Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data.
Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data.
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DOI：
10.1038/s41467-022-34277-7
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发表时间：
2022-10-30
影响因子：
16.6
通讯作者：
Ma, Qin
中科院分区：
文献类型：
--
作者：
Chen, Junyi;Wu, Zhenyu;Qi, Ren;Ma, Anjun;Zhao, Jing;Xu, Dong;Li, Lang;Ma, Qin
可以分析来自大量基因表达数据库的药物筛选数据，以确定癌症药物的最佳临床应用。越来越多的单细胞 RNA 测序 (scRNA-seq) 数据还通过帮助研究癌细胞亚群药物反应的异质性，为提高治疗效果提供了见解。开发计算方法来预测和解释从临床样本收集的单细胞数据中的癌症药物反应可能非常有用。我们提出了 scDEAL，这是一种通过整合大规模批量细胞系数据来预测单细胞水平癌症药物反应的深度迁移学习框架。 scDEAL 的亮点包括将药物相关的批量 RNA-seq 数据与 scRNA-seq 数据相协调，并转移在批量 RNA-seq 数据上训练的模型来预测 scRNA-seq 中的药物反应。 scDEAL 的另一个特点是集成梯度特征解释，以推断耐药机制的特征基因。我们在六个 scRNA-seq 数据集上对 scDEAL 进行了基准测试，并通过三个侧重于药物反应标签预测、基因签名识别和伪时间分析的案例研究证明了其模型的可解释性。我们相信 scDEAL 可以帮助研究细胞重编程、药物选择和重新利用以提高治疗效果。单细胞 RNA-seq 数据提供了预测癌症药物反应的机会，同时考虑肿瘤内异质性。在这里，作者开发了一个深度迁移学习框架 - scDEAL - 通过整合单细胞和批量 RNA-seq 数据来预测癌症中的单细胞药物反应。
Drug screening data from massive bulk gene expression databases can be analyzed to determine the optimal clinical application of cancer drugs. The growing amount of single-cell RNA sequencing (scRNA-seq) data also provides insights into improving therapeutic effectiveness by helping to study the heterogeneity of drug responses for cancer cell subpopulations. Developing computational approaches to predict and interpret cancer drug response in single-cell data collected from clinical samples can be very useful. We propose scDEAL, a deep transfer learning framework for cancer drug response prediction at the single-cell level by integrating large-scale bulk cell-line data. The highlight in scDEAL involves harmonizing drug-related bulk RNA-seq data with scRNA-seq data and transferring the model trained on bulk RNA-seq data to predict drug responses in scRNA-seq. Another feature of scDEAL is the integrated gradient feature interpretation to infer the signature genes of drug resistance mechanisms. We benchmark scDEAL on six scRNA-seq datasets and demonstrate its model interpretability via three case studies focusing on drug response label prediction, gene signature identification, and pseudotime analysis. We believe that scDEAL could help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy. Single-cell RNA-seq data provide the opportunity to predict drug response in cancer while considering intratumour heterogeneity. Here, the authors develop a deep transfer learning framework - scDEAL - to predict single-cell drug responses in cancer by integrating single-cell and bulk RNA-seq data.
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