An automated method for peak detection and matching in large gas chromatography-mass spectrometry data sets
A new approach for peak detection and matching has been developed and applied to two data sets. The first consisted of the Gas Chromatography-Mass Spectrometry (GC-MS) samples of 965 human sweat samples obtained from a population of 197 individuals. The second data set contained 500 synthetic chromatograms, and was generated to validate the peak detection and matching methods. The size of both of the data sets (around 500 000 detectable peaks over all chromatograms in data set 1, and around 100 000 in data set 2) would make it unfeasible to check manually whether peaks are matched. In the method described, the first procedure involves pre-processing the data before carrying out the second procedure of peak detection. The final procedure of peak matching consists of three stages: (a) finding potential target peaks in the full data set over all chromatograms; (b) matching peaks in the chromatograms to these targets to form clusters of spectra associated with each target; (c) merging targets where appropriate. Peak detection and matching were applied to both data sets, and the importance of stage (c) of peak matching described. In addition to the analysis of the synthetic chromatograms, the method was also validated by shuffling the original order of the sweat chromatograrns and performing the methods independently on the newly shuffled data. Copyright (c) 2007 John Wiley & Sons, Ltd.
peak detection gc-ms peak matching metabolomics