Progressive peak clustering in GC-MS metabolomic experiments applied to Leishmania parasites

Motivation: A common problem in the emerging field of metabolomics is the consolidation of signal lists derived from metabolic profiling of different cell/tissue/fluid states where a number of replicate experiments was collected on each state. Results: We describe an approach for the consolidation of peak lists based on hierarchical clustering, first within each set of replicate experiments and then between the sets of replicate experiments. The problems of finding the dendrogram tree cutoff which gives the optimal number of peak clusters and the effect of different clustering methods were addressed. When applied to gas chromatography-mass spectrometry metabolic profiling data acquired on Leishmania mexicana, this approach resulted in robust data matrices which completely separated the wild-type and two mutant parasite lines based on their metabolic profile.