NormalizeMets: assessing, selecting and implementing statistical methods for normalizing metabolomics data

Abstract

Introduction: In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented. Objectives: We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers. Methods: We present NormalizeMets—a joint graphical user interface wit..