Bioinformatics AdvancesBioinformaticsPeer-reviewed journal article
xOmicsShiny: an R Shiny application for cross-omics data analysis and pathway mapping
Benbo GaoiD, Yu Huining SuniD, Xinmin ZhangiD, Tin-Chi LiniD, Wei Li, Romi Admanit, Baohong ZhangiD
Abstract
SummaryWe developed xOmicsShiny, a feature-rich R Shiny-powered application that enables biologists to fully explore omics datasets across experiments and data types, with an emphasis on uncovering biological insights at the pathway level. The data merging feature ensures flexible exploration of cross-omics data, such as transcriptomics, proteomics, metabolomics, and lipidomics. The pathway mapping function covers a broad range of databases, including WikiPathways, Reactome, and KEGG pathways. In addition, xOmicsShiny offers several visualization options and analytical tasks for everyday omics data analysis, namely, PCA, Volcano plot, Venn Diagram, Heatmap, WGCNA, and advanced clustering analyses. The application employs customizable modules to perform various tasks, generating both interactive plots and publication-ready figures. This dynamic, modular design overcomes the issue of slow loading in R Shiny tools and allows it to be readily expanded by the research and developer community. Availability and implementationThe R Shiny application is publicly available at: http://xOmicsShiny.bxgenomics.com. Researchers can upload their own data to the server or use the pre-loaded demo dataset. The source code, under MIT license is provided at https://github.com/interactivereport/xOmicsShiny for local installation. A full tutorial of the application is available at https://interactivereport.github.io/xOmicsShiny/tutorial/docs/index.html. Contactyuhenry.sun@biogen.com or baohong.zhang@biogen.com Supplementary dataSupplementary data are available at bioRxiv online.
- assay: proteomics
- assay: metabolomics
- assay: metagenomics
- assay: bulk RNA-seq
- research focus: database / resource
- research focus: dimensionality reduction
- research focus: clustering
- approach: statistical model
- Citations
- 5
- FWCI
- 0.68
- Field percentile
- top 34%
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