metabolomics-targeted-analysis

name	metabolomics-targeted-analysis
description	Targeted metabolomics analysis using MRM/SRM with standard curves. Covers absolute quantification, method validation, and quality assessment. Use when quantifying specific metabolites using calibration curves and internal standards.
tool_type	mixed
primary_tool	skyline

Version Compatibility

Reference examples tested with: ggplot2 3.5+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scikit-learn 1.4+, scipy 1.12+, 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.

Targeted Metabolomics Analysis

"Quantify specific metabolites from my MRM data" -> Perform absolute quantification using calibration curves, internal standards, and quality assessment for targeted metabolomics.

• CLI: Skyline for peak integration and export
• Python/R: calibration curve fitting and sample quantification

Skyline Data Export Processing

library(tidyverse)

# Load Skyline export
skyline_data <- read.csv('skyline_export.csv')

# Expected columns: Replicate, Peptide/Molecule, Area, Concentration (for standards)
colnames(skyline_data)

# Filter to quantifier transitions
quant_data <- skyline_data %>%
    filter(Quantitative == TRUE | is.na(Quantitative))

# Pivot to matrix format
intensity_matrix <- quant_data %>%
    select(Replicate, Molecule, Area) %>%
    pivot_wider(names_from = Replicate, values_from = Area)

Standard Curve Fitting

# Standard curve data
standards <- data.frame(
    concentration = c(0, 1, 5, 10, 50, 100, 500, 1000),  # nM
    area = c(100, 5000, 25000, 50000, 240000, 480000, 2300000, 4500000)
)

# Linear regression (log-log for wide range)
fit_linear <- lm(area ~ concentration, data = standards)
fit_loglog <- lm(log10(area) ~ log10(concentration +  data  standards


fit_weighted  lmarea  concentration data  standards
                   weights    standardsconcentration  


summaryfit_linearr.squared
summaryfit_weightedr.squared


ggplotstandards aesx  concentration y  area 
    geom_pointsize   
    geom_smoothmethod   se   
    scale_x_log10 
    scale_y_log10 
    theme_bw 
    labstitle   x   y  

Calculate Concentrations

calculate_concentration <- function(area, fit, method = 'linear') {
    if (method == 'linear') {
        coef <- coef(fit)
        conc <- (area - coef[1]) / coef[2]
    } else if (method == 'loglog') {
        coef <- coef(fit)
        conc <- 10^((log10(area) - coef[1]) / coef[2]) - 1
    
    pmaxconc   



samples  data.frame
    sample  paste0 
    area           


samplesconcentration  calculate_concentrationsamplesarea fit_weighted


dilution_factor  10
samplesconcentration_original  samplesconcentration  dilution_factor

Internal Standard Normalization

# Data with internal standard
data_with_istd <- data.frame(
    sample = paste0('Sample', 1:10),
    analyte_area = c(12000, 45000, 8000, 120000, 35000, 78000, 22000, 95000, 41000, 63000),
    istd_area = c(50000, 52000, 48000, 51000, 49000, 53000, 47000, 50000, 51000, 49000)
)


data_with_istdresponse_ratio  data_with_istdanalyte_area  data_with_istdistd_area


istd_conc  100  
data_with_istdconcentration  calculate_concentration
    data_with_istdresponse_ratio  istd_conc
    fit_weighted

Method Validation Metrics

# Accuracy and precision from QC samples
qc_data <- data.frame(
    level = rep(c('Low', 'Medium', 'High'), each = 6),
    nominal = rep(c(10, 100, 500), each = 6),
    measured = c(
        c(9.5, 10.2, 11.1, 9.8, 10.5, 10.0),
        c(98, 102, 95   
             
    



validation_metrics  qc_data 
    group_bylevel nominal 
    summarise
        mean  meanmeasured
        sd  sdmeasured
        cv_percent  sdmeasured  meanmeasured  
        accuracy_percent  meanmeasured  nominal  
        bias_percent  meanmeasured  nominal  nominal  
        .groups  
    

printvalidation_metrics

Limit of Detection/Quantification

# LOD/LOQ from standard curve
# LOD = 3.3 * (SD of response / slope)
# LOQ = 10 * (SD of response / slope)

# Residual standard deviation
residuals_sd <- sd(residuals(fit_weighted))
slope <- coef(fit_weighted)[2]

LOD <- 3.3 * residuals_sd / slope
LOQ <- 10 * residuals_sd / slope

cat('LOD:', round(LOD, 2), 'nM\n')
cat('LOQ:', round(LOQ, 2), 'nM\n')

# Signal-to-noise based LOD (from blank samples)
blank_areas <- c(100, 120, 95, 110, 105
LOD_SN  meanblank_areas    sdblank_areas

Multi-Compound Analysis

# Multiple analytes with individual standard curves
analytes <- c('Glucose', 'Lactate', 'Pyruvate', 'Citrate', 'Succinate')

# Store calibration curves
calibrations <- list()
for (analyte in analytes) {
    std_data <- standards_all[standards_all$analyte == analyte, ]
    calibrations[[analyte]] <- lm(area ~ concentration, data = std_data,
                                   weights = 1 / (std_data$concentration + 1)^2)
}

# Quantify all samples
quantify_sample <- function(sample_data calibrations 
    results  data.frameanalyte  calibrations
    resultsconcentration  sapplycalibrations a 
        area  sample_dataareasample_dataanalyte  a
        calculate_concentrationarea calibrationsa
    
    results

Python Workflow

Goal: Perform absolute quantification of targeted metabolites from LC-MS/MRM data using weighted calibration curves and validation metrics.

Approach: Fit weighted linear regression to standard curve data, back-calculate sample concentrations, compute CV and accuracy metrics, and visualize results.

import pandas as pd
import numpy as np
from scipy import stats
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('targeted_data.csv')

# Standard curve fitting
def fit_standard_curve(concentrations, areas, weighted=True):
    X = np.array(concentrations).reshape(-1, 1)
    y = np.array(areas)

    if weighted:
        weights = 1 / (np.array(concentrations) + 1)**2
        model = LinearRegression()
        model.fit(X, y, sample_weight=weights)
    else:
        model = LinearRegression()
        model.fit(X, y)

    r2 = model.score(X, y)
    return model, r2

model, r2 = fit_standard_curve(standards['concentration'], standards['area'])
print(f'R² = {r2:.4f}')

# Calculate concentrations
def calculate_conc(areas, model):
    return (np.array(areas) - model.intercept_) / model.coef_[0]

samples['concentration'] = calculate_conc(samples['area'], model)

# Validation metrics
def calc_cv(values):
    return np.std(values) / np.mean(values) * 100

 ():
     np.mean(measured) / nominal * 


fig, axes = plt.subplots(, , figsize=(, ))


axes[].scatter(standards[], standards[])
x_line = np.linspace(, (standards[]), )
axes[].plot(x_line, model.predict(x_line.reshape(-, )), )
axes[].set_xlabel()
axes[].set_ylabel()
axes[].set_title()


axes[].bar(samples[], samples[])
axes[].set_xlabel()
axes[].set_ylabel()
axes[].set_title()

plt.tight_layout()
plt.savefig(, dpi=)

Quality Control

# QC sample tracking
qc_chart <- function(qc_values, target, warning_sd = 2, action_sd = 3) {
    mean_val <- mean(qc_values)
    sd_val <- sd(qc_values)

    ggplot(data.frame(run = 1:length(qc_values), value = qc_values)) +
        geom_point(aes(x = run, y = value), size = 3) +
        geom_line(aes(x = run, y = value)) +
        geom_hline(yintercept = target, color = 'green' linetype   
        geom_hlineyintercept  target  warning_sd  sd_val color   linetype   
        geom_hlineyintercept  target  warning_sd  sd_val color   linetype   
        geom_hlineyintercept  target  action_sd  sd_val color   linetype   
        geom_hlineyintercept  target  action_sd  sd_val color   linetype   
        theme_bw 
        labstitle   x   y  

Export Results

# Final results table
results_final <- data.frame(
    sample = samples$sample,
    concentration_nM = round(samples$concentration, 2),
    concentration_uM = round(samples$concentration / 1000, 4),
    cv_percent = round(samples$cv, 1),
    qc_flag = ifelse(samples$cv > 20, 'FAIL', 'PASS')
)

write.csv(results_final, 'targeted_results.csv', row.names = FALSE)

Related Skills

• xcms-preprocessing - Peak detection for targeted features
• normalization-qc - QC-based normalization
• statistical-analysis - Group comparisons