GScluster: network-weighted gene-set clustering analysis

• Published in: BMC genomics Vol. 20; no. 1; p. 352
• Main Authors: Yoon, Sora, Kim, Jinhwan, Kim, Seon-Kyu, Baik, Bukyung, Chi, Sang-Mun, Kim, Seon-Young, Nam, Dougu
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 09.05.2019; BioMed Central; BMC
• Subjects: Algorithms; Animals; Biochemistry; Cluster analysis; Clustering; Cytotoxicity; Diabetes Mellitus, Type 2 - genetics; Distance measurement; DNA microarrays; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation; Gene Regulatory Networks; Gene sequencing; Gene-set analysis; Gene-set clustering; Genes; Genetic research; Genomics; Humans; Hypertension; Kinases; Leukemia; Medical prognosis; Methodology; Methods; Neoplasms - genetics; Network; Network analysis; Novels; Post-processing; Protein interaction; Protein Interaction Mapping - methods; Protein-protein interaction; Protein-protein interactions; Proteins; Ribonucleic acid; RNA; Software; Gene-set clustering; Gene-set analysis; Protein-protein interaction; Network
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/s12864-019-5738-6

Gene-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. Here, 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. Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.