Bioinformatics tools for cancer metabolomics |
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| Authors: | Grigoriy Blekherman Reinhard Laubenbacher Diego F Cortes Pedro Mendes Frank M Torti Steven Akman Suzy V Torti Vladimir Shulaev |
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| Institution: | (1) Virginia Bioinformatics Institute, Washington St. 0477, Blacksburg, VA 24061, USA;(2) Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA;(3) School of Computer Science and Manchester Centre for Integrative Systems Biology, The University of Manchester, 131 Princess St, Manchester, M1 7DN, UK;(4) Department of Cancer Biology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA;(5) Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA;(6) Department of Biological Sciences, College of Arts and Sciences, University of North Texas, 1155 Union Circle #305220, Denton, TX 76203, USA; |
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| Abstract: | It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review
focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different
metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate
data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering
techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion
of available software packages. |
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