MetaProViz (Metabolomics Processing, functional analysis and Visualization), a free open-source R-package that provides mechanistic hypotheses from metabolomics data by integrating prior knowledge from literature with metabolomics. MetaProViz offers an interactive framework consisting of four modules: Processing, differential analysis, functional analysis and visualization of both intracellular and exometabolomics (=consumption-release (CoRe) data). Those modules and their functions can be used independently from each other or in combination (Fig.1).
Fig. 1: Overview of MetaProViz functions.The first module, MetaProViz, Processing
, allows the customized
processing of raw peak metabolomics data from different experimental
setups, including options to perform feature filtering due to
missingness, Total Ion Count (TIC) normalisation, Missing Value
Imputation (MVI) based on half-minimum and outlier detection based on
Hotellin’s T2. All of these pre-processing parameters can be customized
and combined as needed.
The second module of MetaProViz,
Differential Metabolite Analysis (DMA)
, allows the user to perform
differential analysis between two conditions (e.g. Tumour versus
Healthy) calculating the Log2FC, p-value, adjusted p-value and t-value,
whereby the user can choose all the test statistics. The input can
either be the output of the Preprocessing
module or any DF including
metabolite values and information about the conditions that should be
compared.
The third module of MetaProViz, Functional Analysis
, includes
different methods to create clusters of metabolites based on their
distribution across the data using logical regulatory rules, prior
knowledge for enrichment analysis and functions to perform over
representation analysis (ORA). Here, the user can either input the
output of the Processing
or Differential Metabolite Analysis (DMA)
module, or any other DF including Log2FC and statistics or metabolite
values.
The fourth module of MetaProViz, Visualization
, can easily create
customized visualizations of the output results of each of the other
MetaProViz modules or custom files. Here we not only enable overview
plots such as PCA, heatmap, Volcano plot, but also individual graphs of
each metabolite as bar graphs, box plots or violin plots. Moreover, the
user can provide additional information such as pathways the metabolites
correspond to, the clusters the metabolites where assigned to or any
other meta-information to customize the plots for color, shape or
selections, thus enabling biological interpretation of the results
otherwise missed in the data.
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 “Tutorials” button, where you can follow specific user case examples for different analysis. Otherwise, you can also follow the links below:
- Standard metabolomics data
- Consumption-Release (CoRe) metabolomics data from cell culture media
- Enrichment Analysis
- Metadata Analysis
Here you will find a brief overview and information about the installation of the package and its dependencies.
clear cell Renal Cell Carcinoma (ccRCC) patients data from Hakimi et. al including 138 matched tumour and normal tissue pairs [@Hakimi2016]. Cell-lines data from intra- and extracellular metabolomics data from cell culture media from metabolomics workbench project PR001418.
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, Dimitrios Prymidis, Macabe Daley, Denes Turei, Julio Saez-Rodriguez and Christian Frezza},
year = {2023},
note = {R package version 2.1.3},
}