Quantification of run order effect on chromatography - mass spectrometry profiling data.

Area of Science:

• Analytical Chemistry
• Biomolecular Profiling
• Chemometrics

Background:

• Chromatography-mass spectrometry (CMS) is crucial for biological studies, but sensitive to experimental variations.
• The run order effect, a common challenge in CMS profiling, can obscure low-magnitude biological changes.
• Objective quantification of run order effects is needed for reliable data interpretation.

Purpose of the Study:

• To introduce and validate orthogonal projections to latent structures (OPLS) as a method for objective run order effect quantification.
• To demonstrate OPLS's ability to provide a rapid metric for run order effects across all detected features.
• To show how OPLS-derived metrics can inform data normalization and analytical protocol optimization.

Main Methods:

• Application of orthogonal projections to latent structures (OPLS) for multivariate data analysis.
• Quantification of run order effect based on the proportion of variation correlated with sample sequence.
• Demonstration on plasma profiling data from Gas Chromatography-Mass Spectrometry (GC-MS) and Liquid Chromatography-Mass Spectrometry (LC-MS) metabolomics and lipidomics platforms.

Main Results:

• OPLS provides an objective and rapid metric for quantifying the run order effect in CMS profiling studies.
• The OPLS-based quantification correlates experimental data variation with run order across all detected features.
• Results enable evaluation of data normalization strategies and optimization of analytical protocols.

Conclusions:

• OPLS offers a robust and efficient approach for assessing and managing run order effects in CMS-based biomolecular profiling.
• This method supports the reliability of biological interpretations by accounting for instrumental drift and analytical variability.
• OPLS facilitates improved analytical method development and data quality in metabolomics and lipidomics.