Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Abstract

Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky–Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC–MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky–Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.

Is Nontarget Analysis Ready for Regulatory Application? Influence of Peak-Picking Algorithms on Data Analysis

“…This includes settings for preprocessing steps that cannot be skipped (and may differ between algorithms) and which were attempted to be kept at standard or recommended settings when possible. By optimizing settings, the number of false positives and false negatives could probably be drastically reduced, for example, Guo and Huan report up to 90% true positive rates for MSDial after optimizing all settings for a metabolomics data set . However, every software comes with a set of different parameters that need optimization, making this process highly individual depending on the choice of software but should be integrated in any method development.…”

Construction of a label-free electrochemical immunosensor based on a Co9S8/Co3C@C dual-heterojunction for the detection of diethylstilbestrol

“…Currently, with the rapid development of analytical and detection techniques, a great deal of effort has been put into DES detection. Radioimmunoassay, 2 capillary electrophoresis, 3 liquid chromatography-mass spectrometry, 4 gas chromatography-mass spectrometry, 5 enzyme-linked immunoassay, 6 and liquid chromatography-mass spectrometry 7 are the main methods used to detect diethylstilbestrol residues. An RIA method was developed for the detection of diethylstilbestrol (DES) in bovine liver and employs a purification procedure to circumvent these problems.…”