Abstract
The global monitoring of metabolites provides a phenotypic characterization of biological systems that is of primary interest to offer mechanistic insights into specific cellular or disease processes. Due to its ability to assess multiple components with high sensitivity, mass spectrometry has become one of the major analytical platforms in metabolomics. Handling appropriately massive data structures generated by modern devices involves a combination of successive procedures needed to extract the relevant information and highlight biologically pertinent compounds. The typical workflow includes raw data preprocessing and pretreatment, data modeling, and statistical validation. Relevant compounds can then be related to prior biological knowledge according to the phenomenon under study. Data analysis plays a central role in the biomarkers discovery process by making sense of data. The different aspects needed for appropriate handling and modeling are discussed thoroughly in this chapter with respect to the intrinsic characteristics of metabolomic data obtained from mass spectrometry experiments.
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