Closing the Knowledge Gap of Post-Acquisition Sample Normalization in Untargeted Metabolomics.

ABSTRACT: Sample normalization is a crucial step in metabolomics for fair quantitative comparisons. It aims to minimize sample-to-sample variations due to differences in the total metabolite amount. When samples lack a specific metabolic quantity to accurately represent their total metabolite amounts, post-acquisition sample normalization becomes essential. Despite many proposed normalization algorithms, understanding remains limited of their differences, hindering the selection of the most suitable one for a given metabolomics study. This study bridges this knowledge gap by employing data simulation, experimental simulation, and real experiments to elucidate the differences in the mechanism and performance among common post-acquisition sample normalization methods. Using public datasets, we first d

SUBMITTER: Low B 

PROVIDER: S-EPMC11659990 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature