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<p>Single-cell perturbation technologies enable systematic investigation of gene functions and regulatory networks with single-cell resolution. However, performing large-scale and combinatorial perturbation screens poses notable challenges due to their exponentially increased complexity. Computational methods, including foundation models, have been developed to predict perturbation effects. Yet despite claims of promising performance, concerns remain about their true efficacy, particularly when evaluated across diverse and previously unseen cellular contexts and perturbation scenarios. Here, we present a comprehensive benchmark of 27 methods for single-cell perturbation response prediction, evaluated across 29 datasets using 6 complementary performance metrics. By evaluating them under multiple scenarios, we systematically assess their generalizability, including that of emerging foundation models. Our results provide practical guidance for method selection and underscore the need for cellular context embedding approaches to enhance the generalizability of perturbation effect prediction in single-cell research.</p>

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Benchmarking algorithms for generalizable single-cell perturbation response prediction

  • 期刊论文

  • 发表日期：

    2025年12月11日

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  • Zhiting Wei,
  • Yiheng Wang,
  • Yicheng Gao,
  • Shuguang Wang,
  • Ping Li,
  • Duanmiao Si,
  • Yuli Gao,
  • Siqi Wu,
  • Danlu Li,
  • Kejing Dong,
  • Xingbo Yang,
  • Chen Tang,
  • Shaliu Fu,
  • Xiaohan Chen,
  • Wannian Li,
  • Yuzhou You,
  • Chen Zhang,
  • Aibin Liang,
  • Guohui Chuai,
  • Qi Liu

摘要

Single-cell perturbation technologies enable systematic investigation of gene functions and regulatory networks with single-cell resolution. However, performing large-scale and combinatorial perturbation screens poses notable challenges due to their exponentially increased complexity. Computational methods, including foundation models, have been developed to predict perturbation effects. Yet despite claims of promising performance, concerns remain about their true efficacy, particularly when evaluated across diverse and previously unseen cellular contexts and perturbation scenarios. Here, we present a comprehensive benchmark of 27 methods for single-cell perturbation response prediction, evaluated across 29 datasets using 6 complementary performance metrics. By evaluating them under multiple scenarios, we systematically assess their generalizability, including that of emerging foundation models. Our results provide practical guidance for method selection and underscore the need for cellular context embedding approaches to enhance the generalizability of perturbation effect prediction in single-cell research.