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

Background The 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. Results Here, we propose a multi-omics dat...

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Vydáno v:	BMC bioinformatics Ročník 22; číslo 1; s. 97 - 22
Hlavní autoři:	Wen, Yuqi, Song, Xinyu, Yan, Bowei, Yang, Xiaoxi, Wu, Lianlian, Leng, Dongjin, He, Song, Bo, Xiaochen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 27.02.2021 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Algorithms Analysis Bioinformatics Biomedical and Life Sciences Cancer Cancer subtyping Cluster Analysis Clustering Computational Biology Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Data entry Data integration Datasets Deoxyribonucleic acid DNA DNA methylation Gene expression Genomes Genomics Humans Integration Life Sciences Machine Learning and Artificial Intelligence in Bioinformatics Methodology Methodology Article Methods Microarrays MicroRNAs MicroRNAs - genetics miRNA Multi-dimensional data integration Multidimensional data Multiplex network Multiplexing Neoplasm Recurrence, Local Noise Precision medicine Probability distribution Random walk Random walk theory Random walk with restart Reproducibility of Results Ribonucleic acid RNA Similarity China Cancer subtyping Multi-dimensional data integration Random walk with restart Multiplex network
ISSN:	1471-2105, 1471-2105
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Shrnutí:	Background The 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. Results Here, 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. Conclusions RWRF provides powerful support to users to decipher the cancer molecular subtypes, thus may benefit precision treatment of specific patients in clinical practice.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-021-04029-3