{
  "abstract": "Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 13 representative methods and baselines across 25 datasets spanning diverse perturbation modalities and species, including two primary immune-cell drug-response resources. We evaluated three core tasks-generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types-using 24 metrics covering expression-level accuracy, relative changes, differential expression (DE) recovery, and distributional similarity. Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: Expression-level agreement was the highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where biological consistency dominated outcomes. This benchmark establishes current methodological limits, clarifies that different metrics probe distinct biological signals rather than redundant summaries of the same prediction problem, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses.",
  "abstract_shown": true,
  "affiliations": [
    "GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China",
    "Guangzhou National Laboratory, Guangzhou, China",
    "The University of Hong Kong, Hong Kong, China",
    "Research Center for Industries of the Future, Westlake University, Hangzhou, China",
    "School of Life Sciences, Westlake University, Hangzhou, China",
    "Westlake Institute for Advanced Study, Hangzhou, China",
    "Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China",
    "Renaissance Era Innovation Technology Co., Ltd, China",
    "Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China",
    "State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Lanxiang Li",
      "orcid": "0000-0003-2921-9105"
    },
    {
      "affs": [
        1
      ],
      "name": "Yue You",
      "orcid": "0000-0003-3883-445X"
    },
    {
      "affs": [
        1
      ],
      "name": "Yunlin Fu",
      "orcid": "0009-0002-8292-3169"
    },
    {
      "affs": [
        2
      ],
      "name": "Wenyu Liao"
    },
    {
      "affs": [
        3,
        4,
        5,
        6
      ],
      "name": "Xueying Fan",
      "orcid": "0000-0002-7809-2254"
    },
    {
      "affs": [
        1
      ],
      "name": "Shihong Lu",
      "orcid": "0009-0005-3787-0063"
    },
    {
      "affs": [
        1
      ],
      "name": "Ye Cao",
      "orcid": "0009-0007-7074-2578"
    },
    {
      "affs": [
        1
      ],
      "name": "Bo Li"
    },
    {
      "affs": [
        1
      ],
      "name": "Wenle Ren",
      "orcid": "0009-0002-5376-2634"
    },
    {
      "affs": [
        7
      ],
      "name": "Jiaming Kong"
    },
    {
      "affs": [
        8
      ],
      "name": "Shuangjia Zheng",
      "orcid": "0000-0001-9747-4285"
    },
    {
      "affs": [
        1,
        9
      ],
      "name": "Jizheng Chen",
      "orcid": "0000-0001-6507-990X"
    },
    {
      "affs": [
        3,
        4,
        5,
        6
      ],
      "name": "Xiaodong Liu",
      "orcid": "0000-0002-9315-3406"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Luyi Tian",
      "orcid": "0000-0003-3420-3685"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dzy5vm9kp7xf0.bib",
  "cited_by": 29,
  "doi": "10.1126/sciadv.aed3414",
  "fulltext_url": null,
  "id": "dzy5vm9kp7xf0",
  "issns": [
    "2375-2548"
  ],
  "landing_url": "https://doi.org/10.1126/sciadv.aed3414",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7212020435",
  "pdf_url": null,
  "pmcid": "PMC13557097",
  "pmid": "42715312",
  "published": "2026-09-09",
  "publisher": "American Association for the Advancement of Science",
  "ris": "https://research.draconitic.com/paper/dzy5vm9kp7xf0.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T13:21:05Z",
      "native_id": "W7212020435",
      "source": "openalex",
      "url": "https://doi.org/10.1126/sciadv.aed3414"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "A systematic comparison of single-cell perturbation response prediction models",
  "title_text": "A systematic comparison of single-cell perturbation response prediction models",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T13:20:41Z",
  "url": "https://research.draconitic.com/paper/dzy5vm9kp7xf0",
  "venue": "Science Advances",
  "versions": []
}