Best practices and tools in R and Python forstatistical processing and visualization oflipidomics and metabolomics data

Importance of the Topic

High-throughput mass spectrometry–based lipidomics and metabolomics yield complex, high-dimensional data sets that require tailored statistical methods and visualizations.
Accurate handling of missing values, normalization of batch effects, appropriate data transformation, and scaling are crucial to extract biologically meaningful trends and avoid misleading conclusions.
Freely available R and Python libraries can empower scientists to perform robust, reproducible analyses and generate publication-ready graphics without extensive coding expertise.

Objectives and Overview of the Study

This review summarizes best practices, statistical workflows, and visualization approaches for clinical lipidomics and metabolomics data in R and Python.
It aims to guide beginners through:

• Data pre-processing and quality control
• Univariate and multivariate statistical analyses
• Exploratory dimensionality reduction (PCA, t-SNE, UMAP)
• Supervised modeling (PLS-DA, OPLS-DA) and feature selection
• Generation of publication-ready plots (box plots, volcano plots, lipid networks, heat maps).

The review is accompanied by a GitBook repository containing scripts, step-by-step notebooks, and code examples.

Methodology and Tools

Data Pre-processing:

• Missing value imputation: constant (half-minimum), k-nearest neighbors, random forest
• Batch correction: LOESS, SERRF using QC samples
• Normalization: probabilistic quotient normalization (PQN), sum or median scaling
• Transformation and scaling: log, square-root, autoscaling, Pareto scaling

Univariate Statistics:

• Descriptive statistics and box/violin plots
• Group comparisons: t-test, Welch’s t, Mann–Whitney U, ANOVA, Kruskal–Wallis
• Post hoc: Tukey HSD, Dunn tests
• Volcano plots for fold change vs. p-value

Multivariate and Clustering:

• Unsupervised: PCA (princomp, Factoextra), t-SNE (Rtsne, sklearn.manifold), UMAP (umap, umap-learn)
• Supervised: PLS-DA and OPLS-DA (ropls, mixOmics, caret, tidymodels)
• Hierarchical clustering: Euclidean distance, Ward’s linkage, dendrograms (ggtree, ComplexHeatmap)

Visualization Libraries:

• R: tidyverse, ggplot2, ggpubr, ggstatsplot, plotly, Cytoscape (lipid networks)
• Python: pandas, seaborn, matplotlib, plotly, statsmodels, scikit-learn

Main Results and Discussion

The review distills a core analysis pipeline: data inspection → imputation → normalization → transformation → scaling → statistical testing → visualization.
Key recommendations include:

• Identify missing value mechanisms (MCAR, MAR, MNAR) and choose imputation accordingly
• Use QC samples and randomization to correct systematic batch drifts
• Apply log transformation before scaling to stabilize variance
• Choose non-parametric tests when assumptions of normality or equal variances are violated
• Compare PCA, t-SNE, and UMAP: PCA for global structure and interpretability, UMAP for balanced local/global patterns, t-SNE for local cluster discovery
• Leverage lipid subclass and fatty acyl chain plots to reveal class-specific structural trends

Example applications demonstrate how R and Python scripts can reproduce workflow steps and generate publication-quality outputs.

Benefits and Practical Applications of the Method

Implementing these best practices ensures robust chemometric analysis and reproducibility across laboratories.
Automated pipelines in R and Python reduce manual handling errors and facilitate standardized reporting.
Publication-ready graphics accelerate manuscript preparation and improve data communication to diverse audiences (academia, industry, QA/QC laboratories).

Future Trends and Opportunities

Emerging developments include:

• Integration of machine-learning and deep-learning for automated feature selection and classification
• Cloud-based, interactive dashboards for real-time data exploration
• Standardization of lipidomics and metabolomics reporting checklists and data formats
• Enhanced multi-omics integration using unified R/Python frameworks
• Advanced batch correction algorithms leveraging ensemble and AI-based methods

These directions will further democratize omics data analysis and foster data sharing across the global research community.

Conclusion

This review provides a comprehensive roadmap for conducting statistical analysis and visualization of lipidomics and metabolomics data in R and Python.
By combining theoretical guidance with practical code examples in the GitBook, researchers at all levels can build reproducible pipelines, interpret complex data sets, and generate high-impact visualizations.
Mastery of these open-source tools will enhance analytical rigor and accelerate discoveries in biomedical, clinical, and industrial applications.

Reference

• Olshansky, G. et al. Challenges and opportunities for prevention and removal of unwanted variation in lipidomic studies. Prog. Lipid Res. 87, 101177 (2022).
• Fan, S. et al. Systematic error removal using random forest for normalizing large-scale untargeted lipidomics data. Anal. Chem. 91, 3590–3596 (2019).
• van den Berg, R. A. et al. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom. 7, 142 (2006).
• Wold, S., Trygg, J. & Sjöström, M. Orthogonal projections to latent structures (O-PLS). J. Chemom. 16, 119–128 (2002).
• McInnes, L., Healy, J. & Melville, J. UMAP: Uniform Manifold Approximation and Projection. J. Open Source Softw. 3, 861 (2018).
• Bley, C. R. & Meckwerth, W. ADViSELipidomics: a workflow for analyzing lipidomics data. Bioinformatics 38, 5460–5462 (2022).