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- Title
Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm).
- Authors
Eriksson, Lennart; Antti, Henrik; Gottfries, Johan; Holmes, Elaine; Johansson, Erik; Lindgren, Fredrik; Long, Ingrid; Lundstedt, Torbjörn; Trygg, Johan; Wold, Svante
- 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 bothXandY. 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 microorganismSaccharomyces 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.
- Subjects
GENETIC research; GENOMICS; PROTEOMICS; HEREDITY; CELL cycle; MICROORGANISMS
- Publication
Analytical & Bioanalytical Chemistry, 2004, Vol 380, Issue 3, p419
- ISSN
1618-2642
- Publication type
Article
- DOI
10.1007/s00216-004-2783-y