ArticleBriefings in bioinformatics2022
TIGER: technical variation elimination for metabolomics data using ensemble learning architecture.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive metabolites for incident myocardial infarction: a two-step meta-analysis of individual patient data from six cohorts comprising 7897 individuals from the COnsortium of METabolomics Studies.Cardiovascular research · 2023Pooled it
- Robust Metabolomics Data Normalization across Scales and Experimental Designs.Analytical chemistry · 2026Article
- Extensive variation between chromosomes of North American and European hop.Nature communications · 2026Article
- Achieving Effective Batch-to-Batch Error Correction through Suppression Correction and Dual MSTUS Normalization.Analytical chemistry · 2026Article
- Integrative Metabolomics of Targeted and Nontargeted Analyses in T2D Progression.Diabetes care · 2026Article
- Urea cycle modulation by combined SGLT2 inhibitors and metformin.BMC medicine · 2026Article
- Correction of gas chromatography-mass spectrometry long-term instrumental drift using quality control samples over 155 days.Scientific reports · 2025Article
- Genomic insights into the virulence repertoire and hemibiotrophic lifestyle of the grapevine black rot pathogen Phyllosticta ampelicida.G3 (Bethesda, Md.) · 2025Article
- Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data.Nature communications · 2025Review
- High-dimensional mediation analysis to elucidate the role of metabolites in the association between PFAS exposure and reduced SARS-CoV-2 IgG in pregnancy.The Science of the total environment · 2025Article
- LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer.Nature communications · 2025Article
- Double-Weighted Bayesian Model Combination for Metabolomics Data Description and Prediction.Metabolites · 2025Article
- Role of major cardiovascular surgery-induced metabolic reprogramming in acute kidney injury in critical care.Intensive care medicine · 2025Observational
- Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.Cardiovascular diabetology · 2024Article
- Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function.Metabolites · 2024Article
- Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort.Biomarker research · 2024Article
- Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study.BMJ open diabetes research & care · 2024Article
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- Identification of candidate metabolite biomarkers for metabolic syndrome and its five components in population-based human cohorts.Cardiovascular diabetology · 2023Article
- Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples.Metabolites · 2023Review
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12 authors.
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Abstract
Large metabolomics datasets inevitably contain unwanted technical variations which can obscure meaningful biological signals and affect how this information is applied to personalized healthcare. Many methods have been developed to handle unwanted variations. However, the underlying assumptions of many existing methods only hold for a few specific scenarios. Some tools remove technical variations with models trained on quality control (QC) samples which may not generalize well on subject samples. Additionally, almost none of the existing methods supports datasets with multiple types of QC samples, which greatly limits their performance and flexibility. To address these issues, a non-parametric method TIGER (Technical variation elImination with ensemble learninG architEctuRe) is developed in this study and released as an R package (https://CRAN.R-project.org/package=TIGERr). TIGER integrates the random forest algorithm into an adaptable ensemble learning architecture. Evaluation results show that TIGER outperforms four popular methods with respect to robustness and reliability on three human cohort datasets constructed with targeted or untargeted metabolomics data. Additionally, a case study aiming to identify age-associated metabolites is performed to illustrate how TIGER can be used for cross-kit adjustment in a longitudinal analysis with experimental data of three time-points generated by different analytical kits. A dynamic website is developed to help evaluate the performance of TIGER and examine the patterns revealed in our longitudinal analysis (https://han-siyu.github.io/TIGER_web/). Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.
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