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Classification-based comparison of pre-processing methods for interpretation of mass spectrometry generated clinical
Wouter Wegdam1, Perry D Moerland2, Marrije R Buist1
1Department of Gynaecologic Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.
Proteome Science
|May 16, 2009
Summary
Evaluating mass spectrometry data processing is key for disease biomarker discovery. This study shows pre-processing methods yield similar results, but parameter settings critically impact classification accuracy for clinical samples.
Area of Science:
- Biomarker discovery using mass spectrometry
- Proteomics and clinical diagnostics
Background:
- Mass spectrometry (MS) is vital for identifying disease-associated protein profiles.
- Standardized sample collection is crucial, but data processing remains a challenge.
- Pre-processing and classification methods require robust evaluation.
Purpose of the Study:
- To benchmark pre-processing methods using patient sample classification.
- To compare Ciphergen and Cromwell software for SELDI-TOF MS data analysis.
- To assess the impact of parameter settings on classification accuracy.
Main Methods:
- Systematic comparison of two pre-processing methods (Ciphergen, Cromwell) on clinical SELDI-TOF MS datasets.
- Evaluation using five classification methods with a double cross-validation protocol.
- Assessment of peak detection and intensity similarity between methods.
Main Results:
- Ciphergen and Cromwell pre-processing showed comparable reproducibility and peak overlap.
- No single pre-processing method consistently outperformed others across all settings.
- Parameter settings significantly influenced classification accuracy, more than the method itself.
Conclusions:
- Patient sample classification serves as a valid benchmark for evaluating MS data pre-processing.
- Both methods produced similar classification outcomes for ovarian cancer and Gaucher disease datasets.
- Careful selection and evaluation of pre-processing parameter settings are crucial for reliable clinical biomarker discovery.
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