Abstract

In metabolomics studies there is a clear increase of data. This indicates the necessity of both having a battery of suitable analysis methods and validation procedures able to handle large amounts of data. In this review, an overview of the metabolomics data processing pipeline is presented. A selection of recently developed and most cited data processing methods is discussed. In addition, commonly used chemometric and machine learning analysis methods as well as validation approaches are described.

Keywords: Multivariate data analysis, data processing, chemometrics, metabolomics, statistical validation, validation procedures, chemometric and machine learning analysis, NMR, downstream data analysis, Filtration, non-linear regression method