A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight

Area of Science:

• Biomedical Research
• Computational Biology
• Data Science

Background:

• Lipidomics offers high-resolution insights into metabolic signaling and disease.
• The R programming language is a robust framework for analyzing complex lipidomic datasets.
• A standardized analytical pipeline is crucial for reproducible lipidomics research.

Purpose of the Study:

• To present a comprehensive roadmap for lipidomics analysis in R.
• To integrate and contextualize key R packages for a standardized analytical lifecycle.
• To emphasize reproducibility, nomenclature standardization, and machine learning in biomarker discovery.

Main Methods:

• Utilized R packages including xcms, MSnbase, LipidMS 3.0, lipidr, mixOmics, and clusterProfiler.
• Structured the analysis pipeline around data acquisition, preprocessing, annotation, statistical modeling, and interpretation.
• Demonstrated integration of advanced tools for bridging lipid abundance and biological insights.

Main Results:

• A coherent R-based pipeline for lipidomics analysis was synthesized.
• Methodological pitfalls, statistical assumptions, and reproducibility constraints were discussed.
• The guide facilitates systematic tool selection for translating lipidomic signatures into discoveries.

Conclusions:

• The R roadmap accelerates the translation of complex lipidomic signatures into reproducible and clinically meaningful discoveries.
• Emphasis on standardization and advanced tools enhances the reliability and impact of lipidomics studies.
• This approach supports researchers in navigating the complexities of lipidomics data analysis.