This dynamic selection of samples will enhance the metabolite coverage in each time window. 4.3.3. Multivariate Classification and Prediction Prior to multivariate analysis, the data of all putative metabolites (integrated areas under the metabolites chromatographic profiles) were normalized using the weighted sum of the concentrations of

11 labeled internal standards (listed in supporting text) eluting over the whole chromatographic time range. OPLS-DA [29,30] was used to highlight patterns of metabolites that were systematically Inhibitors,research,lifescience,medical co-varying over multiple samples in relation to the acute effect of strenuous exercise and to investigate the robustness of these patterns. This was done by correlating the Inhibitors,research,lifescience,medical resolved metabolic information against the exercise phase (pre- vs. post- exercise) and predicting independent samples with known phase into existing models. Data were mean-centered and scaled to unit variance prior to modeling, and the number of significant OPLS-DA Inhibitors,research,lifescience,medical components was decided by seven-fold full cross validation [58]. OPLS is

a PLS algorithm [59] with an integrated orthogonal signal correction (OSC) filter [60], which allows the systematic variation correlated to the response, in this case exercise phase, to be modeled Inhibitors,research,lifescience,medical in one PXD101 concentration predictive component and the systematic variation not related to the response in orthogonal components. In this way, the prediction results could

be visualized in the predictive OPLS-DA score vector (t1[p]) and a facilitated interpretation of the metabolic patterns related to exercise phase was obtained in the corresponding OPLS-DA covariance loading vector (w*1[p]). This is crucial Inhibitors,research,lifescience,medical for the understanding of complex biological data and in particular for human data, where the inter-person variability can be extensive, and hence is likely to confound the interpretation if not mafosfamide separated from the information of interest. 4.4. Evaluation of Data Processing and Modeling The strategy of processing large sample sets by selecting representative subsets that capture the metabolic variation in the entire sample set was evaluated by comparing parameters descriptive for the multiple sample comparisons, metabolic information content and sample predictions. The results obtained for the two selected representative sample subsets were compared to the results obtained when processing and modeling all samples concurrently. 4.4.1.