Suppr超能文献

netGO:用于网络集成通路富集分析的 R-Shiny 软件包。

netGO: R-Shiny package for network-integrated pathway enrichment analysis.

机构信息

School of Life Sciences.

Department of Mathematical Sciences, Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.

出版信息

Bioinformatics. 2020 May 1;36(10):3283-3285. doi: 10.1093/bioinformatics/btaa077.

Abstract

SUMMARY

We present an R-Shiny package, netGO, for novel network-integrated pathway enrichment analysis. The conventional Fisher's exact test (FET) considers the extent of overlap between target genes and pathway gene-sets, while recent network-based analysis tools consider only network interactions between the two. netGO implements an intuitive framework to integrate both the overlap and networks into a single score, and adaptively resamples genes based on network degrees to assess the pathway enrichment. In benchmark tests for gene expression and genome-wide association study (GWAS) data, netGO captured the relevant gene-sets better than existing tools, especially when analyzing a small number of genes. Specifically, netGO provides user-interactive visualization of the target genes, enriched gene-set and their network interactions for both netGO and FET results for further analysis. For this visualization, we also developed a standalone R-Shiny package shinyCyJS to connect R-shiny and the JavaScript version of cytoscape.

AVAILABILITY AND IMPLEMENTATION

netGO R-Shiny package is freely available from github, https://github.com/unistbig/netGO.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

摘要

我们提出了一个 R-Shiny 包 netGO,用于新颖的网络综合途径富集分析。传统的 Fisher 精确检验(FET)考虑了目标基因与途径基因集之间的重叠程度,而最近的基于网络的分析工具只考虑了两者之间的网络相互作用。netGO 实现了一个直观的框架,将重叠和网络集成到一个单一的分数中,并根据网络度自适应地对基因进行重采样,以评估途径的富集情况。在基因表达和全基因组关联研究(GWAS)数据的基准测试中,netGO 比现有工具更好地捕获了相关的基因集,特别是在分析少量基因时。具体来说,netGO 为 netGO 和 FET 结果提供了目标基因、富集基因集及其网络相互作用的用户交互可视化,以供进一步分析。对于这种可视化,我们还开发了一个独立的 R-Shiny 包 shinyCyJS,用于连接 R-shiny 和 cytoscape 的 JavaScript 版本。

可用性和实现

netGO R-Shiny 包可从 github 免费获得,网址为 https://github.com/unistbig/netGO。

补充信息

补充数据可在生物信息学在线获得。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验