Commentary: Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Keski‐Rahkonen, Pekka; Robinson, Oliver; Alfano, Rossella; Plusquin, Michelle; Scalbert, Augustin

doi:10.3389/fpubh.2021.755837

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Recent arguments present such a problem as caused by the different choices in peak picking solutions when studying a metabolomics dataset. 17,18 Given the low reproducibility among peak picking algorithms, it is essential to understand the behind-the-scenes mechanisms so that we can develop a better solution. This issue has also been critical to the reproducibility and robustness of a metabolomics study.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Moreover, the mechanistic differences between peak picking algorithms cause discrepancies in the peak picking outcomes. − Such discrepancies are a detriment to the reproducibility of a metabolomics study and its biological conclusions. Recent arguments present such a problem as caused by the different choices in peak picking solutions when studying a metabolomics dataset. , …”

Section: Introductionmentioning

confidence: 99%

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

Guo

Huan

2023

Anal. Chem.

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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.

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Section: ■ Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Guo

Huan

2023

Anal. Chem.

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“…The commentary by Keski-Rahkonen et al ( 1 ) on our study “ Data processing thresholds for abundance and sparsity and missed biological insights in an untargeted chemical analysis of blood specimens for exposomics ” ( 2 ) needed some additional comparative analyses to clarify the discrepancies noticed for the example peaks that were flagged as missed in the MS-DIAL data processing workflow used in our study. A re-evaluation of the published data matrix available at EBI Metabolights repository accession MTBLS1684 ( https://www.ebi.ac.uk/metabolights/MTBLS1684/ ), from the original study ( 3 , 4 ) indicated that the LC/MS peaks were reported by identifiers created using neutral masses and retention time (RT) pairs while assuming a proton adduct for all peaks.…”

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confidence: 99%

Response: Commentary: Data processing thresholds for abundance and sparsity and missed biological insights in an untargeted chemical analysis of blood specimens for exposomics

Barupal

2022

Front. Public Health

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Commentary: Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Cited by 2 publications

References 8 publications

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

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

Response: Commentary: Data processing thresholds for abundance and sparsity and missed biological insights in an untargeted chemical analysis of blood specimens for exposomics

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