Identifying cancer subtypes by integration of multi-omic data is beneficial to improve the understanding of disease progression, and provides more precise treatment for patients. Cancer subtypes identification is usually accomplished by clustering patients with unsupervised learning approaches. Thus, most existing integrative cancer subtyping methods are performed in an entirely unsupervised way. An integrative cancer subtyping approach can be improved to discover clinically more relevant cancer subtypes when considering the clinical survival response variables. In this study, we propose a Survival Supervised Graph Clustering (S2GC)for cancer subtyping by taking into consideration survival information. Specifically, we use a graph to represent similarity of patients, and develop a multi-omic survival analysis embedding with patient-to-patient similarity graph learning for cancer subtype identification. The multi-view (omic)survival analysis model and graph of patients are jointly learned in a unified way. The learned optimal graph can be unitized to cluster cancer subtypes directly. In the proposed model, the survival analysis model and adaptive graph learning could positively reinforce each other. Consequently, the survival time can be considered as supervised information to improve the quality of the similarity graph and explore clinically more relevant subgroups of patients. Experiments on several representative multi-omic cancer datasets demonstrate that the proposed method achieves better results than a number of state-of-the-art methods. The results also suggest that our method is able to identify biologically meaningful subgroups for different cancer types. (Our Matlab source code is available online at github: https://github.com/CLiu272/S2GC)

中文翻译：

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基于生存分析和多组学肿瘤数据整合的癌症亚型监督图聚类


通过整合多组学数据来识别癌症亚型有利于提高对疾病进展的理解，并为患者提供更精准的治疗。癌症亚型识别通常是通过采用无监督学习方法对患者进行聚类来完成的。因此，大多数现有的综合癌症亚型分型方法都是以完全无人监督的方式进行的。在考虑临床生存反应变量时，可以改进综合癌症亚型分析方法，以发现临床上更相关的癌症亚型。在这项研究中，我们考虑到生存信息，提出了一种用于癌症亚型划分的生存监督图聚类（S2GC）。具体来说，我们使用图表来表示患者的相似性，并开发嵌入患者间相似性图学习的多组学生存分析，以识别癌症亚型。以统一的方式共同学习患者的多视图（组学）生存分析模型和图。学习到的最佳图可以统一起来直接对癌症亚型进行聚类。在所提出的模型中，生存分析模型和自适应图学习可以相互促进。因此，生存时间可以被视为监督信息，以提高相似图的质量并探索临床上更相关的患者亚组。对几个代表性多组学癌症数据集的实验表明，所提出的方法比许多最先进的方法取得了更好的结果。结果还表明，我们的方法能够识别不同癌症类型的具有生物学意义的亚组。 （我们的 Matlab 源代码可在 github 上在线获取：https://github.com/CLiu272/S2GC）