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GScluster: network-weighted gene-set clustering analysis.


ABSTRACT: BACKGROUND: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. RESULTS: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. CONCLUSIONS:Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.

SUBMITTER: Yoon S 

PROVIDER: S-EPMC6507172 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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GScluster: network-weighted gene-set clustering analysis.

Yoon Sora S   Kim Jinhwan J   Kim Seon-Kyu SK   Baik Bukyung B   Chi Sang-Mun SM   Kim Seon-Young SY   Nam Dougu D  

BMC genomics 20190509 1


<h4>Background</h4>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.<h4>Results</h4>Here, we presented a novel network-weighted gene-set clustering that  ...[more]

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