# Single Cell Proteomics Data Processing > Raw Mass Spectrometry Data Processing (pyOpenMS) - Skill: `pharmolix/single-cell-proteomics-data-processing` (Agent Skill) - Install (CLI): `npx skillmds@latest add pharmolix/single-cell-proteomics-data-processing` - Raw SKILL.md: https://api.skillmd.com/api/skills/pharmolix/single-cell-proteomics-data-processing/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: pharmolix (https://skillmd.com/u/pharmolix) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pharmolix/single-cell-proteomics-data-processing --- # Raw Mass Spectrometry Data Processing (pyOpenMS) Load, inspect, centroid, and extract features from raw LC-MS/MS data files. This is **Step 1** of the proteomics pipeline — all downstream peptide identification and quantification steps require centroided, quality-checked spectra as input. --- ## What it does 1. Loads raw or profile-mode spectra from mzML, mzXML, or vendor-converted files using pyOpenMS 2. Inspects run-level QC metrics: total ion current (TIC), scan counts per MS level, m/z and RT ranges 3. Converts profile-mode spectra to centroid mode using PeakPickerHiRes 4. Extracts MS1 and MS2 spectra separately for downstream use 5. Detects LC-MS features (isotope envelopes) using FeatureFinder for label-free quantification 6. Extracts extracted ion chromatograms (EIC) for targeted m/z values 7. Converts between mzML, mzXML, and featureXML formats 8. Generates per-run QC plots (TIC, scan distribution, peak width) --- ## Why this exists If you ask a general AI to "process my mzML files for proteomics," it will: - Not distinguish between profile-mode and centroid-mode spectra (critical difference for downstream tools) - Use incorrect pyOpenMS API calls (the API changed significantly between versions 2.x and 3.x) - Skip quality control checks that reveal injection failures, column issues, or contamination - Not explain the difference between peak picking and feature detection, or when each is needed - Produce centroided output without verifying peak width or mass accuracy This skill encodes the correct methodological decisions: - Checks MS level distribution before processing to confirm DDA vs. DIA acquisition mode - Applies PeakPickerHiRes (correct algorithm) not PeakPickerIterative (for Orbitrap data) - Separates MS1 feature detection (for LFQ) from MS2 centroiding (for database search) - Generates TIC plots to visually confirm run quality before investing compute time in search - Uses pyOpenMS 3.x API (`MSExperiment`, `MzMLFile`) which differs from 2.x --- ## Reference Methods **pyOpenMS** is the Python interface to OpenMS (Röst et al., 2016), a C++ framework for computational mass spectrometry. It provides direct access to the full OpenMS algorithm library including peak picking, feature detection, alignment, and quantification. **PeakPickerHiRes:** Gaussian-based centroiding algorithm optimized for high-resolution instruments (Orbitrap, Q-TOF). Fits a Gaussian to each isotope peak and reports the apex m/z and intensity. Requires profile-mode input. **FeatureFinderCentroided:** Identifies LC-MS features by grouping isotope envelopes across consecutive MS1 scans. Outputs feature maps with m/z, RT, intensity, and charge state per feature. Used as input to label-free quantification. **File formats:** - `mzML`: Open, XML-based vendor-neutral format. Use for all pipeline steps. - `mzXML`: Older open format; convert to mzML with msconvert if needed. - `featureXML`: OpenMS-native format for feature maps. - `pepXML` / `mzIdentML`: Downstream peptide identification results formats. --- ## Usage (Python — pyOpenMS) ```python import pyopenms as oms import numpy as np import pandas as pd import matplotlib.pyplot as plt # ── Step 1: Load mzML file ──────────────────────────────────────────────────── exp = oms.MSExperiment() oms.MzMLFile().load("sample.mzML", exp) print(f"Spectra loaded: {exp.getNrSpectra()}") print(f"Chromatograms: {exp.getNrChromatograms()}") # ── Step 2: QC — inspect scan distribution ──────────────────────────────────── ms1_scans, ms2_scans = [], [] for spec in exp: level = spec.getMSLevel() if level == 1: ms1_scans.append(spec.getRT()) elif level == 2: ms2_scans.append(spec.getRT()) print(f"MS1 scans: {len(ms1_scans)}") print(f"MS2 scans: {len(ms2_scans)}") print(f"MS2/MS1 ratio: {len(ms2_scans)/len(ms1_scans):.1f} " f"(expect 5–20 for typical DDA)") # ── Step 3: Plot Total Ion Chromatogram (TIC) ───────────────────────────────── tic_rt, tic_int = [], [] for spec in exp: if spec.getMSLevel() == 1: tic_rt.append(spec.getRT() / 60) # convert to minutes tic_int.append(spec.calculateTIC()) fig, ax = plt.subplots(figsize=(10, 3)) ax.plot(tic_rt, tic_int, lw=0.8, color="#2166AC") ax.set_xlabel("Retention time (min)") ax.set_ylabel("Total ion current") ax.set_title("TIC — sample.mzML") plt.tight_layout() plt.savefig("figures/tic_sample.pdf") plt.close() # ── Step 4: Check acquisition mode (profile vs centroid) ───────────────────── first_ms1 = next(s for s in exp if s.getMSLevel() == 1) is_centroid = first_ms1.getType() == oms.SpectrumSettings.CENTROID print(f"Acquisition mode: {'centroid' if is_centroid else 'profile'}") # ── Step 5: Centroiding (only if profile-mode) ──────────────────────────────── if not is_centroid: picker = oms.PeakPickerHiRes() params = picker.getParameters() params.setValue("signal_to_noise", 1.0) # lower = more peaks; 0.0 = all peaks params.setValue("ms_levels", [1, 2]) picker.setParameters(params) centroided_exp = oms.MSExperiment() picker.pickExperiment(exp, centroided_exp, check_spectrum_type=False) print(f"Centroided spectra: {centroided_exp.getNrSpectra()}") oms.MzMLFile().store("sample_centroided.mzML", centroided_exp) else: centroided_exp = exp print("Already centroided — no peak picking needed") # ── Step 6: Extract a single MS2 spectrum ──────────────────────────────────── ms2_spectra = [s for s in centroided_exp if s.getMSLevel() == 2] spec = ms2_spectra[0] mz_array, int_array = spec.get_peaks() precursor = spec.getPrecursors()[0] print(f"\nExample MS2 spectrum:") print(f" Precursor m/z: {precursor.getMZ():.4f}") print(f" Precursor charge: {precursor.getCharge()}") print(f" Fragment peaks: {len(mz_array)}") print(f" RT: {spec.getRT()/60:.2f} min") # ── Step 7: Feature detection (MS1, for LFQ) ────────────────────────────────── ff = oms.FeatureFinder() ff_name = "centroided" features = oms.FeatureMap() seeds = oms.FeatureMap() ff_params = oms.FeatureFinder().getParameters(ff_name) # Extract MS1-only experiment for feature finding ms1_exp = oms.MSExperiment() for spec in centroided_exp: if spec.getMSLevel() == 1: ms1_exp.addSpectrum(spec) ff.run(ff_name, ms1_exp, features, ff_params, seeds) features.setUniqueIds() print(f"\nFeatures detected: {features.size()}") oms.FeatureXMLFile().store("sample_features.featureXML", features) # ── Step 8: Export features as DataFrame ───────────────────────────────────── rows = [] for feat in features: rows.append({ "feature_id": feat.getUniqueId(), "mz": feat.getMZ(), "rt_min": feat.getRT() / 60, "intensity": feat.getIntensity(), "charge": feat.getCharge(), "rt_start": feat.getConvexHull().getBoundingBox().minX() / 60, "rt_end": feat.getConvexHull().getBoundingBox().maxX() / 60, }) features_df = pd.DataFrame(rows) features_df.to_csv("sample_features.csv", index=False) print(features_df.head()) # ── Step 9: Extracted ion chromatogram (EIC) for a target m/z ──────────────── target_mz = 524.2736 # example: target peptide precursor m/z tolerance = 0.02 # ± 20 mDa (Orbitrap); use 0.05 for Q-TOF eic_rt, eic_int = [], [] for spec in centroided_exp: if spec.getMSLevel() == 1: mzs, ints = spec.get_peaks() mask = np.abs(mzs - target_mz) <= tolerance eic_rt.append(spec.getRT() / 60) eic_int.append(float(ints[mask].sum()) if mask.any() else 0.0) fig, ax = plt.subplots(figsize=(8, 3)) ax.plot(eic_rt, eic_int, lw=1.2, color="#E64B35") ax.set_xlabel("Retention time (min)") ax.set_ylabel("Intensity") ax.set_title(f"EIC — m/z {target_mz:.4f} ± {tolerance*1000:.0f} mDa") plt.tight_layout() plt.savefig(f"figures/eic_{target_mz:.2f}.pdf") plt.close() ``` ## Batch processing multiple runs ```python from pathlib import Path mzml_files = list(Path("raw/").glob("*.mzML")) for mzml_path in mzml_files: exp = oms.MSExperiment() oms.MzMLFile().load(str(mzml_path), exp) # QC summary ms1 = sum(1 for s in exp if s.getMSLevel() == 1) ms2 = sum(1 for s in exp if s.getMSLevel() == 2) print(f"{mzml_path.name}: {ms1} MS1, {ms2} MS2") # Centroid if needed first_ms1 = next(s for s in exp if s.getMSLevel() == 1) if first_ms1.getType() != oms.SpectrumSettings.CENTROID: picker = oms.PeakPickerHiRes() out_exp = oms.MSExperiment() picker.pickExperiment(exp, out_exp, check_spectrum_type=False) out_path = Path("centroided") / mzml_path.name oms.MzMLFile().store(str(out_path), out_exp) print(f" → centroided: {out_path}") ``` --- ## Example Output ``` Raw MS Data Processing ======================= File: HeLa_digest_rep1.mzML Spectra loaded: 18,432 Chromatograms: 0 Scan distribution: MS1 scans: 1,842 MS2 scans: 16,590 MS2/MS1 ratio: 9.0 (expect 5–20 for typical DDA) Acquisition mode: profile Centroiding with PeakPickerHiRes... Centroided spectra: 18,432 ✓ Feature detection (MS1): Features detected: 14,271 Charge states: z=1 (12%), z=2 (38%), z=3 (31%), z=4+ (19%) RT range: 5.2 – 118.4 min m/z range: 300.1 – 1,650.4 Example MS2 spectrum (RT = 42.3 min): Precursor m/z: 524.2736 Precursor charge: 2+ Fragment peaks: 187 Exported: HeLa_digest_rep1_centroided.mzML HeLa_digest_rep1_features.featureXML HeLa_digest_rep1_features.csv (14,271 features) figures/tic_HeLa_digest_rep1.pdf ``` --- ## Interpretation Guide - **MS2/MS1 ratio**: A ratio of 5–20 is typical for DDA experiments. Ratio < 3 suggests the instrument was triggering few MS2 events (check intensity threshold settings); ratio > 30 is unusual - **Profile vs centroid**: Profile data shows Gaussian peak shapes per m/z; centroid data shows single m/z values per peak. Database search tools (MSFragger, Comet) require centroid input - **TIC shape**: A broad, smooth Gaussian-like TIC indicates a good LC gradient. Flat TIC at the start/end is normal (column equilibration). Sudden drops mid-gradient indicate injection failure or column clogging - **Feature count**: 10,000–30,000 features per run is typical for a HeLa cell digest on Orbitrap. Fewer than 5,000 suggests poor digest efficiency, short gradient, or injection problems - **Charge state distribution**: z=2 and z=3 should dominate (> 60% combined) for a typical tryptic digest. High z=1 fraction suggests incomplete digestion or non-tryptic peptides - **EIC tolerance**: Use ± 5–10 ppm for Orbitrap data (e.g., ± 0.003 Da at m/z 600); use ± 50 ppm or ± 0.05 Da for Q-TOF data --- ## Citation If you use this skill in a publication, please cite: - Röst, H.L. et al. (2016). OpenMS: a flexible open-source software platform for mass spectrometry data analysis. *Nature Methods*, 13(9), 741–748. - Sturm, M. et al. (2008). OpenMS — an open-source software framework for mass spectrometry. *BMC Bioinformatics*, 9, 163.