{
  "abstract": "Copy number changes are a hallmark of cancer and reveal which cells are malignant and how tumors evolve. Single-cell RNA sequencing is widely used to infer them in individual tumor cells. Existing methods do so by normalizing each cell against a reference of normal cells. As a result, their output depends on which cells are analyzed together, they often label normal cells as aneuploid, and they struggle in samples without normal cells. Here we present REFCON, a deep-learning model that infers copy number from each cell's own expression, without a reference. We evaluated REFCON on more than 410,000 held-out cells from cell lines, organoids and 112 tumors of nine cancer types, profiled with seven sequencing technologies. It agreed more closely with single-cell DNA than existing methods, was unaffected by sample composition, and labeled far fewer normal cells as aneuploid. In patient-derived organoids, which contain no normal cells, it gave consistent profiles across passages and tracked clonal evolution. REFCON addresses long-standing limitations of reference-based inference and enables robust, reference-free copy number calling from single-cell transcriptomes.",
  "abstract_shown": true,
  "affiliations": [
    "Bilkent University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. M. Gencturk"
    },
    {
      "name": "A. E. Cicek"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7z2bnm97hww63.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.03.742406",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.03.742406v2.full",
  "id": "7z2bnm97hww63",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.03.742406v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7201840246",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.03.742406v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7z2bnm97hww63.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:19Z",
      "native_id": "10.64898/2026.08.03.742406",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.03.742406v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Reference-free and robust copy number inference in single-cell tumor transcriptomes",
  "title_text": "Reference-free and robust copy number inference in single-cell tumor transcriptomes",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:19Z",
  "url": "https://research.draconitic.com/paper/7z2bnm97hww63",
  "venue": "bioRxiv",
  "versions": []
}