TIGER: technical variation elimination for metabolomics data using ensemble learning architecture Article Swipe
Siyu Han
,
Jialing Huang
,
Francesco Foppiano
,
Cornelia Prehn
,
Jerzy Adamski
,
Karsten Suhre
,
Ying Li
,
Giuseppe Matullo
,
Freimut Schliess
,
Christian Gieger
,
Annette Peters
,
Rui Wang‐Sattler
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbab535
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbab535
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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- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbab535
- https://academic.oup.com/bib/article-pdf/23/2/bbab535/42805323/bbab535.pdf
- OA Status
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- Cited By
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- References
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- Related Works
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- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4205882990Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bbab535Digital Object Identifier
- Title
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TIGER: technical variation elimination for metabolomics data using ensemble learning architectureWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-11-18Full publication date if available
- Authors
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Siyu Han, Jialing Huang, Francesco Foppiano, Cornelia Prehn, Jerzy Adamski, Karsten Suhre, Ying Li, Giuseppe Matullo, Freimut Schliess, Christian Gieger, Annette Peters, Rui Wang‐SattlerList of authors in order
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https://doi.org/10.1093/bib/bbab535Publisher landing page
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https://academic.oup.com/bib/article-pdf/23/2/bbab535/42805323/bbab535.pdfDirect link to full text PDF
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/23/2/bbab535/42805323/bbab535.pdfDirect OA link when available
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Tiger, Variation (astronomy), Computer science, Architecture, Artificial intelligence, Ensemble learning, Machine learning, Geography, Computer security, Astrophysics, Physics, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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