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Bioinformatics Advance Access published online on March 9, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl085
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received January 4, 2006
Revised February 16, 2006
Accepted March 4, 2006

Article

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

David P. De Souza 1, Eleanor C. Saunders 1, Malcolm J. McConville 1, and Vladimir A. Likic 2 *

1 Department of Biochemistry and Molecular Biology, The Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Royal Parade, Parkville, 3010, Australia
2 The Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, 3010, Australia


  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.

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 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.

Associate Editor: Martin Bishop
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