{
  "abstract": "Single-cell perturbation profiling is limited by cost and throughput, while computational methods remain constrained by inconsistent evaluation and underperformance relative to simple baselines. Here we present DCAF, a unified framework for single-cell perturbation simulation that that jointly addresses data validity, evaluation validity and predictive modeling. Among its three components, DCAF-data addresses the single-measurement limitation by reconstructing pre- and post-perturbation status using entropy-regularized optimal transport, and is demonstrated on both cell line and multi-cell type datasets. DCAF-criterion evaluates log fold changes in non-silenced genes to improve sensitivity to model error and robustness to zero inflation noise, validated on synthetic datasets. DCAF-model combines JEPA with explicit modeling for regulatory-dynamics modeling for interpretable prediction. Across benchmarks, DCAF outperforms state-of-the-art deep-learning methods, as well as mean and linear baselines in cross-gene, cross-cell-type and cross-region generalization. In adult Mouse brain, DCAF recapitulates known cell-type-specific regulatory heterogeneity of Dgcr8 knockdown at both cell type and gene levels. DCAF thus provides a rigorous and biologically grounded foundation for reliable perturbation simulation across development, disease and therapeutic discovery.",
  "abstract_shown": true,
  "affiliations": [
    "BGI Research, Wuhan"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "T. Xia"
    },
    {
      "name": "C. Lei"
    },
    {
      "name": "N. Chen"
    },
    {
      "name": "H. Hou"
    },
    {
      "name": "H. Zhang"
    },
    {
      "name": "H. Qin"
    },
    {
      "name": "D. Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Q. Li"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "S. Fang"
    },
    {
      "affs": [
        0
      ],
      "name": "Y. Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/72c096qt9frnq.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.13.758713",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758713v1.full",
  "id": "72c096qt9frnq",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758713v1",
  "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": "W7222931517",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758713v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/72c096qt9frnq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:32Z",
      "native_id": "10.64898/2026.10.13.758713",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.13.758713v3"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "DCAF: Unifying Data Validity, Evaluation Validity and Predictive Modeling for Single-cell Perturbation Simulation",
  "title_text": "DCAF: Unifying Data Validity, Evaluation Validity and Predictive Modeling for Single-cell Perturbation Simulation",
  "type": "preprint",
  "updated_at": "2026-11-02T00:20:37Z",
  "url": "https://research.draconitic.com/paper/72c096qt9frnq",
  "venue": "bioRxiv",
  "versions": []
}