GeneScope: Adaptive Gene Resolution for Chemical Perturbation Prediction

Predicting how cells respond to drugs could help researchers decide which experiments to run across a vast space of compounds and cellular contexts. Current methods learn perturbation effects in latent spaces, model changes in cell populations, or draw on pretrained cellular representations. Yet benchmarks of genetic perturbation prediction show that complex deep models do not consistently outperform simple linear baselines. These findings encourage a closer look at what models preserve about cells before treatment. Global compression can blur gene-level differences, but choosing which details to retain is difficult when the responding genes are still unknown. We introduce GeneScope, an adaptive-resolution Transformer built on full coverage with selective refinement. GeneScope keeps a compact view of all input genes, then uses untreated cells to identify where additional detail is needed. Genes that are poorly reconstructed or vary across controls receive finer-resolution representations. The controls thus guide where to preserve detail, while the compound guides how the state changes. Every gene can still respond, whether or not it is selected for refinement. We evaluate GeneScope on Tahoe and two Sci-Plex3-derived benchmarks. Shared-condition comparisons show improvements in response correlation and high-response gene recovery over linear and multiple deep-learning baselines. These gains hold without a pretrained cellular encoder. Adding pretrained representations further improves response rank correlation on both SciPlex benchmarks. GeneScope therefore ties gene resolution to the cellular state we can observe, rather than the response we have yet to predict. Code and preprocessing pipelines are available at an anonymous repository: https://anonymous.4open.science/r/GeneScope/.