Exploratory analysis of high-throughput metabolomic data
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ORIGINAL ARTICLE
Exploratory analysis of high-throughput metabolomic data Chalini D. Wijetunge • Zhaoping Li • Isaam Saeed • Jairus Bowne • Arthur L. Hsu • Ute Roessner • Antony Bacic • Saman K. Halgamuge
Received: 17 March 2013 / Accepted: 8 May 2013 Ó Springer Science+Business Media New York 2013
Abstract In order to make sense of the sheer volume of metabolomic data that can be generated using current technology, robust data analysis tools are essential. We propose the use of the growing self-organizing map (GSOM) algorithm and by doing so demonstrate that a deeper analysis of metabolomics data is possible in comparison to the widely used batch-learning self-organizing map, hierarchical cluster analysis and partitioning around medoids algorithms on simulated and real-world timecourse metabolomic datasets. We then applied GSOM to a recently published dataset representing metabolome response patterns of three wheat cultivars subject to a field simulated cyclic drought stress. This novel and information rich analysis provided by the proposed GSOM framework can be easily extended to other high-throughput metabolomics studies.
Electronic supplementary material The online version of this article (doi:10.1007/s11306-013-0545-6) contains supplementary material, which is available to authorized users.
Keywords Metabolomics data analysis Growing selforganising map Unsupervised learning
1 Introduction High-throughput metabolomics aims to comprehensively identify and quantify small-molecules (metabolites) in a given biological sample (Wishart 2008). The data generated from a metabolomics experiment complements genomic, transcriptomic and proteomic studies, and has profound advantages in bridging the gap between genotype and phenotype (Fiehn 2002). The significant advances in high-throughput technologies have produced an avalanche of data in biology. Unfortunately, traditional statistical methods seem insufficient to handle such data and have many limitations, especially for high dimensional and huge volume datasets. These two obstacles often hinder the analysis and interpretation of metabolomics data. Therefore, in order to extract global information from biological experiments subjected to high-throughput analyses, a
Chalini D. Wijetunge and Zhaoping Li are equal first authors. C. D. Wijetunge Z. Li I. Saeed A. L. Hsu S. K. Halgamuge (&) Optimisation and Pattern Recognition Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC 3010, Australia e-mail: [email protected] J. Bowne U. Roessner Australian Centre for Plant Functional Genomics, School of Botany, The University of Melbourne, Parkville, VIC 3010, Australia
A. L. Hsu Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia A. Bacic ARC Centre of Excellence in Plant Cell Walls, School of Botany, The University of Melbourne, Parkville, VIC 3010, Australia
J. Bowne U. Roessner A. Bacic Metabolomics Australia, School of
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