Systematic Evaluation of Chromatographic Peak Quality for Targeted Mass Spectrometry via Variational Autoencoder.

Abstract

Targeted mass spectrometry is a powerful technique for quantifying specific proteins or metabolites in complex biological samples. Accurate peak picking is a critical step as it determines the absolute abundance of each analyte by integrating the area under the picked peaks. Although automated software exists for handling such complex tasks, manual intervention is often required to rectify potential errors like misclassification or mis-picking events, which can significantly affect quantification accuracy. Therefore, it is necessary to develop objective scoring functions to evaluate peak-picking results and to identify problematic cases for further inspection. In this study, we present targeted mass spectrometry quality encoder (TMSQE), a data-driven scoring function that summarizes peak quality in three types: transition level, peak group level, and consistency level across samples. Through unsupervised learning from large data sets containing 1,703,827 peak groups, TMSQE establishes a reliable standard for systematic and objective evaluations of chromatographic peak quality in targeted mass spectrometry. TMSQE shows a high degree of consistency with expert experiences and can efficiently capture problematic cases after the automated software. Furthermore, we demonstrate the generalizability of TMSQE by successfully applying it to various data sets, including both peptide and metabolite data sets. Our proposed scoring approach provides a reliable solution for consistent and accurate peak quality evaluation, facilitating peak quality control for targeted mass spectrometry.