Lipid set enrichment analysis

Lipid Set Enrichment Analysis (LSEA) Ranked by Log Fold-Change

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Summary

Lipid set enrichment analysis (LSEA) identifies significantly enriched or depleted lipid classes and chain properties by ranking individual lipid molecules according to log fold-change from differential expression results. This technique reveals coordinated regulation patterns across predefined lipid sets that may not be apparent from individual lipid statistics alone.

When to use

Apply this skill after completing two-group or multi-group differential expression analysis on lipidomics data when you have computed log fold-change (logFC) values and want to identify which lipid classes (e.g., PC, PG, CL, TG) or chain-length/unsaturation features show coordinated enrichment or depletion across the ranked lipid list, rather than examining individual lipids in isolation.

When NOT to use

• Input lipid-level data lacks log fold-change values or statistical ranking (use univariate analysis first)
• Sample size is too small to reliably estimate differential expression (typically need n ≥ 3 per group)
• Research question focuses on individual lipid biomarkers rather than coordinated lipid class regulation

Inputs

• de_analysis result object with logFC values
• Two-group or multi-group differential expression table with log fold-change computed
• Predefined lipid set definitions (provided by lipidr)

Outputs

• significant_lipidsets table filtered by significance threshold
• Enrichment scores and adjusted p-values for each lipid set
• Visualizations (e.g., plots and tables) of enriched/depleted lipid classes and chain properties

How to apply

Load the differential expression result object containing logFC values (e.g., from benign vs. cancer or cancer vs. metastasis comparisons). Call the lsea() function with rank.by='logFC' parameter to rank all measured lipid molecules by their log fold-change values and compute enrichment statistics against predefined lipid sets (organized by lipid class, chain length, and unsaturation). Extract the significant_lipidsets table from lsea output and filter for lipid sets meeting your significance threshold (typically adjusted p-value < 0.05). Visualize and tabulate the results to show enrichment scores, adjusted p-values, and the direction of enrichment for each significant lipid set.

Related tools

• lipidr (R package providing lsea() function, differential expression analysis (de_analysis), and visualization of enrichment results) — https://github.com/ahmohamed/lipidr
• limma (Underlying R package required for statistical computation of differential expression and logFC values)
• R (Programming language and runtime for executing lipidr and enrichment analysis workflow)

Examples

lsea_result <- lsea(two_group, rank.by='logFC'); significant_sets <- lsea_result$significant_lipidsets; plot_enrichment(lsea_result)

Evaluation signals

• Significant lipid sets table is non-empty and contains expected lipid classes (PC, PG, CL, TG, etc.)
• Adjusted p-values of significant lipid sets are below the chosen threshold (e.g., padj < 0.05)
• Enrichment direction (up-regulated vs. down-regulated) is biologically concordant with the comparison (e.g., PCs and PGs up-regulated in cancer vs. benign)
• Visualization shows clear separation or clustering of enriched lipid sets with interpretable scores and p-values
• Results are reproducible when re-running lsea() with the same rank.by='logFC' parameter and threshold

Limitations

• LSEA requires predefined lipid set annotations; results are limited to lipid classes, chain lengths, and unsaturation patterns encoded in the reference sets
• Ranking by logFC alone does not account for effect size variability or confidence intervals; integration with p-values or adjusted effect sizes may provide additional context
• Small sample sizes may lead to unstable logFC estimates and reduced statistical power for enrichment detection
• The method assumes independence between lipid molecules within sets, which may not hold for co-regulated pathways or structural isomers

Evidence

• [other] Running lipid set enrichment analysis (lsea) with rank.by='logFC' on two-group differential results identifies significant lipid sets that can be extracted and visualized to show enriched lipid classes and chain unsaturations.: "Running lipid set enrichment analysis (lsea) with rank.by='logFC' on two-group differential results identifies significant lipid sets that can be extracted and visualized to show enriched lipid"
• [other] Call lsea() function with rank.by='logFC' parameter to rank lipid molecules by log fold-change and compute enrichment statistics for predefined lipid sets.: "Call lsea() function with rank.by='logFC' parameter to rank lipid molecules by log fold-change and compute enrichment statistics for predefined lipid sets."
• [other] Extract the significant_lipidsets table from lsea output, filtering for lipid classes and chain-length features meeting the significance threshold.: "Extract the significant_lipidsets table from lsea output, filtering for lipid classes and chain-length features meeting the significance threshold."
• [readme] A novel lipid set enrichment analysis is implemented to detect preferential regulation of certain lipid classes, total chain lengths or unsaturation patterns.: "A novel lipid set enrichment analysis is implemented to detect preferential regulation of certain lipid classes, total chain lengths or unsaturation patterns."
• [intro] PCs and PGs up-regulated and CLs and TGs down-regulated in cancer tissues compared to benign samples: "PCs and PGs up-regulated and CLs and TGs down-regulated in cancer tissues compared to benign samples"