bio-metabolomics-lipidomics

name	bio-metabolomics-lipidomics
description	Analyze lipidomics data for lipid species identification, quantification, and pathway interpretation using LipidSearch, MS-DIAL, and LIPID MAPS. Use when: user has lipidomics data, needs lipid class annotation, wants to analyze sphingolipids/phospholipids/fatty acids, or interpret lipid-specific pathways. Triggers: lipid species, lipidomics, sphingolipid, phospholipid, fatty acid, ceramide, triglyceride, lipid class, LIPID MAPS, LipidSearch, lipidr, lipid annotation, chain composition, lipid profiling, phosphatidylcholine, PE, PC, SM.
tool_type	mixed
primary_tool	lipidr
upstream	{"repo":"https://github.com/GPTomics/bioSkills","license":"MIT","original_author":"GPTomics"}

Version Compatibility

Reference examples tested with: ggplot2 3.5+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, xcms 4.0+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Lipidomics Analysis

"Analyze my lipidomics data" → Identify and quantify lipid species by class and chain composition, then perform differential lipid analysis and pathway interpretation.

• R: lipidr::as_lipidomics_experiment(), de_analysis()
• CLI: MS-DIAL or LipidSearch for lipid identification

R Workflow with lipidr

library(lipidr)
library(ggplot2)

# Load lipidomics data (LipidSearch or Skyline format)
lipid_data <- read_lipidomes('lipidsearch_export.csv', data_type = 'LipidSearch')

# Or from generic matrix
lipid_data <- as_lipidomics_experiment(
    data = intensity_matrix,
    sample_info = sample_metadata,
    lipid_info = lipid_annotations
)

# Data summary
print(lipid_data)
plot_samples(lipid_data, type = 'tic')

Lipid Annotation

# Parse lipid names to extract class, chain info
lipid_data <- annotate_lipids(lipid_data)

# View lipid classes
table(rowData(lipid_data)$Class)

# Chain length and saturation
plot_chain_distribution(lipid_data)

Normalization

# Normalize by internal standards
lipid_data <- normalize_pqn(lipid_data)

# Or by specific internal standard class
lipid_data <- normalize_istd(lipid_data, istd_class = 'PC')

# Log transform
lipid_data <- log_transform(lipid_data)

# QC plot
plot_samples(lipid_data, type = 'boxplot')

Differential Analysis

# Define contrasts
de_results <- de_analysis(
    lipid_data,
    Treatment - Control,
    measure = 'Area'
)

# Significant lipids
sig_lipids <- significant_lipids(de_results, p.cutoff = 0.05, logFC.cutoff = 1)

# Volcano plot
plot_results_volcano(de_results, show.labels = TRUE)

# By lipid class
plot_results_volcano(de_results, facet = 'Class')

Enrichment Analysis

# Lipid class enrichment
enrich_results <- lsea(de_results, rank.by = 'logFC')

# Plot enrichment
plot_enrichment(enrich_results, significant.only = TRUE)

# Chain length enrichment
chain_enrich <- lsea(de_results, rank.by = 'logFC', type = 'chain')

Python Workflow with LipidFinder

Goal: Identify and classify lipid species from LC-MS data using PyOpenMS and LipidMaps annotation.

Approach: Load mzML data, extract features from XCMS preprocessing, annotate by m/z against LipidMaps, and parse lipid nomenclature for class and chain composition.

import pandas as pd
import numpy as np
from pyopenms import MSExperiment, MzMLFile

# Load mzML
exp = MSExperiment()
MzMLFile().load('lipidomics.mzML', exp)

# Extract lipid features (after XCMS preprocessing)
features = pd.read_csv('xcms_features.csv')

# LipidMaps annotation by m/z
def annotate_lipidmaps(mz, adduct='[M+H]+', tolerance_ppm=10):
    '''Query LipidMaps for lipid annotation'''
    import requests

    url = f'https://www.lipidmaps.org/rest/compound/lm_id/{mz}'
    # Note: Use local database for production
    return None  # Placeholder

# Parse lipid nomenclature
def parse_lipid_name(name):
    '''Extract lipid class and chain info from shorthand notation'''
    import re

    pattern = r'(\w+)\s*\((\d+):(\d+)(?:/(\d+):(\d+))?\)'
    match = re.match(pattern, name)

    if match:
        lipid_class = match.group(1)
        chain1_carbon = int(match.group(2))
        chain1_unsat = int(match.group(3))
        return {
            'class': lipid_class,
            'total_carbons': chain1_carbon,
            'total_unsaturation': chain1_unsat
        }
    return None

# Example
parse_lipid_name('PC(34:1)')  # {'class': 'PC', 'total_carbons': 34, 'total_unsaturation': 1}

MS-DIAL Lipidomics

# Load MS-DIAL alignment results
msdial_data <- read.csv('msdial_lipidomics.csv')

# Extract lipid annotations
lipid_cols <- c('Metabolite.name', 'Ontology', 'INCHIKEY', 'SMILES')
annotations <- msdial_data[, lipid_cols]

# Intensity matrix
intensity_cols <- grep('Area', colnames(msdial_data), value = TRUE)
intensities <- msdial_data[, intensity_cols]

# Filter by annotation confidence
high_conf <- msdial_data$Annotation.tag == 'Lipid'
msdial_lipids <- msdial_data[high_conf, ]

Lipid Class Visualization

library(ggplot2)

# Summarize by class
class_summary <- lipid_data %>%
    group_by(Class, Condition) %>%
    summarise(mean_intensity = mean(Intensity), .groups = 'drop')

# Stacked bar plot
ggplot(class_summary, aes(x = Condition, y = mean_intensity, fill = Class)) +
    geom_bar(stat = 'identity', position = 'fill') +
    scale_fill_brewer(palette = 'Set3') +
    theme_bw() +
    labs(y = 'Relative Abundance', title = 'Lipid Class Composition')
ggsave('lipid_class_composition.png', width = 8, height = 6)

# Heatmap by class
library(pheatmap)
class_matrix <- lipid_data %>%
    group_by(Class, Sample) %>%
    summarise(total = sum(Intensity), .groups = 'drop') %>%
    pivot_wider(names_from = Sample, values_from = total)

pheatmap(as.matrix(class_matrix[, -1]),
         labels_row = class_matrix$Class,
         scale = 'row',
         clustering_method = 'ward.D2')

Pathway Mapping

library(KEGGREST)

# Map lipids to KEGG pathways
lipid_kegg <- keggFind('compound', 'lipid')

# Glycerophospholipid metabolism
pathway_lipids <- keggGet('hsa00564')

# Or use LipidMaps classification
# Classes: FA, GL, GP, SP, ST, PR, SL, PK

Saturation Analysis

# Analyze saturation patterns
sat_analysis <- lipid_data %>%
    mutate(
        saturation_class = case_when(
            total_db == 0 ~ 'Saturated',
            total_db == 1 ~ 'Monounsaturated',
            TRUE ~ 'Polyunsaturated'
        )
    ) %>%
    group_by(Condition, saturation_class) %>%
    summarise(mean_abundance = mean(Intensity), .groups = 'drop')

ggplot(sat_analysis, aes(x = Condition, y = mean_abundance, fill = saturation_class)) +
    geom_bar(stat = 'identity', position = 'dodge') +
    theme_bw() +
    labs(title = 'Saturation Profile by Condition')

Export Results

# Comprehensive results table
results_table <- data.frame(
    Lipid = rownames(de_results),
    Class = rowData(lipid_data)$Class,
    Chain = rowData(lipid_data)$total_chain,
    logFC = de_results$logFC,
    pvalue = de_results$P.Value,
    adj_pvalue = de_results$adj.P.Val
)

write.csv(results_table, 'lipidomics_results.csv', row.names = FALSE)

Related Skills

• xcms-preprocessing - Peak detection for lipidomics
• metabolite-annotation - General annotation methods
• statistical-analysis - Multivariate analysis
• pathway-mapping - Lipid pathway enrichment