Masserstein

Masserstein applies optimal transport (Wasserstein distance, Earth Mover’s distance) to perform robust linear deconvolution of experimental mass spectra for estimating proportions of ions in complex mixtures, including cases with overlapping isotopic envelopes and measurement inaccuracies.

Key Features:

• Wasserstein Distance Application: Uses the Wasserstein distance (Earth Mover’s distance) from optimal transport theory to quantify spectral dissimilarity.
• Linear Deconvolution: Obtains ion proportion estimates by minimizing the Wasserstein distance between reference and experimental spectra, reducing sensitivity to measurement inaccuracies and overlapping isotopic envelopes.
• Implementation and Tools: Implemented as a Python 3 package with command-line applications WSDistance (computes Wasserstein distance) and WSDeconv (estimates ion proportions by deconvolution).
• Validation and Performance: Validated on a dataset of 200 mass spectra and on simulated datasets that replicate common measurement inaccuracies.

Scientific Applications:

• Proteomics: Deconvolves proteomics mass spectra to improve quantification of peptide and protein ions.
• Metabolomics: Supports quantification of metabolites in metabolomics mass spectrometry analyses.
• Environmental analysis: Applies to environmental mass spectrometry for identification and quantification of molecular species.
• Biomarker discovery: Improves accuracy of molecular quantification to aid biomarker discovery efforts.
• Metabolic profiling: Facilitates metabolic profiling through more accurate estimation of metabolite proportions.

Methodology:

Computes Wasserstein (Earth Mover’s) distance between spectra and minimizes this distance for linear deconvolution to estimate ion proportions; implemented in Python 3 with command-line tools WSDistance and WSDeconv and validated on 200 measured spectra and simulated datasets.