Multi-dimensional data integration algorithm based on random walk with restart

BMC Bioinformatics

Xinyu Song¹  Bowei Yan²  Song He²  Yuqi Wen²  Dongjin Leng²  Xiaochen Bo²  Lianlian Wu³  Xiaoxi Yang⁴ 

[1] Department of Biomedical Engineering, Chinese PLA General Hospital, 100853, Beijing, People’s Republic of China;Department of Biotechnology, Beijing Institute of Radiation Medicine, 100850, Beijing, People’s Republic of China;Department of Biotechnology, Beijing Institute of Radiation Medicine, 100850, Beijing, People’s Republic of China;Academy of Medical Engineering and Translational Medicine, Tianjin University, 300072, Tianjin, People’s Republic of China;Experimental Center, Beijing Friendship Hospital, Capital Medical University, 100069, Beijing, People’s Republic of China;

关键词: Random walk with restart;    Multiplex network;    Multi-dimensional data integration;    Cancer subtyping;   

DOI  :  10.1186/s12859-021-04029-3

来源: Springer

【 摘 要 】

BackgroundThe accumulation of various multi-omics data and computational approaches for data integration can accelerate the development of precision medicine. However, the algorithm development for multi-omics data integration remains a pressing challenge.ResultsHere, we propose a multi-omics data integration algorithm based on random walk with restart (RWR) on multiplex network. We call the resulting methodology Random Walk with Restart for multi-dimensional data Fusion (RWRF). RWRF uses similarity network of samples as the basis for integration. It constructs the similarity network for each data type and then connects corresponding samples of multiple similarity networks to create a multiplex sample network. By applying RWR on the multiplex network, RWRF uses stationary probability distribution to fuse similarity networks. We applied RWRF to The Cancer Genome Atlas (TCGA) data to identify subtypes in different cancer data sets. Three types of data (mRNA expression, DNA methylation, and microRNA expression data) are integrated and network clustering is conducted. Experiment results show that RWRF performs better than single data type analysis and previous integrative methods.ConclusionsRWRF provides powerful support to users to decipher the cancer molecular subtypes, thus may benefit precision treatment of specific patients in clinical practice.

【 授权许可】

CC BY