Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics.

ABSTRACT: Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicate

SUBMITTER: Caesar LK 

PROVIDER: S-EPMC5917943 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature