TIGERr
Technical Variation Elimination with Ensemble Learning Architecture
The R implementation of TIGER. TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters. TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared. Reference: Han S. et al. (2022) doi:10.1093/bib/bbab535.
- Version1.0.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- TIGERr citation info
- Last release01/06/2022
Documentation
Team
Siyu Han
Ying Li
Show author detailsRolesAuthorJialing Huang
Show author detailsRolesAuthorFrancesco Foppiano
Show author detailsRolesAuthorCornelia Prehn
Show author detailsRolesAuthorJerzy Adamski
Show author detailsRolesAuthorKarsten Suhre
Show author detailsRolesAuthorGiuseppe Matullo
Show author detailsRolesAuthorFreimut Schliess
Show author detailsRolesAuthorChristian Gieger
Show author detailsRolesAuthorAnnette Peters
Show author detailsRolesAuthorRui Wang-Sattler
Show author detailsRolesAuthor
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- Imports3 packages