Pathview is a leading tool for pathway based data integration and visualization. It maps, integrates and renders a wide variety of biological data on relevant pathway graphs. Pathview has 3 important features: Interpretable graphs with publication quality: KEGG view for easy interpretation and Graphviz view for better graphical control. Strong data integration capacity. It works with: 1) all data mappable to pathways, 2) 30 of molecular ID types (genes/protein, compound/metabolite), 3) 5000+ species, 4) various data attributes and formats. * Simple and powerful: fully automated and error-resistant, seamlessly integrates with a wide range of pathway and gene set (enrichment) analysis tools.
Please cite the Pathview paper when using this open-source package. This will help the project and our team:
Luo W, Brouwer C. Pathview: an R/Biocondutor package for pathway-based data integration and visualization. Bioinformatics, 2013, 29(14):1830-1831, doi: 10.1093/bioinformatics/btt285
# install from BioConductor if(!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("pathview") # Or the development version from GitHub: # install.packages("devtools") devtools::install_github("datapplab/pathview")
Note Pathview focuses on KEGG pathways, which is good for most regular analyses. If you are interested in working with other major pathway databases, including Reactome, MetaCyc, SMPDB, PANTHER, METACROP etc, you can use SBGNview. Please check the quick start page and the main tutorial for details.
library(pathview) data(gse16873.d) pv.out <- pathview(gene.data = gse16873.d[, 1], pathway.id = "04110", species = "hsa", out.suffix = "gse16873")
Please check the BioC page for tutorials and extra documentations.
Also see the Pathview Web server for interactive GUI with example graphics.
Thank you for your interest.
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