Benchmarking algorithms for generalizable single-cell perturbation response prediction

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.