| Autor: |
Sora Yoon, Jinhwan Kim, Seon-Kyu Kim, Bukyung Baik, Sang-Mun Chi, Seon-Young Kim, Dougu Nam |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
BMC Genomics, Vol 20, Iss 1, Pp 1-14 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
1471-2164 |
| DOI: |
10.1186/s12864-019-5738-6 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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