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Johan Trygg

Bio: Johan Trygg is an academic researcher from Umeå University. The author has contributed to research in topics: Partial least squares regression & Chemometrics. The author has an hindex of 50, co-authored 185 publications receiving 15952 citations. Previous affiliations of Johan Trygg include Nanjing Medical University & Wellcome Trust Centre for Human Genetics.


Papers
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Journal ArticleDOI
TL;DR: In this article, a generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described, which removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity).
Abstract: A generic preprocessing method for multivariate data, called orthogonal projections to latent structures (O-PLS), is described. O-PLS removes variation from X (descriptor variables) that is not correlated to Y (property variables, e.g. yield, cost or toxicity). In mathematical terms this is equivalent to removing systematic variation in X that is orthogonal to Y. In an earlier paper, Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175-185) described orthogonal signal correction (OSC). In this paper a method with the same objective but with different means is described. The proposed O-PLS method analyzes the variation explained in each PLS component. The non-correlated systematic variation in X is removed, making interpretation of the resulting PLS model easier and with the additional benefit that the non-correlated variation itself can be analyzed further. As an example, near-infrared (NIR) reflectance spectra of wood chips were analyzed. Applying O-PLS resulted in reduced model complexity with preserved prediction ability, effective removal of non-correlated variation in X and, not least, improved interpretational ability of both correlated and non-correlated variation in the NIR spectra.

2,096 citations

Journal ArticleDOI
TL;DR: An overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies is provided, including the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS, and dynamic extensions thereof.
Abstract: We provide an overview of how the underlying philosophy of chemometrics is integrated throughout metabonomic studies. Four steps are demonstrated: (1) definition of the aim, (2) selection of objects, (3) sample preparation and characterization, and (4) evaluation of the collected data. This includes the tools applied for linear modeling, for example, Statistical Experimental Design (SED), Principal Component Analysis (PCA), Partial least-squares (PLS), Orthogonal-PLS (OPLS), and dynamic extensions thereof. This is illustrated by examples from the literature. Keywords: Statistical Experimental Design (SED) • PCA • OPLS • Class-specific studies • Dynamic studies • Multivariate Design

1,194 citations

Journal ArticleDOI
TL;DR: In this paper, class-orthogonal variation can be exploited to augment classificaiton analysis (OPLS-DA) for the purpose of discriminant analysis, and the OPLS method can be used to augment classification.
Abstract: The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classific ...

1,179 citations

Journal ArticleDOI
TL;DR: The S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, e.g., OPLS discriminate analysis, having two or more classes, and an improved visualization and discrimination of interesting metabolites could be demonstrated.
Abstract: Metabolomics studies generate increasingly complex data tables, which are hard to summarize and visualize without appropriate tools The use of chemometrics tools, eg, principal component analysis (PCA), partial least-squares to latent structures (PLS), and orthogonal PLS (OPLS), is therefore of great importance as these include efficient, validated, and robust methods for modeling information-rich chemical and biological data Here the S-plot is proposed as a tool for visualization and interpretation of multivariate classification models, eg, OPLS discriminate analysis, having two or more classes The S-plot visualizes both the covariance and correlation between the metabolites and the modeled class designation Thereby the S-plot helps identifying statistically significant and potentially biochemically significant metabolites, based both on contributions to the model and their reliability An extension of the S-plot, the SUS-plot (shared and unique structure), is applied to compare the outcome of mu

1,080 citations

Journal ArticleDOI
TL;DR: The implementation of the statistical total correlation spectroscopy (STOCSY) analysis method with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data.
Abstract: We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure−discriminant analysis (O-PLS-DA) offers a new powerful framework for ...

823 citations


Cited by
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Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: Most of the papers surveyed did not report using randomisation or blinding to reduce bias in animal selection and outcome assessment, consistent with reviews of many research areas, including clinical studies, published in recent years.
Abstract: animals used (i.e., species/strain, sex, and age/weight). Most of the papers surveyed did not report using randomisation (87%) or blinding (86%) to reduce bias in animal selection and outcome assessment. Only 70% of the publications that used statistical methods fully described them and presented the results with a measure of precision or variability [5]. These findings are a cause for concern and are consistent with reviews of many research areas, including clinical studies, published in recent years [2–22].

6,271 citations

Journal ArticleDOI
01 May 1981
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

4,948 citations

Journal ArticleDOI
19 Dec 2013-Nature
TL;DR: It is shown that a large bowel microbial fermentation product, butyrate, induces the differentiation of colonic Treg cells in mice and ameliorated the development of colitis induced by adoptive transfer of CD4+ CD45RBhi T cells in Rag1−/− mice.
Abstract: Gut commensal microbes shape the mucosal immune system by regulating the differentiation and expansion of several types of T cell. Clostridia, a dominant class of commensal microbe, can induce colonic regulatory T (Treg) cells, which have a central role in the suppression of inflammatory and allergic responses. However, the molecular mechanisms by which commensal microbes induce colonic Treg cells have been unclear. Here we show that a large bowel microbial fermentation product, butyrate, induces the differentiation of colonic Treg cells in mice. A comparative NMR-based metabolome analysis suggests that the luminal concentrations of short-chain fatty acids positively correlates with the number of Treg cells in the colon. Among short-chain fatty acids, butyrate induced the differentiation of Treg cells in vitro and in vivo, and ameliorated the development of colitis induced by adoptive transfer of CD4(+) CD45RB(hi) T cells in Rag1(-/-) mice. Treatment of naive T cells under the Treg-cell-polarizing conditions with butyrate enhanced histone H3 acetylation in the promoter and conserved non-coding sequence regions of the Foxp3 locus, suggesting a possible mechanism for how microbial-derived butyrate regulates the differentiation of Treg cells. Our findings provide new insight into the mechanisms by which host-microbe interactions establish immunological homeostasis in the gut.

3,596 citations

Journal ArticleDOI
TL;DR: This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing.
Abstract: There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics and other functional genomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, facilitates experimental replication, and enables the re-interrogation and comparison of data by others. Accordingly, the Metabolomics Standards Initiative is building a general consensus concerning the minimum reporting standards for metabolomics experiments of which the Chemical Analysis Working Group (CAWG) is a member of this community effort. This article proposes the minimum reporting standards related to the chemical analysis aspects of metabolomics experiments including: sample preparation, experimental analysis, quality control, metabolite identification, and data pre-processing. These minimum standards currently focus mostly upon mass spectrometry and nuclear magnetic resonance spectroscopy due to the popularity of these techniques in metabolomics. However, additional input concerning other techniques is welcomed and can be provided via the CAWG on-line discussion forum at http://msi-workgroups.sourceforge.net/ or http://Msi-workgroups-feedback@lists.sourceforge.net. Further, community input related to this document can also be provided via this electronic forum.

3,301 citations