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From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data
Saer Samanipour1,2,3, Phil Choi2,4, Jake W O'Brien2
1Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands.
Analytical Chemistry
|November 29, 2021
Summary
Cent2Prof converts centroided mass spectrometry data to peak profiles and back. This package improves data analysis by reducing false positives and enhancing profile prediction accuracy.
Area of Science:
- Analytical Chemistry
- Computational Chemistry
- Bioinformatics
Background:
- Centroiding reduces mass spectrometry data size but loses information density.
- The centroiding process and its impact on results are not always clear.
- High-resolution mass spectrometry generates large datasets requiring efficient processing.
Purpose of the Study:
- Introduce Cent2Prof, a package for converting between centroided and profile mass spectrometry data.
- Develop algorithms for accurate centroiding and mass peak profile reconstruction.
- Evaluate the performance of Cent2Prof against existing methods like MZmine.
Main Methods:
- Developed a centroiding algorithm using resolution-based mass peak width for data fitting.
- Implemented a mass peak profile prediction algorithm utilizing a random-forest model.
- Compared Cent2Prof's centroiding accuracy and profile prediction error with MZmine.
Main Results:
- Cent2Prof achieved a false detection rate of ≤5%, significantly outperforming MZmine's 30% false positive rate.
- Profile prediction error was ≤56%, independent of mass, ionization, and intensity.
- The Cent2Prof profile prediction was 6 times more accurate than resolution-based estimations.
Conclusions:
- Cent2Prof effectively converts between centroided and profile mass spectrometry data.
- The package offers improved accuracy in centroiding and profile prediction for mass spectrometry data analysis.
- Cent2Prof enhances data interpretation by preserving and reconstructing peak profile information.
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