A structured study of cross-condition prediction of transcriptional responses to gene perturbations

Gene perturbation experiments coupled with transcriptomic profiling are crucial for uncovering causal gene-gene relationships, yet it remains cost-prohibitive to systematically explore perturbation responses across diverse biological conditions. As a result, in silico prediction of perturbation response has emerged as an important strategy for guiding cost-effective experimental design. Although recent methods have begun to address cross-condition perturbation prediction, it remains under-characterized across scenarios defined by whether the perturbation has been observed during training under other biological conditions. Here, we study cross-condition prediction under both seen- and unseen-perturbation scenarios. We introduce TranScouter, a lightweight encoderdecoder framework that represents perturbed genes using LLM-derived embeddings of their text summaries and represents biological conditions using transcriptomic profiles of control cells from the target condition. Across evaluated benchmarks, TranScouter performs competitively in both scenarios. We further use empirical analyses to characterize how condition-space coverage and perturbation-effect transferability shape crosscondition performance.