CrispyDrug: A causal framework for learning joint representations of genetic and chemical perturbations in cells

Large-scale single-cell RNA-seq perturbation datasets provide detailed measurements of how interventions alter cellular state. However, existing perturbation models typically focus on either genetic or chemical perturbations, while causal methods for jointly modeling both perturbation types are missing. Here, we introduce CrispyDrug, a novel causal framework for learning joint representations of genetic and chemical perturbations from single-cell transcriptomic data. CrispyDrug treats both perturbation types as interventions on a shared cellular outcome and learns representations through their effects on post-treatment gene expression. Our framework proceeds in two stages. (1) First, it learns perturbation representations by predicting post-treatment expression and, at the same time, balances the representations wrt. to the perturbation type. (2) Second, it trains perturbation-type-specific decoders that map the joint representations back to genetic or chemical perturbations. Across large-scale experiments in the K562 leukemia cell line, we find that CrispyDrug captures heterogeneous transcriptional responses and achieves stronger predictive performance than existing perturbation models. By analyzing the learned latent space and cross-perturbation retrieval, we further find that the joint representation links genetic and chemical perturbations through similarities in their transcriptional effects. To the best of our knowledge, CrispyDrug is the first framework to jointly model both genetic and chemical perturbations in single-cell transcriptomics with an explicit causal interpretation.