Abstract
Differences in sample quantities, as well as experimental variations, require the application of an optimal normalization procedure. Variations in the metabolite profiles caused by factors beyond an investigator’s control and interest may also affect the data, and lead to erroneous and incorrect conclusions. Corrections for these factors must be made, in addition to spectral preprocessing and/or metabolite quantification. Subsequently, it is possible to perform different types of analysis, ranging from unsupervised analyses (e.g. to determine sample sub-types or metabolite grouping) to supervised analyses aimed at classification and feature selection. Likewise, quantified metabolic data can be integrated with the results from other experiments, e.g. transcriptomics or proteomics, leading to the development of systems biology models. Data analysis strategy depends on specific study and there is no approach that can fit all data or all questions. This chapter provides an overview of methods used in metabolomics data analysis with their positive and negative sides and many examples.
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