SCAFFOLD: A Simple Graph-Based Linear Model Enables Accurate Prediction of Transcriptomic Responses to Unseen Perturbations

Predicting responses to untested gene perturbations could extend the reach of single-cell genetic screens. As recent work emphasizes large pretrained models, we investigate how existing biological relationships can guide prediction. Across two studies and four cell backgrounds, stronger Gene Ontology (GO) annotation overlap and STRING associations corresponded to more similar measured perturbation responses. We introduce scaffold, a graph-based linear predictor in which a GO annotation-overlap graph constrains gene representations while measured responses train a shared linear response map. In a unified comparison of 17 methods across nine scPerturBench datasets, scaffold ranked first on seven datasets and third on the remaining two, achieving the best mean dataset rank of 1.44 under the benchmark's six-metric ranking procedure. Controlled comparisons showed similar prediction quality with GO and STRING, while control-cell coexpression and much wider GO neighborhoods performed less well. These results demonstrate the predictive value of biological relationships within a compact linear model and identify relationship source and neighborhood selection as useful design choices alongside advances in model scale.