Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene–gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments.

了解细胞对遗传扰动的反应是许多生物医学应用的核心，从识别癌症中涉及的遗传相互作用到开发再生医学方法。然而，可能的多基因扰动数量的组合爆炸严重限制了实验研究。在这里，我们提出了图增强型基因激活和抑制模拟器（GEARS），这是一种将深度学习与基因-基因关系知识图相结合的方法，利用单细胞 RNA 测序数据来预测对单基因和多基因扰动的转录反应。扰动屏幕。 GEARS 能够预测由从未经过实验扰动的基因组成的扰动组合的结果。在组合扰动筛选中预测四种不同的遗传相互作用亚型时，GEARS 的精度比现有方法高出 40%，并且识别出最强相互作用的精度是之前方法的两倍。总体而言，GEARS 可以预测多基因扰动的表型独特效应，从而指导扰动实验的设计。