Improving Genetic Perturbation Response Prediction with an Enhanced Biological Knowledge Graph

Perturb-seq is a technique that combines scRNA-seq and CRISPR to explore cellular system operations and disease-associated genes, providing profound insights into the mechanisms behind biological processes. Although powerful, such a method is limited by its scalability for its cost-intensive and time-consuming nature, which calls for in silico prediction of genetic perturbation responses. Among all computational methods, GEARS represents the state-of-the-art by explicitly modeling the response of each gene to the perturbed gene, exploiting gene-gene relationships derived from Gene Ontology annotations. However, our evaluation of Gene Ontology annotations indicated that they are insufficient as the sole source of prior knowledge for predicting genetic perturbation responses. Therefore, they cannot fully support predicting genetic perturbation responses. We addressed this gap by constructing an augmented gene ontology network that incorporates extensive knowledge of diseases, drugs, and genes to capture nuanced gene-gene relationships not indicated by Gene Ontology alone. By replacing only the Gene Ontology graph in GEARS, our method outperforms GEARS in both single gene and combinational perturbation predictions. These findings suggest the effectiveness and importance of incorporating finer prior knowledge in predicting genetic perturbation responses, thereby encouraging future works on improving knowledge representation for single-cell perturbation prediction.