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Han, S. ; Huang, J. ; Foppiano, F. ; Prehn, C. ; Adamski, J.* ; Suhre, K.* ; Li, Y.* ; Matullo, G.* ; Schliess, F.* ; Gieger, C. ; Peters, A. ; Wang-Sattler, R.

TIGER: Technical variation elimination for metabolomics data using ensemble learning architecture.

Brief. Bioinform. 23:bbab535 (2022)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Ensemble Learning ; Longitudinal Analysis ; Machine Learning ; Metabolomics ; Predictive Modelling
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Journal Briefings in Bioinformatics
Quellenangaben Volume: 23, Issue: 2, Pages: , Article Number: bbab535 Supplement: ,
Publisher Oxford University Press
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology II (EPI2)
Institute of Translational Genomics (ITG)
CF Metabolomics & Proteomics (CF-MPC)
Grants Ministry of Education