bio-metabolomics-lipidomics

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.