Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data

• Objective: This review compiles best practices and freely accessible tools in R and Python for the statistical processing and visualization of extensive mass spectrometry-based lipidomics and metabolomics data.
• Focus: The article provides a “solid core” of resources for exploratory data analysis (EDA) and visualization to help researchers identify and visualize statistically significant trends and biologically relevant differences within their complex datasets.
• Implication: It guides researchers on using modern computational platforms (R/Python) and integrating metadata (e.g., clinical parameters) with their omics data to perform robust and reproducible downstream analysis.

PubMed: 41027880 DOI: 10.1038/s41467-025-63751-1 Overview generated by: Gemini 2.5 Flash, 28/11/2025

Key Focus: Data Analysis Tools and Best Practices in R and Python

This review article serves as a comprehensive guide and compilation of best practices and freely accessible tools in R and Python for the statistical processing and visualization of mass spectrometry-based lipidomics and metabolomics data. The authors acknowledge that these “omics” generate extensive datasets that require specific data exploration skills to effectively identify and visualize statistically significant trends and biologically relevant differences.

The Need for Dedicated Tools

Mass spectrometry-based lipidomics and metabolomics workflows are characterized by high-dimensional data, complex normalization needs, and the necessity to integrate data with extensive metadata (such as clinical parameters). Standard spreadsheet software is insufficient for handling the volume and complexity of these datasets. The review addresses this gap by compiling and discussing computational resources tailored for this purpose.

Core Areas Covered by the Review

The review focuses on the core stages of post-acquisition data analysis, primarily emphasizing exploratory data analysis (EDA) and visualization:

1. Statistical Processing: Tools and packages for performing univariate and multivariate statistical analyses, including methods like Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA), which are standard for feature reduction and visualizing separation between groups.
2. Visualization: Compilations of packages for generating high-quality graphical representations essential for biological interpretation, such as volcano plots, heatmaps, box plots, and specialized lipid/metabolite class distribution plots.
3. Best Practices: Discussion of standardized workflows and best practices to ensure reproducibility and accurate results in data handling, which is critical given the inherent variability in mass spectrometry data.
4. R and Python Focus: The article prioritizes tools within the R and Python ecosystems, which are the dominant platforms for modern biological data analysis due to their powerful statistical libraries and open-source nature.

Conclusion and Utility

The review provides a valuable resource for researchers in the lipidomics and metabolomics fields, compiling the solid core of accessible tools required for transforming raw data into biologically meaningful insights. By focusing on R and Python, it guides users toward implementing robust, reproducible, and effective computational strategies for their high-dimensional data.