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Related Concept Videos

Overview of Lipid Metabolism01:24

Overview of Lipid Metabolism

Lipid metabolism is a crucial process in the human body that involves the synthesis and degradation of lipids. This process is essential for energy production, cell membrane formation, and hormone production, among other functions.
Lipolysis: The Breakdown of Lipids:
Lipolysis is the process of breaking down lipids, particularly triglycerides, into glycerol and fatty acids. This process typically occurs in the adipose tissue and is triggered by various hormones, including glucagon and...

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Updated: May 28, 2026

Shotgun Lipidomics of Rodent Tissues
11:46

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Published on: November 18, 2022

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

Maria-Christina P Papatheodorou1, Panagiotis Vlamos1, Marios G Krokidis1

  • 1Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.

Metabolites
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study provides a roadmap for analyzing lipidomics data using R, a programming language. It details a pipeline from data processing to biological interpretation, enhancing reproducibility and biomarker discovery.

Keywords:
R librariesdata processingfunctional analysislipid ontologylipidomicsmulti-omics integration

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Published on: March 18, 2022

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.