ndmaSNF: cancer subtype discovery based on integrative framework assisted by network diffusion model

Abstract

// Chao Yang 1 , Shu-Guang Ge 2 and Chun-Hou Zheng 1 1 College of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, China 2 College of Electrical Engineering and Automation, Anhui University, Hefei, Anhui 230601, China Correspondence to: Chun-Hou Zheng, email: zhengch99@126.com Keywords: cancer subtyping, integrative method, network diffusion, somatic mutation data Received: July 03, 2017      Accepted: August 27, 2017      Published: October 06, 2017 ABSTRACT Recently, with the rapid progress of high-throughput sequencing technology, diverse genomic data are easy to be obtained. To effectively exploit the value of those data, integrative methods are urgently needed. In this paper, based on SNF (Similarity Network Diffusion) [1], we proposed a new integrative method named ndmaSNF (network diffusion model assisted SNF), which can be used for cancer subtype discovery with the advantage of making use of somatic mutation data and other discrete data. Firstly, we incorporate network diffusion model on mutation data to make it smoothed and adaptive. Then, the mutation data along with other data types are utilized in the SNF framework by constructing patient-by-patient similarity networks for each data type. Finally, a fused patient network containing all the information from different input data types is obtained by using a nonlinear iterative method. The fused network can be used for cancer subtype discovery through the clustering algorithm. Experimental results on four cancer datasets showed that our ndmaSNF method can find subtypes with significant differences in the survival profile and other clinical features.