Learning Perturbation Effects Through Contrastive Alignment of Transcriptomics and Textual Embeddings

Single-cell perturbation screens offer a scalable approach for characterizing the effects of genetic and chemical interventions on cellular state. However, most existing representation-learning methods are tailored to a single perturbation modality and fail to explicitly incorporate external semantic knowledge, which limits their ability to generalize across datasets and perturbation types. Here, we introduce PertOmni, a CLIP-style multimodal representation-learning framework that aligns transcriptomic perturbation signatures with text-derived embeddings of curated gene and compound descriptions. PertOmni jointly trains a shared transcriptomic encoder and dataset-specific text encoders using a masked contrastive objective that emphasizes within–cell-type discrimination while mitigating confounding effects arising from cell-type heterogeneity. We evaluate the produced joint embedding space on bi-directional retrieval, drug–gene interaction inference, and perturbation prediction across both small-molecule and CRISPRi perturbation datasets, and demonstrate consistent improvements over strong baseline methods.