A hierarchical approach to removal of unwanted variation for large-scale metabolomics data.

ABSTRACT: Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV

SUBMITTER: Kim T 

PROVIDER: S-EPMC8371158 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature