Thus, data analysis methodology is critical for generating meaningful scientific results from these information-rich metabolomic data. The typical pipeline used in such analysis has been well detailed and described in the literature, although with some notational and semantic nuances , and can be summarized in the following steps: (i) processing (extracting features from raw instrumental data to a suitable form; normalization, scaling, centering, etc., to put all samples and variables on a comparable scale); (ii) statistical analysis/modeling (this covers understanding and visualization of data and feature selection methods, and validation/estimation of the predictive capability of the applied statistical models); (iii) annotation of the selected features; and (iv) interpretation and metabolic pathway and network analysis, leading to the generation of research hypotheses and knowledge compilation.