Supervised Graph Clustering for Cancer Subtyping Based on Survival Analysis and Integration of Multi-Omic Tumor Data

作     者：Liu, Cheng Cao, Wenming Wu, Si Shen, Wenjun Jiang, Dazhi Yu, Zhiwen Wong, Hau-San 

作者机构：Shantou Univ Dept Comp Sci Shantou 515063 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China Shantou Univ Med Coll Shantou 515041 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China 

出 版 物：《IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS》 (IEEE/ACM Trans. Comput. BioL. Bioinf.)

年 卷 期：2022年第19卷第2期

页      面：1193-1202页

核心收录：

学科分类：0710[理学-生物学] 0808[工学-电气工程] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Department of Education of Guangdong Province [2018KQNCX075] City University of Hong Kong [7004884, 7005055] Natural Science Foundation of Guangdong Province [2019A1515010943] Key Project of Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Natural Science) [2018KZDXM035] Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Artificial Intelligence) [2019KZDZX1030] Li Ka Shing Foundation Cross-Disciplinary Research Grant [2020LKSFG04D, 2020LKSFG07B] 

主　　题：Cancer Analytical models Adaptation models Optimization Bioinformatics Silicon Multi-omic data Cancer subtype identification survival analysis data integration 

摘      要：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://***/CLiu272/S2GC)