exploratory analysis of different types of two dimensional metabolomics data

Metabolomics has been successfully applied in many fields including clinical research, drug discovery, toxicology, and phytochemistry. there are three main questions which should be answered from analysis of metabolomics data. 1) which biomarkers are responsible in appearing a specific phenotype? 2) are different factors and their interactions important in appearing a specific phenotype in biological systems? and finally 3) how the concentration of specified metabolites are changed when a biological system is exposed to a stress? in fact extracting relevant biological information from large data sets is a major challenge in this field and comprehensive analysis of targeted and untargeted metabolomics data is still a major challenge. gas chromatography-mass spectrometry (gc-ms)-based metabolomics is ideal for identifying and quantitating small molecular metabolites (<650 daltons). this unit shows that on gc-ms- based metabolomics easily allows integrating targeted assays for absolute quantification of specific metabolites with untargeted metabolomics to discover novel compounds. the raw gc-ms data showing the three dimensional nature of the data (intensity as a function of elution time and mass). these data can be used in three formats: vectorized data, total ion chromatogram (tic) data and peak area data. peak area data can be obtained with mcr-als method. in this study exploratory analysis was applied on different types of a 2 factor simulated gc-ms data. the data were simulated in different conditions including the presence of selectivity in spectral mode for all components, one components and when there is not selectivity for any components in data. it was shown that different results were obtained when different forms of data is applied. pca was applied as the exploratory method in this study.