Data Normalization in NMR-based Metabolomics

Abstract

The aim of this article is to summarize recent bioinformatic and statistical developments applicable to NMR-based metabolomics. Extracting relevant information from large multivariate datasets by statistical data analysis strategies may be of considerable complexity. Typical tasks comprise for example classification of specimens, identification of differentially produced metabolites, and estimation of fold changes. In this context it is of prime importance to minimize contributions from unwanted biases and experimental variance prior to these analyses. This is the goal of data normalization. Therefore, special emphasize is given to different data normalization strategies. In the first part, we will discuss the requirements and the pros and cons for a variety of commonly applied strategies. In the second part, we will concentrate on possible solutions in case that the requirements for the standard strategies are not fulfilled. In the last part, very recent developments will be discussed that allow reliable estimation of metabolic signatures for sample classification without prior data normalization. In this contribution special emphasis will be given to techniques that have worked well in our hands.

Tools and resources for metabolomics research community: A 2017–2018 update

“…A recent study provided proof of concept for quantitative urine NMR metabolomics pipeline for large‐scale epidemiology and genetics study . Tools for NMR data normalization continue to evolve . Another recent treatise underscored the developments in the areas of preprocessing and statistical analysis of NMR metabolomics data, including Bayesian methods, statistical correlation analysis, statistical association networks, and data standards, among others .…”