bio-metabolomics-targeted-analysis

name	bio-metabolomics-targeted-analysis
description	Perform targeted metabolomics quantification using MRM/SRM transitions, calibration curves, and internal standards. Use when: user needs absolute quantification of metabolites, has MRM/SRM data, wants to build calibration curves, validate a targeted method, or process Skyline output. Triggers: MRM, SRM, calibration curve, absolute quantification, targeted quantification, internal standard, Skyline, targeted metabolomics, LOD, LOQ, method validation, standard curve, concentration calculation, quantitative metabolomics.
tool_type	mixed
primary_tool	skyline
upstream	{"repo":"https://github.com/GPTomics/bioSkills","license":"MIT","original_author":"GPTomics"}

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 + 1), data = standards)

# Weighted linear regression (1/x^2 weighting)
fit_weighted <- lm(area ~ concentration, data = standards,
                   weights = 1 / (standards$concentration + 1)^2)

# R-squared
summary(fit_linear)$r.squared
summary(fit_weighted)$r.squared

# Plot standard curve
ggplot(standards, aes(x = concentration, y = area)) +
    geom_point(size = 3) +
    geom_smooth(method = 'lm', se = TRUE) +
    scale_x_log10() +
    scale_y_log10() +
    theme_bw() +
    labs(title = 'Standard Curve', x = 'Concentration (nM)', y = 'Peak Area')

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
    }
    return(pmax(conc, 0))  # No negative concentrations
}

# Apply to samples
samples <- data.frame(
    sample = paste0('Sample', 1:10),
    area = c(12000, 45000, 8000, 120000, 35000, 78000, 22000, 95000, 41000, 63000)
)

samples$concentration <- calculate_concentration(samples$area, fit_weighted)

# Account for dilution factor
dilution_factor <- 10
samples$concentration_original <- samples$concentration * 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)
)

# Calculate response ratio
data_with_istd$response_ratio <- data_with_istd$analyte_area / data_with_istd$istd_area

# IS-normalized concentration (using IS-corrected standard curve)
istd_conc <- 100  # nM - known ISTD concentration
data_with_istd$concentration <- calculate_concentration(
    data_with_istd$response_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, 105, 99, 101),
        c(485, 510, 495, 520, 490, 505)
    )
)

# Calculate metrics
validation_metrics <- qc_data %>%
    group_by(level, nominal) %>%
    summarise(
        mean = mean(measured),
        sd = sd(measured),
        cv_percent = sd(measured) / mean(measured) * 100,
        accuracy_percent = mean(measured) / nominal * 100,
        bias_percent = (mean(measured) - nominal) / nominal * 100,
        .groups = 'drop'
    )

print(validation_metrics)

# Acceptance criteria
# CV < 15% (< 20% at LLOQ)
# Accuracy 85-115% (80-120% at LLOQ)

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 <- mean(blank_areas) + 3 * sd(blank_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.frame(analyte = names(calibrations))
    results$concentration <- sapply(names(calibrations), function(a) {
        area <- sample_data$area[sample_data$analyte == a]
        calculate_concentration(area, calibrations[[a]])
    })
    return(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

def calc_accuracy(measured, nominal):
    return np.mean(measured) / nominal * 100

# Plot results
fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# Standard curve
axes[0].scatter(standards['concentration'], standards['area'])
x_line = np.linspace(0, max(standards['concentration']), 100)
axes[0].plot(x_line, model.predict(x_line.reshape(-1, 1)), 'r-')
axes[0].set_xlabel('Concentration')
axes[0].set_ylabel('Area')
axes[0].set_title(f'Standard Curve (R² = {r2:.4f})')

# Sample concentrations
axes[1].bar(samples['sample'], samples['concentration'])
axes[1].set_xlabel('Sample')
axes[1].set_ylabel('Concentration')
axes[1].set_title('Sample Quantification')

plt.tight_layout()
plt.savefig('targeted_results.png', dpi=150)

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 = 'solid') +
        geom_hline(yintercept = target + warning_sd * sd_val, color = 'orange', linetype = 'dashed') +
        geom_hline(yintercept = target - warning_sd * sd_val, color = 'orange', linetype = 'dashed') +
        geom_hline(yintercept = target + action_sd * sd_val, color = 'red', linetype = 'dashed') +
        geom_hline(yintercept = target - action_sd * sd_val, color = 'red', linetype = 'dashed') +
        theme_bw() +
        labs(title = 'QC Levey-Jennings Chart', x = 'Run', y = 'Measured Concentration')
}

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