Alignstein

Alignstein aligns multidimensional liquid chromatography–mass spectrometry (LC-MS) data using a generalized Wasserstein distance (optimal transport) to correct retention time drift and enable accurate comparison of chromatographic features across runs.

Key Features:

• Wasserstein Distance-Based Alignment: Alignstein employs a generalization of the Wasserstein distance to align multidimensional LC-MS chromatograms without reducing dimensionality.
• Handling Swapped Signals: The method detects and corrects swapped elution orders of analytes caused by retention time drift across replicates.
• No Need for Reference Samples or Prior Identification: The algorithm operates independently of reference samples or prior signal identification.
• Validation and Benchmarking: Alignstein has been validated using publicly available benchmark datasets and demonstrates competitive alignment performance.
• Tandem Mass Spectrum Utilization: The tool leverages spatial properties of chromatograms to extract information from tandem mass spectra (MS/MS) during alignment.
• Optimal Transport Framework: The approach uses optimal transport theory to overcome limitations of traditional alignment algorithms and support downstream statistical analyses.

Scientific Applications:

• Metabolomics: Alignstein enables precise quantitative LC-MS analysis in metabolomics by preserving multidimensional feature integrity during alignment.
• Proteomics: The method supports comparative proteomic LC-MS studies by correcting retention time drift and swapped elution orders.
• Pharmaceutical Research: Alignstein facilitates accurate LC-MS-based quantitative analyses in pharmaceutical research.

Methodology:

Alignstein applies optimal transport theory via a generalized Wasserstein distance to multidimensional LC-MS chromatograms and leverages spatial chromatogram properties to incorporate information from tandem mass spectra, operating without reference samples or prior identification.