Technical variation elimination for metabolomics data using ensemble learning architecture
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 is developed in this study and released as an R package. 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. Overall, TIGER is expected to be a powerful tool for metabolomics data analysis.
Thank you for checking our TIGER @BigCatZoo!
Any questions regarding TIGER please drop an email to the zookeeper Siyu Han (siyu.han@tum.de) or post it to issues.
GitHub pre-release version
# Enter the following command in R:
if (!library("devtools", logical.return = T)) install.packages("devtools")
devtools::install_github("HAN-Siyu/TIGER")
Stable version
# CRAN: https://CRAN.R-project.org/package=TIGERr
# Enter the following command in R:
install.packages("TIGERr")
All dependencies will be installed when installing TIGER in R.
https://han-siyu.github.io/TIGER_web/
To cite TIGER in publications, please use:
TIGER: technical variation elimination for metabolomics data using ensemble learning architecture. Briefings in bioinformatics 23.2 (2022): bbab535. (doi: https://doi.org/10.1093/bib/bbab535)
- TIGER (this work): technical variation elimination for metabolomics data using ensemble learning architecture
- LION: an integrated R package for effective prediction of lncRNA/ncRNA–protein interaction
- LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer