Characterising context-dependent gene functions is crucial for understanding the genetic bases of health and disease. To date, inference of gene functions from large-scale genetic perturbation screens is based on ad hoc analysis pipelines involving unsupervised clustering and functional enrichment. We present Knowledge- and Context-driven Machine Learning (KCML), a framework that systematically predicts multiple context-specific functions for a given gene based on the similarity of its perturbation phenotype to those with known function. As a proof of concept, we test KCML on three datasets describing phenotypes at the molecular, cellular and population levels and show that it outperforms traditional analysis pipelines. In particular, KCML identified an abnormal multicellular organisation phenotype associated with the depletion of olfactory receptors, and TGFβ and WNT signalling genes in colorectal cancer cells. We validate these predictions in colorectal cancer patients and show that olfactory receptors expression is predictive of worse patient outcomes. These results highlight KCML as a systematic framework for discovering novel scale-crossing and context-dependent gene functions. KCML is highly generalisable and applicable to various large-scale genetic perturbation screens.

中文翻译：

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KCML：一种机器学习框架，用于从遗传扰动屏幕推断多尺度基因功能。

表征背景相关的基因功能对于理解健康和疾病的遗传基础至关重要。迄今为止，从大规模遗传扰动筛选中推断基因功能是基于涉及无监督聚类和功能富集的临时分析流程。我们提出了知识和上下文驱动的机器学习（KCML），这是一个框架，可以根据扰动表型与已知功能的相似性，系统地预测给定基因的多个上下文特定功能。作为概念证明，我们在描述分子、细胞和群体水平表型的三个数据集上测试了 KCML，并表明它优于传统的分析流程。特别是，KCML 发现了与结直肠癌细胞中嗅觉受体、TGFβ 和 WNT 信号基因耗竭相关的异常多细胞组织表型。我们在结直肠癌患者中验证了这些预测，并表明嗅觉受体的表达可以预测患者的预后较差。这些结果凸显了 KCML 作为发现新颖的跨尺度和背景依赖性基因功能的系统框架。KCML具有很强的通用性，适用于各种大规模遗传扰动筛选。