{
  "abstract": "Automated cell-type annotation is a prerequisite for nearly all single-cell RNA-sequencing (scRNA-seq) analysis, and the proliferation of annotation tools -- spanning marker-based, similarity-based, classical machine-learning, deep-learning, semi-supervised, large language model (LLM), and transformer foundation-model families -- has exceeded any existing guidance on how to select among them. Existing benchmarks utilize convenient, well-known datasets in which cell number, class imbalance, cell-type number, and differential-expression strength vary concurrently, so performance cannot be attributed to any dataset property. We benchmarked 63 tools from seven families using a Taguchi L9(34) orthogonal array that varied these four properties independently and extended the comparison under more realistic conditions: real data, cross-platform transfer, public marker databases, LLM annotation, and fine-tuned foundation models. With standardized preprocessing and inputs, the leading cell-level families performed within 0.06 {kappa} of one another, and fine-tuned foundation models averaged only slightly higher. Across families, sequencing platforms, and data types, accuracy was predicted near-linearly by how separable cell types were in a shared expression embedding space, measured as k-nearest-neighbor (kNN) purity (R2 = 0.85-0.99). We found that dataset identity accounted for {approx}84% of {kappa} variance while tool identity accounted for {approx}4%. Strong performers were distributed across families, for example, Seurat label transfer and singleCellNet among reference-based classifiers and mLLMCelltype among LLM annotators. When cell types cleanly separate, many annotation methods suffice; when they do not, algorithmic complexity struggles to compensate for noisy signal. Method selection should therefore prioritize available resources, such as curated references and compute availability, over sophisticated modeling.",
  "abstract_shown": true,
  "affiliations": [
    "University of Notre Dame"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "O. Wardhana"
    },
    {
      "name": "Z. Zeng"
    },
    {
      "name": "X. Lu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8qxrmd03mrhyf.bib",
  "cited_by": 1,
  "doi": "10.64898/2026.08.28.747622",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747622v1.full",
  "id": "8qxrmd03mrhyf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747622v1",
  "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": "W7207793520",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747622v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8qxrmd03mrhyf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:01Z",
      "native_id": "10.64898/2026.08.28.747622",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.28.747622v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Cell-type separability predicts annotation accuracy and outweighs algorithm choice: a factorial benchmark across seven scRNA paradigms",
  "title_text": "Cell-type separability predicts annotation accuracy and outweighs algorithm choice: a factorial benchmark across seven scRNA paradigms",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/8qxrmd03mrhyf",
  "venue": "bioRxiv",
  "versions": []
}