ArticleMetabolomics : Official journal of the Metabolomic Society2018
Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.
Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers.
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135 citing papers in PubMed.
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- Metabolomic responses are more sensitive in muscle than serum following 28 days of arduous exercise with erythropoietin administration.Experimental physiology · 2026Article
- Plasma proteome profiling identifies novel biomarkers and predictors for schizophrenia.Translational psychiatry · 2026Article
- Information-Content-Informed Kendall-Tau Correlation Methodology: Interpreting Missing Values in Metabolomics as Potentially Useful Information.Metabolites · 2026Article
- Characterizing Human Oxidative, Anabolic and Glycolytic Metabolism in Athletes with Extreme Physiologies.Sports medicine - open · 2026Article
- Induced Sputum Multi-Omics Reveals Airway Signatures of COPD in Smokers: A Pilot Study.International journal of molecular sciences · 2026Article
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- A Serum and Urine Metabolomics Dataset of TCM Syndromes in Cholelithiasis.Scientific data · 2026Article
- Targeted stool metabolomics suggests exploratory catecholamine- and tryptophan-linked metabolic features in autism spectrum disorder.Frontiers in neuroscience · 2026Article
- Cross-omics association of small extracellular vesicle miRNAs and metabolites in seminal plasma of idiopathic male infertility.Frontiers in endocrinology · 2026Article
- Ontogenetic Changes in the Digestive Capacities of the Naozhou Stock of Large Yellow Croaker (Animals : an open access journal from MDPI · 2025Article
75 more citing papers are in PubMed but not listed here.
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16 authors.
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Abstract
backgroundUntargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.
methodsWe investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci.
resultsRun day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.
conclusionMissing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.
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