Showing papers by "Johan Trygg published in 2004"

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

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.

Prediction and spectral profile estimation in multivariate calibration

Abstract: Direct and indirect calibration have been compared with respect to both prediction and model interpretation. This included their ability to estimate the pure spectral profile of each known constitu ...

The PLS method -- partial least squares projections to latent structures -- and its applications in industrial RDP (research, development, and production)

Abstract: The PLS method -- partial least squares projections to latent structures -- and its applications in industrial RDP (research, development, and production)

Three-block bi-focal PLS (3BIF-PLS) and its application in QSAR

Abstract: When X and Y are multivariate, the two-block partial least squares (PLS) method is often used. In this paper, we outline an extension addressing a special case of the three-block (X/Y/Z) problem, where Z sits "under" Y. We have called this approach three-block bi-focal PLS (3BIF-PLS). It views the X/Y relationship as the dominant problem, and seeks to use the additional information in Z in order to improve the interpretation of the Y-part of the X/Y association. Two data sets are used to illustrate 3BIF-PLS. Example I relates to single point mutants of haloalkane dehalogenase from Sphingomonas paucimobilis UT26 and their ability to transform halogenated hydrocarbons, some of which are found as organic pollutants in soil. Example II deals with soil remediation capability of bacteria. Whole bacterial communities are monitored over time using "DNA-fingerprinting" technology to see how pollution affects population composition. Since the data sets are large, hierarchical multivariate modelling is invoked to compress data prior to 3BIF-PLS analysis. It is concluded that the 3BIF-PLS approach works well. The paper contains a discussion of pros and cons of the method, and hints at further developmental opportunities.

Global analysis of low-molecular-weight compounds in human plasma using GC/TOF-MS

Abstract: We developed a method for analysis of low-molecular-weight compounds (LMWC) in human plasma involving extraction of metabolites by organic solvents, derivatization of extract and final analysis by ...