MetaProViz enables the user to pre-process metabolomics data including consumption-release (CoRe) data, to perform differential analysis (DMA), do clustering based on regulatory rules (MCA), pathway analysis (ORA) and contains different visualization methods to extract biological interpretable graphs.
We have generated several tutorials showcasing the different functionalities MetaProViz offers using publicly available datasets, which are included as example data within MetaProViz. You can find those tutorial on the top under the “Tutorial” button, where you can follow specific user case examples for different analysis. Otherwise, you can also follow the links below:
Here you will find a brief overview and information about the installation of the package and its dependencies.
MetaProViz is an R package and to install the package, start R and enter:
devtools::install_github("https://github.com/saezlab/MetaProViz")Now MetaProViz can be imported as:
library(MetaProViz)If you are using MetaProViz the following packages are required:
"tidyverse"
"ggplot2"
"factoextra"
"qcc"
"hash"
"reshape"
"gridExtra"
"inflection"
"patchwork"
"clusterProfiler"
"ggupset"
"gtools"
"EnhancedVolcano"
"writexl"
"pheatmap"
"ggfortify"
While we have done our best to ensure all the dependencies are documented, if they aren’t please let us know and we will try to resolve them.
Note if you are running Windows you might have an issue with long paths, which you can resolve in the registry on Windows 10: Computer Configuration > Administrative Templates > System > Filesystem > Enable Win32 long paths (If you have a different version of Windows, just google “Long paths fix” and your Windows version)
GNU GENERAL PUBLIC LICENSE, Version 3, 29 June 2007
@Manual{,
title = {MetaProViz: METabolomics pre-PRocessing, functiOnal analysis and VIZualisation},
author = {Christina Schmidt and Dimitrios Prymidis and Julio Saez-Rodriguez and Christian Frezza},
year = {2023},
note = {R package version 2.0.1},
}