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

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 i

SUBMITTER: Han S 

PROVIDER: S-EPMC8921617 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature