Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm). Eriksson, L., Antti, H., Gottfries, J., Holmes, E., Johansson, E., Lindgren, F., Long, I., Lundstedt, T., Trygg, J., & Wold, S. Analytical and Bioanalytical Chemistry, 380(3):419–429, October, 2004. ![Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm) [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{eriksson_using_2004,
title = {Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)},
volume = {380},
issn = {1618-2650},
url = {https://doi.org/10.1007/s00216-004-2783-y},
doi = {10/ct97hh},
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.},
language = {en},
number = {3},
urldate = {2021-06-30},
journal = {Analytical and Bioanalytical Chemistry},
author = {Eriksson, Lennart and Antti, Henrik and Gottfries, Johan and Holmes, Elaine and Johansson, Erik and Lindgren, Fredrik and Long, Ingrid and Lundstedt, Torbjörn and Trygg, Johan and Wold, Svante},
month = oct,
year = {2004},
pages = {419--429},
}
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