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bio-metabolomics-lipidomics

Annotate and quantify lipid species from lipidomics datasets for class- and chain-level analysis.

7|Updated Apr 5, 2026
Search Tags:#normalization#differential analysis#lipidomics#lipidr#lipid annotation#ms-dial#lipidmaps
One-click install
npx skills add https://github.com/dailycafi/metabolism-skills --skill bio-metabolomics-lipidomics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bio-metabolomics-lipidomics
Source: https://github.com/dailycafi/metabolism-skills/tree/main/skills/metabolomics-analysis/lipidomics
Command: npx skills add https://github.com/dailycafi/metabolism-skills --skill bio-metabolomics-lipidomics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Lipidomics datasets require specialized parsing, annotation, normalization, and class-aware statistical analysis that standard metabolomics pipelines do not provide. This Skill makes it straightforward to extract lipid class and chain information, normalize to internal standards, perform differential testing, and generate publication-ready visualizations for lipid-centric studies.

Core Features & Use Cases

  • Lipid annotation and parsing: Extract lipid class, total chain composition, and saturation from LipidSearch, MS-DIAL, or shorthand notation.
  • Normalization & QC: Normalize by internal standards or PQN and produce QC plots to detect batch effects and outliers.
  • Differential and enrichment analysis: Run class- and species-level differential testing, volcano plots, and lipid set enrichment (LSEA) by class or chain features.
  • Visualization & export: Create class composition bar plots, heatmaps, saturation profiles, and export comprehensive result tables for downstream interpretation.
  • Cross-tool workflows: Support R workflows using lipidr, and Python-guided feature annotation from XCMS/PyOpenMS with LipidMaps lookups.

Quick Start

Analyze my lipidomics dataset to annotate lipid classes and chain composition, normalize to internal standards, perform differential analysis between treatment and control, and produce class-level visualizations.

Frequently Asked Questions about bio-metabolomics-lipidomics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I annotate and normalize lipidomics data from MS-DIAL outputs?▼

Lipidomics data from MS-DIAL outputs can be parsed to extract lipid class and chain composition, then normalized using internal standards or PQN to produce QC plots for batch effect detection.

What is lipid set enrichment analysis (LSEA) and how is it applied to lipidomics datasets?▼

LSEA performs lipid set enrichment analysis by grouping lipid species into class or chain features. It identifies statistically significant lipid pathways in comparative experiments alongside differential testing.

Can I use lipidr R workflows with XCMS processed Python feature tables for lipid annotation?▼

Yes, lipidr-compatible R workflows and Python feature tables processed via XCMS are supported. LipidMaps mappings enable cross-tool lipid annotation, class aggregation, and differential analysis.

How do I perform class- and species-level differential analysis on lipidomics data?▼

Class- and species-level differential analysis is performed by running statistical testing on normalized lipidomics data, generating volcano plots and heatmaps to visualize treatment versus control differences.

Does lipidomics differential analysis require LipidSearch annotations for class aggregation?▼

LipidSearch annotations are supported but not strictly required. MS-DIAL annotations, shorthand notation, or LipidMaps mappings can also extract lipid class and total chain composition for downstream enrichment testing.

What visualizations can I generate for lipidomics comparative experiments?▼

Lipidomics comparative experiments support publication-ready visualizations including class composition bar plots, heatmaps, saturation profiles, and volcano plots to interpret differential lipid species results.

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