{
  "abstract": "Malignant cell identification in single-cell RNA sequencing (scRNA-seq) remains challenging because transcriptomic cell typing, somatic variant detection and inferred copy-number approaches each have modality-specific limitations. Rare or ambiguous malignant populations are especially vulnerable to misclassification or exclusion, despite their relevance to treatment resistance, minimal residual disease, and metastasis. We developed CATFISHR, a semi-automated workflow that integrates RNA-expression similarity, inferred copy-number variation (CNV) profiles, and consensus cell typing to classify malignant cells relative to high-confidence references. CATFISHR was evaluated across eight cohorts, including seven scRNA-seq datasets spanning multiple carcinomas and melanoma subtypes, and one 10x Visium spatial transcriptomics sample. Compared with available benchmarks or ground truths, CATFISHR achieved balanced accuracy > 0.85 in six of eight cohorts and F1 score > 0.80 in six of eight cohorts. Cells uniquely identified by CATFISHR as malignant (\"Hidden\" cells) were present in every dataset and were not consistently explained by standard quality-control metrics. In several cohorts, Hidden cells were transcriptionally closer to benchmark malignant than non-malignant compartments; in Visium data, sampled Hidden spots contained histologist-confirmed morphologically malignant cells. CATFISHR reduces missed malignant calls while preserving rare, transitional and benchmark-discordant populations for downstream investigation.",
  "abstract_shown": true,
  "affiliations": [
    "NEXUS Personalized Health Technologies, ETH Zurich"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "P. Shukla"
    },
    {
      "name": "M. Mannino"
    },
    {
      "name": "M. Hofer"
    },
    {
      "name": "E. R. Lattmann"
    },
    {
      "name": "V. Haunerdinger"
    },
    {
      "affs": [
        0
      ],
      "name": "P. Turko"
    },
    {
      "name": "M. P. Levesque"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2yexq6541ebjt.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.22.760906",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760906v1.full",
  "id": "2yexq6541ebjt",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760906v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7223937288",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760906v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/2yexq6541ebjt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:53Z",
      "native_id": "10.64898/2026.10.22.760906",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.22.760906v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification",
  "title_text": "CATFISHR: integration of transcriptome and inferred CNV profiles for comprehensive malignant cell identification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:53Z",
  "url": "https://research.draconitic.com/paper/2yexq6541ebjt",
  "venue": "bioRxiv",
  "versions": []
}