metabolomics-analysis

Metabolomics Analysis

Overview

Metabolomics is the large-scale study of small molecules (metabolites) within cells, tissues, or biofluids. This skill covers untargeted LC-MS and GC-MS metabolomics workflows including feature extraction, normalization, statistical analysis, metabolite identification, and pathway enrichment using Python-based tools.

When to Use This Skill

• Processing LC-MS or GC-MS raw data for metabolite profiling
• Feature detection and alignment across samples
• Metabolite identification using MS2 matching (GNPS, mzCloud, HMDB)
• Statistical analysis: PCA, PLS-DA, ANOVA, fold-change
• Differential metabolite analysis between biological conditions
• Metabolic pathway mapping (KEGG, BioCyc, MetaCyc)
• Normalization methods for metabolomics data
• Multi-omics integration with proteomics and transcriptomics

Quick Start

Loading mzML Data with pyMZML

from pyteomics import mzml
import numpy as np
import pandas as pd

# Load mzML file
spectra = []
with mzml.MzML("sample.mzML") as reader:
    for spectrum in reader:
        if spectrum["ms level"] == 1:
            spectra.append({
                "scan": spectrum["index"],
                "rt": spectrum["scanList"]["scan"][0]["scan start time"],
                "mz": spectrum["m/z array"],
                "intensity": spectrum["intensity array"],
            })

print(f"MS1 spectra loaded: {len(spectra)}")
print(f"RT range: {spectra[0]['rt']:.2f} - {spectra[-1]['rt']:.2f} min")

Feature Detection and Peak Picking

import numpy as np
import pandas as pd
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d

def extract_ion_chromatogram(spectra: list, mz_target: float, ppm: float = 10.0) -> pd.DataFrame:
    """Extract Extracted Ion Chromatogram (EIC) for a target m/z."""
    mz_tol = mz_target * ppm / 1e6
    eic = []
    for s in spectra:
        mask = np.abs(s["mz"] - mz_target) <= mz_tol
        intensity = s["intensity"][mask].sum() if mask.any() else 0.0
        eic.append({"rt": s["rt"], "intensity": intensity})
    return pd.DataFrame(eic)

def detect_peaks(eic: pd.DataFrame, min_intensity: float = 1000.0) -> list:
    """Detect chromatographic peaks in EIC."""
    smoothed = gaussian_filter1d(eic["intensity"].values, sigma=2)
    peaks, props = find_peaks(smoothed, height=min_intensity, prominence=500, width=3)
    return [
        {
            "rt": eic["rt"].iloc[p],
            "peak_intensity": smoothed[p],
            "width": props["widths"][i],
        }
        for i, p in enumerate(peaks)
    ]

# Example: extract glucose (M+H = 181.0708 Da)
eic = extract_ion_chromatogram(spectra, mz_target=181.0708, ppm=5.0)
peaks = detect_peaks(eic, min_intensity=5000.0)
print(f"Peaks found for m/z 181.0708: {len(peaks)}")
for p in peaks:
    print(f"  RT={p['rt']:.2f} min, Intensity={p['peak_intensity']:.0f}")