Drug treatment induces cell type-specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCellState, a deep learning autoencoder-based framework, for predicting the induced transcriptional state in a cell type after drug treatment, based on the drug response in another cell type. Training the method on a large collection of transcriptional drug perturbation profiles, prediction accuracy improves significantly over baseline and alternative deep learning approaches when applying the method to two cell types, with improved accuracy when generalizing the framework to additional cell types. Treatments with drugs or whole drug families not seen during training are predicted with similar accuracy, and the same framework can be used for predicting the results from other interventions, such as gene knock-downs. Finally, analysis of the trained model shows that the internal representation is able to learn regulatory relationships between genes in a fully data-driven manner.

中文翻译：

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DeepCellState：一种基于自动编码器的框架，用于预测药物治疗诱导的细胞类型特异性转录状态

药物治疗会诱导特定于细胞类型的转录程序，并且随着药物和细胞类型组合的数量的增加，测量转录药物反应的详尽筛选的成本变得棘手。我们开发了DeepCellState，这是一个基于深度学习自动编码器的框架，用于根据另一种细胞类型的药物反应，预测药物处理后一种细胞类型中的诱导转录状态。在大量转录药物扰动图谱上对该方法进行训练，当将该方法应用于两种细胞类型时，预测精度相对于基线方法和替代性深度学习方法有了显着提高，而将框架推广到其他细胞类型时，其预测准确性得到了提高。预测训练期间未见的使用药物或全药物家族的治疗具有相似的准确性，相同的框架可用于预测其他干预措施（例如基因敲低）的结果。最后，对经过训练的模型的分析表明，内部表示能够以完全数据驱动的方式学习基因之间的调节关系。