Abstract
A novel peak alignment algorithm using a distance and spectrum correlation optimization (DISCO) method has been developed for two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS)-based metabolomics. This algorithm uses the output of the instrument control software, ChromaTOF, as its input data. It detects and merges multiple peak entries of the same metabolite into one peak entry in each input peak list. After a z-score transformation of metabolite retention times, DISCO selects landmark peaks from all samples based on both two-dimensional retention times and mass spectrum similarity of fragment ions measured by Pearson's correlation coefficient. A local linear fitting method is employed in the original two-dimensional retention time space to correct retention time shifts. A progressive retention time map searching method is used to align metabolite peaks in all samples together based on optimization of the Euclidean distance and mass spectrum similarity. The effectiveness of the DISCO algorithm is demonstrated using data sets acquired under different experiment conditions and a spiked-in experiment.