Alignstein: Optimal transport for improved LC-MS retention time alignment.

Area of Science:

• Analytical Chemistry
• Computational Biology
• Biotechnology

Background:

• Reproducibility in liquid chromatography-mass spectrometry (LC-MS) is compromised by retention time drift, leading to signal misidentification across experiments.
• Current retention time alignment algorithms struggle with accuracy, particularly when analyte elution orders are swapped.

Purpose of the Study:

• To introduce Alignstein, a novel algorithm for robust retention time alignment in LC-MS data.
• To address limitations in existing alignment methods, especially concerning significant retention time variations and signal swapping.

Main Methods:

• Developed Alignstein, an algorithm employing a generalized Wasserstein distance for multidimensional feature comparison.
• Implemented optimal transport to compare chromatographic features without information or dimensional reduction.
• Designed the algorithm to function without requiring reference samples or prior signal identification.

Main Results:

• Alignstein successfully identifies corresponding signals even when retention times are swapped.
• Validated on benchmark datasets, achieving competitive alignment accuracy.
• Demonstrated the algorithm's ability to leverage spatial chromatogram properties and tandem mass spectrum information.

Conclusions:

• Optimal transport provides an effective solution to overcome limitations in existing mass spectrometry data analysis algorithms.
• Alignstein offers a significant advancement in the statistical analysis of LC-MS datasets, enhancing data reproducibility.
• The source code for Alignstein is publicly available for further research and application.