GScluster: network-weighted gene-set clustering analysis

BMC Genomics

Sora Yoon¹  Jinhwan Kim¹  Bukyung Baik¹  Dougu Nam²  Sang-Mun Chi³  Seon-Kyu Kim⁴  Seon-Young Kim⁵ 

[1] 0000 0004 0381 814X, grid.42687.3f, School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea;0000 0004 0381 814X, grid.42687.3f, School of Life Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea;0000 0004 0381 814X, grid.42687.3f, Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea;0000 0004 0533 0818, grid.411236.3, School of Computer Science and Engineering, Kyungsung University, Busan, Republic of Korea;0000 0004 0636 3099, grid.249967.7, Epigenomics Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea;0000 0004 0636 3099, grid.249967.7, Genome Structure Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, South Korea;0000 0004 1791 8264, grid.412786.e, Department of Functional Genomics, University of Science and Technology (UST), 34141, Daejeon, Republic of Korea;0000 0004 0636 3099, grid.249967.7, Genome Editing Research Center, Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), 34141, Daejeon, Republic of Korea;

关键词: Gene-set clustering;    Gene-set analysis;    Protein-protein interaction;    Network;   

DOI  :  10.1186/s12864-019-5738-6

来源: publisher

【 摘 要 】

BackgroundGene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets.ResultsHere, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks.ConclusionsNetwork-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.

【 授权许可】

CC BY