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MARS v0.1.0

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@maccoss maccoss released this 13 Jan 08:23
· 20 commits to main since this release
v0.1.0
3ae307b

Mars v0.1.0 Release Notes

Overview

Initial release of Mars (Mass Accuracy Recalibration System), a mass calibration tool for Thermo Stellar unit resolution DIA mass spectrometry data. Mars learns m/z calibration corrections from spectral library fragment matches using an XGBoost model.

Features

Mars uses a machine learning approach to predict m/z corrections based on:

  • Fragment m/z: Mass-dependent calibration bias
  • Peak intensity: Higher intensity peaks provide more reliable calibration
  • Absolute time: Calibration drift over the acquisition run
  • Spectrum TIC: Space charge effects from high ion current
  • Ion injection time: Signal accumulation duration effects
  • Precursor m/z: DIA isolation window-specific effects
  • Adjacent ion population: Ion density in neighboring m/z ranges (0-1, 1-2, 2-3 Th above)
  • Adjacent ion ratios: Relative ion density (adjacent ions / fragment ions)
  • RF temperatures: Thermal effects from RF amplifier (RFA2) and electronics (RFC2)

Fragment Matching

  • Matches library peptides to DIA MS2 spectra using precursor m/z and RT windows
  • Selects the most intense peak within m/z tolerance (not closest)
  • Configurable minimum intensity threshold

PRISM Integration

  • Optional --prism-csv flag for using exact Skyline RT windows (Start Time, End Time)
  • Falls back to ±5 seconds around library RT when PRISM CSV not provided

Batch Processing

  • Process multiple mzML files with glob patterns (--mzml "*.mzML")
  • Process entire directories with --mzml-dir

QC Reports

Generated quality control outputs include:

  • Delta m/z distribution histogram with MAD and RMS statistics (before/after calibration)
  • 2D heatmap visualization (RT × m/z, color = delta)
  • Hexbin density plots (intensity, RT, m/z, injection time, TIC, fragment ions vs mass error)
  • Model feature importance plot
  • Calibration statistics summary

Output Files

File Description
{input}-mars.mzML Recalibrated mzML file
mars_model.pkl Trained XGBoost calibration model
mars_qc_histogram.png Delta m/z distribution (before/after)
mars_qc_heatmap.png 2D heatmap (RT × m/z, color = delta)
mars_qc_intensity_vs_error.png Intensity vs mass error hexbin
mars_qc_rt_vs_error.png RT vs mass error hexbin
mars_qc_mz_vs_error.png Fragment m/z vs mass error hexbin
mars_qc_tic_vs_error.png TIC vs mass error hexbin
mars_qc_injection_time_vs_error.png Injection time vs mass error hexbin
mars_qc_tic_injection_time_vs_error.png TIC×injection time vs mass error hexbin
mars_qc_fragment_ions_vs_error.png Fragment ions vs mass error hexbin
mars_qc_rfa2_temperature_vs_error.png RFA2 temperature vs error (if available)
mars_qc_rfc2_temperature_vs_error.png RFC2 temperature vs error (if available)
mars_qc_feature_importance.png Model feature importance
mars_qc_summary.txt Calibration statistics

Installation

git clone https://github.com/maccoss/mars.git
cd mars
pip install -e .

Or from PyPI:

pip install mars-ms==0.1.0

Requirements

  • Python 3.10+
  • Spectral library in blib format from Skyline
  • mzML files from Thermo Stellar (or similar unit resolution instrument)
  • PRISM CSV (optional): Skyline report with Start Time, End Time, Replicate Name columns

License

MIT

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