Statistical Bioinformatics Seminar: Dr Yue You, Guangzhou National Laboratory

Title: A Systematic Comparison of Single-Cell Perturbation Response Prediction Models 

Speaker: Dr Yue You, Guangzhou National Laboratory 

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 12 representative methods and 3 baselines across 25 datasets spanning
diverse perturbation modalities and species, including two new 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 recovery, and distributional similarity.  

Across tasks, performance depended strongly on perturbation effect size and evaluation
perspective: expression-level agreement was 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 dataset
heterogeneity dominated outcomes.  

This benchmark establishes current methodological limits, clarifies when metrics
diverge, and provides a foundation for developing virtual-cell models that more
faithfully capture heterogeneous perturbation responses.  

About the speaker: Dr Yue You is a postdoctoral researcher at the Guangzhou National
Laboratory.  Her research centers on developing single-cell multi-omics algorithms and
spatial-omics data analysis to decode cellular dynamics under genetic and
pharmacological perturbations.