SIMA: simultaneous multiple alignment of LC/MS peak lists.

Area of Science:

• Analytical Chemistry
• Biotechnology
• Computational Biology

Background:

• Alignment of multiple liquid chromatography/mass spectrometry (LC/MS) experiments is crucial for biological and technical repeats.
• Current LC systems face challenges with missing observations and non-linear retention time distortions.
• Existing pairwise alignment methods are suboptimal for multiple alignment problems.

Purpose of the Study:

• To introduce SIMA, a novel automated procedure for aligning peak lists from multiple LC/MS runs.
• To address limitations of existing methods in handling multiple experimental alignments.
• To provide a robust and efficient solution for LC/MS data alignment.

Main Methods:

• SIMA combines hierarchical pairwise correspondence estimation with simultaneous alignment and global retention time correction.
• It utilizes a tailored multidimensional kernel function and maximum likelihood estimation for retention time distortion.
• The method is robust to outliers and incorporates incomplete correspondence information.

Main Results:

• SIMA demonstrates competitive and superior performance compared to seven alternative methods on four diverse datasets.
• The algorithm effectively corrects non-linear retention time distortions across multiple runs.
• It successfully aligns peak lists even with missing observations and incomplete correspondence.

Conclusions:

• SIMA offers an advanced and automated solution for multiple LC/MS data alignment.
• The method provides accurate and reliable retention time correction, enhancing data comparability.
• SIMA is a valuable tool for researchers working with complex LC/MS datasets.