Data-processing strategies for metabolomics studies

Metabolomics studies aim at a better understanding of biochemical processes by studying relations between metabolites and between metabolites and other types of information (e.g., sensory and phenotypic features). The objectives of these studies are diverse, but the types of data generated and the methods for extracting information from the data and analysing the data are similar. Besides instrumental analysis tools, various data-analysis tools are needed to extract this relevant information. The entire data-processing workflow is complex and has many steps. For a comprehensive overview, we cover the entire workflow of metabolomics studies, starting from experimental design and sample-size determination to tools that can aid in biological interpretation. We include illustrative examples and discuss the problems that have to be dealt with in data analysis in metabolomics. We also discuss where the challenges are for developing new methods and tailor-made quantitative strategies.