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MetaProViz

Short Introduction

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 five modules: Processing, differential analysis, prior knowledge access and refactoring, 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.
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 Hotelling’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 Processing 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 were 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.

Tutorials

We have written several tutorials that showcase what MetaProViz offers, using publicly available datasets that are included as example data in MetaProViz. You find all of them on the tutorials page and under “Tutorials” at the top of the website. They are grouped into:

  • Get started: a short tour through the main steps.
  • Analysis workflows: standard (intracellular) metabolomics, consumption-release (CoRe) metabolomics from cell culture media, and patient sample metadata.
  • Prior knowledge and metabolite IDs: accessing and integrating prior knowledge, processing metabolite IDs, and the MetSigDB resources.

Here you will find a brief overview and information about the installation of the package and its dependencies.

Example Data

Clear cell renal cell carcinoma (ccRCC) patients data from Hakimi et al. including 138 matched tumour and normal tissue pairs (Hakimi et al. 2016). Cell-lines data from intra- and extracellular metabolomics data from cell culture media from metabolomics workbench project PR001418.

Additionally we also added transcriptomics and proteomics data of ccRCC patients processed with SiRCle (Mora et al. 2024), originally from PDC000127 (Clark et al. 2019).

Installation

MetaProViz is a Bioconductor R package. To install the package, start R and enter:

# 1. Install MetaProViz from Bioconductor devel:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(version = "devel")
BiocManager::install("MetaProViz")

# 2. Install the latest development version from GitHub using devtools
# Install Rtools if you haven’t done this yet, using the appropriate version (e.g. Windows or macOS).
# remotes::install_github("saezlab/MetaProViz")

Now MetaProViz can be imported as:

OmnipathR version requirement

MetaProViz requires OmnipathR version >= 3.17.4. If this version is not available in your Bioconductor release, you can install it from Bioconductor devel with:

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install(version = "devel")
BiocManager::install("OmnipathR")

Windows specifications

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)

License

BSD 3-Clause License, see LICENSE.md.

Contributors

Christina Schmidt, Jannik Franken, Denes Turei, Dimitrios Prymidis, Macabe Daley, Christian Frezza, Julio Saez-Rodriguez

Citation

Please cite our published article: https://link.springer.com/article/10.1038/s44320-026-00231-8

@article{Schmidt_Franken_Turei_Prymidis_Daley_Frezza_Saez-Rodriguez_2026,
         title={Integrated metabolomics data analysis to generate mechanistic hypotheses with MetaProViz},
         DOI={10.1038/s44320-026-00231-8},
         journal={Molecular Systems Biology},
         author={Schmidt, Christina and Franken, Jannik and Turei, Denes and Prymidis, Dimitrios and Daley, Macabe and Frezza, Christian and Saez-Rodriguez, Julio},
         year={2026},
         month={Aug}}

References

Clark, David J, Saravana M Dhanasekaran, Francesca Petralia, Jianbo Pan, Xiaoyu Song, Yingwei Hu, Felipe da Veiga Leprevost, et al. 2019. “Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.” Cell 179 (4): 964–983.e31. https://doi.org/10.1016/j.cell.2019.10.007.

Hakimi, A Ari, Ed Reznik, Chung-Han Lee, Chad J Creighton, A Rose Brannon, Augustin Luna, B Arman Aksoy, et al. 2016. “An Integrated Metabolic Atlas of Clear Cell Renal Cell Carcinoma.” Cancer Cell 29 (1): 104–16. https://doi.org/10.1016/j.ccell.2015.12.004.

Mora, Ariane, Christina Schmidt, Brad Balderson, Christian Frezza, and Mikael Bodén. 2024. “SiRCle (Signature Regulatory Clustering) Model Integration Reveals Mechanisms of Phenotype Regulation in Renal Cancer.” Genome Medicine 16 (1): 144. https://doi.org/10.1186/s13073-024-01415-3.