ArticleAnalytical chemistry2017
Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.
Article in Analytical chemistry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Association of Eicosanoids and Eicosanoid-Related Metabolites With Obesity and Cardiometabolic Traits.Diabetes, obesity & metabolism · 2026Article
- Cardiac remodelling and dysfunction in cancer patients receiving cardiotoxic therapies: proteomic and metabolomic profiling.European heart journal · 2026Article
- Hypoxanthine-early biomarker of outcomes in an ovine model of neonatal hypoxic ischemic encephalopathy.Pediatric research · 2026Article
- mzLearn as a data-driven LC/MS signal detection algorithm that enables pre-trained generative models for untargeted metabolomics.Communications chemistry · 2025Article
- [Alignment method for metabolite chromatographic peaks using anSe pu = Chinese journal of chromatography · 2024Article
- Pulmonary primary oxysterol and bile acid synthesis as a predictor of outcomes in pulmonary arterial hypertension.bioRxiv : the preprint server for biology · 2024Article
- Eicosanoid and eicosanoid-related inflammatory mediators and exercise intolerance in heart failure with preserved ejection fraction.Nature communications · 2023Article
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites.PLoS neglected tropical diseases · 2023Article
- Spectral binning as an approach to post-acquisition processing of high resolution FIE-MS metabolome fingerprinting data.Metabolomics : Official journal of the Metabolomic Society · 2022Article
- Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data.Metabolites · 2022Article
- DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data.Analytical chemistry · 2022Article
- Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests.Analytical and bioanalytical chemistry · 2022Review
- Nontargeted mass spectrometry of dried blood spots for interrogation of the human circulating metabolome.Journal of mass spectrometry : JMS · 2021Article
- DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies.Scientific reports · 2021Article
- Mass Spectrometry-based Metabolomics in Translational Research.Advances in experimental medicine and biology · 2021Review
- A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches.Metabolites · 2020Article
- Deep Neural Networks for Classification of LC-MS Spectral Peaks.Analytical chemistry · 2019Article
- Metabolomics Analytics Workflow for Epidemiological Research: Perspectives from the Consortium of Metabolomics Studies (COMETS).Metabolites · 2019Article
- A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.Metabolites · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessing of raw LC-MS data. These methods can be integrated with existing data analysis workflows by providing initial preprocessing bulk nonlinear retention time correction at the raw data level. Further, this approach provides postprocessing visualization and quantification of peak alignment accuracy, as well as peak-reliability-based parsing of processed data through hierarchical clustering of signal profiles. In a metabolomics data set derived from ∼3000 human plasma samples, we find that application of our alignment tools resulted in substantial improvement in peak alignment accuracy, automated data filtering, and ultimately statistical power for detection of metabolite correlates of clinical measures. These tools will enable metabolomics studies of population scale cohorts.
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