretin accounting for ~4% of all UMIs. The case illustrates the failure mode the protocol is designed to detect: permissive labelling rules can manufacture plausible-looking neuronal populations from data that lack neuronal-level signal. A simulation benchmark delimits the protocol's ambient boundary, and a literature screen identified six published re-analyses of the same dataset, none of which validated the annotations they inherited. No claim is made in this version about the cell-type annotations of any other resource. Availability and implementation. AnnoAudit is implemented in Python (scanpy); MIT-licensed source at github.com/ronnygao/AnnoAudit (doi:10.5281/zenodo.22274752). Contact. gaorong1234@suda.edu.cn",
  "abstract_shown": true,
  "affiliations": [
    "Zhangjiagang First People's Hospital (Suchoow University Affiliated Zhangjiagang Hospital)"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "L. Zhang"
    },
    {
      "name": "H. Rao"
    },
    {
      "name": "M. Li"
    },
    {
      "name": "X. Qian"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "Q. Yan"
    },
    {
      "affs": [
        0
      ],
      "name": "R. Gao"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fg0p85v98ka9a.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.03.749125",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749125v1.full",
  "id": "fg0p85v98ka9a",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749125v1",
  "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": "W7211942150",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749125v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fg0p85v98ka9a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:18Z",
      "native_id": "10.64898/2026.09.03.749125",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.03.749125v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource",
  "title_text": "AnnoAudit: a marker-based protocol for auditing single-cell atlas annotations reveals annotation-driven artifacts in a widely used traumatic brain injury resource",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:50Z",
  "url": "https://research.draconitic.com/paper/fg0p85v98ka9a",
  "venue": "bioRxiv",
  "versions": []
}