Exploratory analysis of high-throughput metabolomic data

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• 作者：Chalini D. Wijetunge (1)
  Zhaoping Li (1)
  Isaam Saeed (1)
  Jairus Bowne (3) (4)
  Arthur L. Hsu (1) (2)
  Ute Roessner (3) (4)
  Antony Bacic (4) (5)
  Saman K. Halgamuge (1)
  
• 关键词：Metabolomics data analysis ; Growing self ; organising map ; Unsupervised learning
• 刊名：Metabolomics
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：9
• 期：6
• 页码：1311-1320
• 全文大小：
• 作者单位：Chalini D. Wijetunge (1)
  Zhaoping Li (1)
  Isaam Saeed (1)
  Jairus Bowne (3) (4)
  Arthur L. Hsu (1) (2)
  Ute Roessner (3) (4)
  Antony Bacic (4) (5)
  Saman K. Halgamuge (1)
  
  1. Optimisation and Pattern Recognition Group, Department of Mechanical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
  3. Australian Centre for Plant Functional Genomics, School of Botany, The University of Melbourne, Parkville, VIC, 3010, Australia
  4. Metabolomics Australia, School of Botany, The University of Melbourne, Parkville, VIC, 3010, Australia
  2. Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia
  5. ARC Centre of Excellence in Plant Cell Walls, School of Botany, The University of Melbourne, Parkville, VIC, 3010, Australia
  
• ISSN：1573-3890

文摘

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 time-course 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.