Data Analytical Methods for the Application of Systems Biology in Nutrition

This chapter reviews the prevailing methods and achievements in bioinformatics and systems biology that have driven research in systems nutrition (SN). Gene set analysis (GSA) has arguably become the most widely adopted method for high-throughput gene expression (i.e. transcriptomic) analysis. The common goal of any GSA algorithm is to identify coordinated changes in sets of functionally cohesive genes. Multivariate analysis incorporates a range of approaches for exploring the structure within and between multivariate datasets. A common underlying theme across many multivariate techniques is dimensionality reduction that is, identifying informative low-dimensional representations of high-dimensional datasets. Data analysis and influence/association network inference are mainly based on the correlation of observed quantities and on the localisation into subsystems (tissue, organ, subcellular compartment) of the relevant events. The chapter mentions some of the most used databases, connecting them with the main methods.