## 3 Experimental Section
### Raw Data Processing and Statistical Data Mining  
The Xconvert program included in Xcalibur was used to convert the Xcalibur (*.raw) files to netCDF (*.cdf) format. Automatic peak finding, deconvolution, and alignment were performed using XCMS running on the open statistical platform R [38]. Preliminary data exploration was accomplished using unsupervised methods such as principle component analysis (PCA) and clustering. For PCA, a scree plot (to show the optimal number of eigenvalues), a scree plot (to show the most important principal components and visually detect clusters), and a loading plot (to show positive and negative correlations of components) were included for each analysis using R package pcaMethods in Bioconductor project [45, 46]. Cluster analysis of the PCA scores was performed using partitioning methods such as K-means using the function kmeans() in R package stats, hierarchical agglomerative methods such as Ward's method using the function hclust() in R package stats, and multiscale bootstrap resampling using R package pvclust, and model-based clustering approach using R package mclust which assumes a variety of data models and applying maximum likelihood estimation and Bayes criteria to identify the most likely model and number of clusters.  
Genetic Algorithms (GA) are a class of algorithms based on the principle of biological evolution, suitable for finding approximate solutions to global optimization problems when there is a very large pool of possible solutions. The GA procedure incorporates operators such as biological inheritance, mutation, selection, and recombination on chromosomes, initial sets of candidate solutions [33]. Starting from a randomly generated set of subgroups and a criteria function for evaluating the fitness of an individual subset, GA procedure repeatedly selects the fittest candidate solutions in each generation and lets them reproduce and keep the population size constant. This process stops when the goal fitness is achieved. Goal fitness is defined as the average reachable fitness in a reasonable amount of generations. Feature selection using GA procedure and further classification were performed using R package GALGO [33]. With the equipment of hardware and software used, 2 days for 1 run using GA with nearest centroid method and the optimized parameter set were required to fulfill the task.  
All calculations were performed in an R integrated development environment (IDE) RKWard under Kubuntu 7.10, a Debian Linux operating system, on a quad core Dell OptiPlex 755 workstation (4 x 3.0 GHz CPU speed, 2 x 4 MB L2 cache, 8 GB RAM). The current versions of Kubuntu, R, Bioconductor, XCMS, pcaMethods, stats, pvclust, mclust, GALGO, and RKWard are free open source software (FOSS).  
The MarkerView 1.1 Software (Applied Biosystems/MDS Sciex, Concord, Ontario, Canada) [39; 40] is designed to allow the data from several samples to be compared so that differences can be identified; typical applications include: metabolomics, biomarker discovery, metabolite identification, impurity profiling, etc. This software was used for data analysis in parallel to described above techniques in current study. The program uses multivariate analysis techniques to compare the samples and provides both supervised and unsupervised methods. Supervised methods use prior knowledge of the sample groups (for example, affected/altered vs. control) to determine the variables that distinguish the groups. In contrast, unsupervised methods allow the structure within the data to be determined and visualized. The two approaches can be combined, i.e. unsupervised methods can be used to determine the groups, and then supervised methods can be used to confirm the important variables.