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A systematic comparison of single-cell perturbation response prediction models.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: A systematic comparison of single-cell perturbation response prediction models.
Συγγραφείς: Li L; GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China.; Guangzhou National Laboratory, Guangzhou, China., You Y; Guangzhou National Laboratory, Guangzhou, China., Fu Y; Guangzhou National Laboratory, Guangzhou, China., Liao W; The University of Hong Kong, Hong Kong, China., Fan X; School of Life Sciences, Westlake University, Hangzhou, China.; Research Center for Industries of the Future, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Westlake Institute for Advanced Study, Hangzhou, China., Lu S; Guangzhou National Laboratory, Guangzhou, China., Cao Y; Guangzhou National Laboratory, Guangzhou, China., Li B; Guangzhou National Laboratory, Guangzhou, China., Ren W; Guangzhou National Laboratory, Guangzhou, China., Kong J; Renaissance Era Innovation Technology Co., Ltd., Beijing, China., Zheng S; Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China., Chen J; Guangzhou National Laboratory, Guangzhou, 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., Liu X; School of Life Sciences, Westlake University, Hangzhou, China.; Research Center for Industries of the Future, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Westlake Institute for Advanced Study, Hangzhou, China., Tian L; GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China.; Guangzhou National Laboratory, Guangzhou, China.
Πηγή: Science advances [Sci Adv] 2026 Sep 11; Vol. 12 (37), pp. eaed3414. Date of Electronic Publication: 2026 Sep 09.
Τύπος έκδοσης: Journal Article; Comparative Study
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 101653440 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2375-2548 (Electronic) Linking ISSN: 23752548 NLM ISO Abbreviation: Sci Adv Subsets: MEDLINE
Imprint Name(s): Original Publication: Washington, DC : American Association for the Advancement of Science, [2015]-
Ιατρικοί όροι (MeSH): Single-Cell Analysis*/methods , Computational Biology*/methods , Gene Expression Regulation*, Humans ; Animals ; Single-Cell Gene Expression Analysis ; Gene Expression Profiling
Περίληψη: 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.
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Nat Methods. 2023 Nov;20(11):1759-1768. (PMID: 37770709)
Nucleic Acids Res. 2021 Dec 2;49(21):e122. (PMID: 34500471)
Cell. 2022 Jul 7;185(14):2559-2575.e28. (PMID: 35688146)
Cell. 2024 Aug 22;187(17):4520-4545. (PMID: 39178831)
Nat Methods. 2017 Mar;14(3):297-301. (PMID: 28099430)
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Science. 2019 Aug 23;365(6455):786-793. (PMID: 31395745)
Cell. 2016 Dec 15;167(7):1853-1866.e17. (PMID: 27984732)
Nat Genet. 2021 Mar;53(3):322-331. (PMID: 33649593)
Science. 2020 Jan 3;367(6473):45-51. (PMID: 31806696)
Brief Funct Genomics. 2018 Jul 1;17(4):246-254. (PMID: 29342231)
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Entry Date(s): Date Created: 20260909 Date Completed: 20260909 Latest Revision: 20260914
Update Code: 20260914
PubMed Central ID: PMC13557097
DOI: 10.1126/sciadv.aed3414
PMID: 42715312
Βάση Δεδομένων: MEDLINE
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  Data: A systematic comparison of single-cell perturbation response prediction models.
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  Data: <searchLink fieldCode="AU" term="%22Li+L%22">Li L</searchLink>; GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China.; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22You+Y%22">You Y</searchLink>; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22Fu+Y%22">Fu Y</searchLink>; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22Liao+W%22">Liao W</searchLink>; The University of Hong Kong, Hong Kong, China.<br /><searchLink fieldCode="AU" term="%22Fan+X%22">Fan X</searchLink>; School of Life Sciences, Westlake University, Hangzhou, China.; Research Center for Industries of the Future, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Westlake Institute for Advanced Study, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Lu+S%22">Lu S</searchLink>; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22Cao+Y%22">Cao Y</searchLink>; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22Li+B%22">Li B</searchLink>; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22Ren+W%22">Ren W</searchLink>; Guangzhou National Laboratory, Guangzhou, China.<br /><searchLink fieldCode="AU" term="%22Kong+J%22">Kong J</searchLink>; Renaissance Era Innovation Technology Co., Ltd., Beijing, China.<br /><searchLink fieldCode="AU" term="%22Zheng+S%22">Zheng S</searchLink>; Global Institute of Future Technology, Shanghai Jiao Tong University, Shanghai, China.<br /><searchLink fieldCode="AU" term="%22Chen+J%22">Chen J</searchLink>; Guangzhou National Laboratory, Guangzhou, 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.<br /><searchLink fieldCode="AU" term="%22Liu+X%22">Liu X</searchLink>; School of Life Sciences, Westlake University, Hangzhou, China.; Research Center for Industries of the Future, Westlake University, Hangzhou, China.; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China.; Westlake Institute for Advanced Study, Hangzhou, China.<br /><searchLink fieldCode="AU" term="%22Tian+L%22">Tian L</searchLink>; GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China.; Guangzhou National Laboratory, Guangzhou, China.
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  Data: 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.
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(PMID: <searchLink fieldCode="PM" term="%2241184550%22">41184550)</searchLink>
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      – TitleFull: A systematic comparison of single-cell perturbation response prediction models.
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      – PersonEntity:
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            NameFull: Liao W
      – PersonEntity:
          Name:
            NameFull: Fan X
      – PersonEntity:
          Name:
            NameFull: Lu S
      – PersonEntity:
          Name:
            NameFull: Cao Y
      – PersonEntity:
          Name:
            NameFull: Li B
      – PersonEntity:
          Name:
            NameFull: Ren W
      – PersonEntity:
          Name:
            NameFull: Kong J
      – PersonEntity:
          Name:
            NameFull: Zheng S
      – PersonEntity:
          Name:
            NameFull: Chen J
      – PersonEntity:
          Name:
            NameFull: Liu X
      – PersonEntity:
          Name:
            NameFull: Tian L
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 11
              M: 09
              Text: 2026 Sep 11
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-electronic
              Value: 2375-2548
          Numbering:
            – Type: volume
              Value: 12
            – Type: issue
              Value: 37
          Titles:
            – TitleFull: Science advances
              Type: main
ResultId 1