3.1. Data Processing and Analysis GC/TOFMS data files were exported to MATLAB software (Mathworks, Natick, MA), where all data processing procedures and the space-filling design were performed using in-house scripts. The multivariate analysis was carried out in the SIMCA-P+ software (MKS Umetrics

AB, Umeå, Sweden). NIST MS http://www.selleckchem.com/products/BI6727-Volasertib.html Search 2.0 (NIST, Gaithersburg, MD) was used for compound identification based on comparison between resolved spectra and standard spectra from NIST 08 mass spectra library, in-house mass spectra library or the MaxPlanck Institute mass spectra library Inhibitors,research,lifescience,medical (http://csbdb.mpimpgolm.mpg.de/csbdb/gmd/gmd.html). 4.3.2. Hierarchical Multivariate Curve Resolution (H-MCR) Multivariate Curve Resolution (MCR) [16] is a method for simultaneous resolving multiple

GC/MS samples (X) into chromatographic (C) and spectral (S) profiles. MCR calculates a common spectral profile (S) (a mass spectrum) for each resolved profile and for each sample a corresponding chromatographic profile Inhibitors,research,lifescience,medical (C) is obtained and (E) is the residual consisting of instrumental noise and possible also unresolved components, see eqv 1. (1) Due to the size and complexity of metabolic data, MCR decomposition Inhibitors,research,lifescience,medical of data into spectral and chromatographic read me profiles cannot be done for the complete data set simultaneously. To cope with this the data is divided into smaller parts. This is done in by splitting the data in the chromatographic dimension into a set of time window. Prior to this division, the samples are aligned in the chromatographic dimension. Each time window is then resolved separately using MCR. This procedure is called H-MCR [21]. For new independent samples chromatographic profiles (C) can be calculated using the common spectral

profiles Inhibitors,research,lifescience,medical (S) using Equation Inhibitors,research,lifescience,medical (2). In this way, a new set of samples can be resolved predictively, meaning that the same set of profiles are obtained (the same metabolites are resolved)[22]. The collection of all spectral profiles from all time windows can be seen as a reference table of putative metabolites. (2) This predictive feature of MCR also made it possible to integrate an internal validation step in the processing. By dividing the samples to be resolved into two sets and performing independent resolution Brefeldin_A of the two sets, interchanging SsetA, CsetA, SsetB and CsetB are obtained. Samples in set 1 are then predicatively resolved using SsetB to get CsetA_pred and set 2 are then predictively resolved using SsetA to get CsetB_pred. By comparing the similarity between SsetA and SsetB, CsetA and CsetA_pred and CsetB and CsetB_pred, respectively, it is possible to identify the profiles that are stable across samples. Here we use Pearson correlation above 0.95 as the criterion for stability. Only profiles that meet this criterion for all comparisons are used. In this way a reference table consisting of verified and stable spectral profiles is created.