Review

Analytical and Bioanalytical Chemistry

, Volume 380, Issue 3, pp 419-429

First online:

Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)

  • Lennart ErikssonAffiliated withUmetrics AB
  • , Henrik AnttiAffiliated withBiological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College of Science Technology and MedicineInstitute of Chemistry, Umeå University
  • , Johan GottfriesAffiliated withAstraZeneca, R&D MölndalInstitute of Chemistry, Umeå University
  • , Elaine HolmesAffiliated withBiological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College of Science Technology and Medicine
  • , Erik JohanssonAffiliated withUmetrics AB
  • , Fredrik LindgrenAffiliated withUmetrics AB, Malmö Office
  • , Ingrid LongAffiliated withDepartment of Pharmaceutical Chemistry, Uppsala University
  • , Torbjörn LundstedtAffiliated withDepartment of Pharmaceutical Chemistry, Uppsala University
  • , Johan TryggAffiliated withInstitute of Chemistry, Umeå University
    • , Svante WoldAffiliated withInstitute of Chemistry, Umeå University

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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.

Keywords

PCA PLS Hierarchical modeling Multivariate analysis Omics data analysis