ReviewMetabolites2023
Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples.
Review in Metabolites, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 33 citations in OpenAlex.
- Long-term performance monitoring of a secondary electrospray ionisation high-resolution mass spectrometry platform using a vapor generator for routine quality control.Analytical methods : advancing methods and applications · 2026Article
- Review
- Biomarkers in Clinical Medicine Research: A Literature Survey in the PubMed Database and a Critical Evaluation.Journal of clinical medicine · 2026Review
- Real-Time Breath Diagnostics: Linking Molecular Pathways, Measurement Technologies, and Clinical Translation.International journal of molecular sciences · 2026Review
- Decoding cancer across scales with metabolomics.Nature reviews. Cancer · 2026Review
- The effects of lactic acid bacterial fermentation on the nutritional components and functional metabolites ofFood chemistry: X · 2026Article
- LCMS-Net: Deep Learning for Raw High Resolution Mass Spectrometry Data Applied to Forensic Cause-of-Death Screening.Analytical chemistry · 2026Article
- A Metabolomics Approach To Identify Metabolites Associated with Uremic Symptoms in Patients Receiving Maintenance Hemodialysis.Kidney360 · 2026Article
- Headspace SPME GC-MS Analysis of Urinary Volatile Organic Compounds (VOCs) for Classification Under Sample-Limited Conditions.Metabolites · 2026Article
- Correction of gas chromatography-mass spectrometry long-term instrumental drift using quality control samples over 155 days.Scientific reports · 2025Article
- Principles of protein abundance regulation across single cells in a mammalian tissue.bioRxiv : the preprint server for biology · 2025Article
- Integrating Advanced Metabolomics and Machine Learning for Anti-Doping in Human Athletes.Metabolites · 2025Review
- Comprehensive molecular characteristics of hepatocellular carcinoma based on multi-omics analysis.BMC cancer · 2025Article
- A Software Tool for Rapid and Automated Preprocessing of Large-Scale Serum Metabolomic Data by Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry.Analytical chemistry · 2025Article
- Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.Analytical and bioanalytical chemistry · 2025Article
- Detection of Sugar Syrups in Honey Using Untargeted Liquid Chromatography-Mass Spectrometry and Chemometrics.Metabolites · 2024Article
- A Metabolomics Approach to Identify Metabolites Associated With Mortality in Patients Receiving Maintenance Hemodialysis.Kidney international reports · 2024Article
- Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning.Scientific reports · 2024Article
- Skin Surface Sebum Analysis by ESI-MS.Biomolecules · 2024Review
- Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies.Metabolites · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.