Progressive peak clustering in GC-MS Metabolomic experiments applied to Leishmania parasites (original) (raw)

Abstract

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. 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 GC-MS 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. * To whom correspondence should be addressed. (metabolites) and provide a quantitative measure of the concentration of individual analytes. A common problem in metabolic profiling is the correlation of signal peaks from two or more cell/tissue states (i.e. wild-type vs mutant, healthy vs diseased etc). For example, if two cell/tissue states A and B are examined, with observed signal peaks A 1 , A 2 , ... A N and B 1 , B 2 , ... B M , it is important to establish the correspondence between these signals, i.e. which signals in A and B refer to the same analyte or metabolite. Furthermore, some signals in A may not have the corresponding signal in B due to metabolites produced in one state but not the other. The correspondence between A 1 , A 2 , ... A N and B 1 , B 2 , ... B M leads directly to the data matrix, whose rows represent individual experiments and columns represent unique analytes observed in the two sets of experiments. The generalization to more than two experiments is straightforward. Metabolic profiling experiments are relatively rapid and inexpensive, with typically multiple replicates recorded for each cell/sample state . Multiple replicate experiments facilitate robust statistical analysis, and have also been used to explore the inherent biological variability in metabolomic studies . If replicate experiments are to be explicitly included in the data matrix, the correspondence between signals observed in different replicates must be established. By taking the previous example of two cell states A and B, and assuming that P replicate experiments of A and Q replicate experiments of B were performed, the two sets of experiments may be represented by a series of signals A 11 , A 12 , .

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