Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm) |
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Authors: | Lennart Eriksson Henrik Antti Johan Gottfries Elaine Holmes Erik Johansson Fredrik Lindgren Ingrid Long Torbjörn Lundstedt Johan Trygg Svante Wold |
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Institution: | (1) Umetrics AB, POB 7960, 907 19 Umeå, Sweden;(2) Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College of Science Technology and Medicine, Sir Alexander Fleming Building, South Kensington, London, SW7 2AZ, UK;(3) AstraZeneca, R&D Mölndal, 431 83 Mölndal, Sweden;(4) Institute of Chemistry, Umeå University, 901 87 Umeå, Sweden;(5) Umetrics AB, Malmö Office, Stortorget 21, 21134 Malmö, Sweden;(6) Department of Pharmaceutical Chemistry, Uppsala University, Box 574, 741 23 Uppsala, Sweden |
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Abstract: | This article describes the applicability of multivariate projection techniques, such as principal-component analysis (PCA) and partial least-squares (PLS) projections to latent structures, to the large-volume high-density data structures obtained within genomics, proteomics, and metabonomics. PCA and PLS, and their extensions, derive their usefulness from their ability to analyze data with many, noisy, collinear, and even incomplete variables in both X and Y. Three examples are used as illustrations: the first example is a genomics data set and involves modeling of microarray data of cell cycle-regulated genes in the microorganism Saccharomyces cerevisiae. The second example contains NMR-metabonomics data, measured on urine samples of male rats treated with either of the drugs chloroquine or amiodarone. The third and last data set describes sequence-function classification studies in a set of G-protein-coupled receptors using hierarchical PCA. |
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Keywords: | PCA PLS Hierarchical modeling Multivariate analysis Omics data analysis |
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