Supervised graph clustering for cancer subtyping based on survival analysis and integration of multi-omic tumor data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

15 Scopus Citations
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Author(s)

  • Si Wu
  • Wenjun Shen
  • Dazhi Jiang
  • Zhiwen Yu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1193-1202
Number of pages10
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume19
Issue number2
Online published21 Jul 2020
Publication statusPublished - Mar 2022

Abstract

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. 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.

Research Area(s)

  • Multi-omic data, Cancer subtype identification, Survival analysis, Data integration

Citation Format(s)

Supervised graph clustering for cancer subtyping based on survival analysis and integration of multi-omic tumor data. / Liu, Cheng; Cao, Wenming; Wu, Si et al.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 19, No. 2, 03.2022, p. 1193-1202.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review