MassSpecWavelet

MassSpecWavelet applies a continuous wavelet transform (CWT) to mass spectrometry (MS) spectra to detect peaks based on peak shape and to improve discrimination of true signals from noise for proteomics and metabolomics analyses.

Key Features:

• Continuous Wavelet Transform (CWT) Utilization: Transforms MS spectra into wavelet space to separate signal from spike and colored noise and to enhance effective signal-to-noise ratio without requiring baseline removal or peak smoothing.
• Shape-Matching Functionality: Evaluates peaks using a shape-matching function that computes a goodness-of-fit coefficient, enabling identification independent of peak amplitude.
• Multiscale Robust Peak Detection: Identifies peaks across different scales and amplitudes with a low false positive rate, enabling detection of both strong and weak peaks.
• Implementation: Implemented in R.

Scientific Applications:

• Proteomics: Reliable detection of peptide and protein peaks in MS spectra for peptide/protein profiling and identification.
• Metabolomics: Detection of metabolite peaks in complex MS datasets where noise and low-amplitude signals are present.
• SELDI-TOF analysis: Handling of SELDI-TOF spectra and datasets with known polypeptide positions to improve peak identification.
• Cross-run and cross-instrument consistency: Provides consistent peak detection results across different runs, instruments, and analysis methods.

Methodology:

Apply a continuous wavelet transform to MS spectra to map data into wavelet space, evaluate peaks across scales using a shape-matching function that yields a goodness-of-fit coefficient, and distinguish true signals from spike and colored noise.