Journal ArticleDOI
OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification†
Max Bylesjö,Mattias Rantalainen,Olivier Cloarec,Jeremy K. Nicholson,Elaine Holmes,Johan Trygg +5 more
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TLDR
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 ...read more
Citations
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Journal ArticleDOI
Assessment of PLSDA cross validation
Johan A. Westerhuis,Huub C. J. Hoefsloot,Suzanne Smit,Daniel J. Vis,Age K. Smilde,Ewoud J. J. van Velzen,John P. M. van Duijnhoven,Ferdi A. van Dorsten +7 more
TL;DR: A strategy based on cross model validation and permutation testing to validate the classification models and advocate against the use of PLSDA score plots for inference of class differences is discussed.
Journal ArticleDOI
Chemometrics in Metabonomics
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.
Journal ArticleDOI
Visualization of GC/TOF-MS-based metabolomics data for identification of biochemically interesting compounds using OPLS class models.
Susanne Wiklund,Erik Johansson,Lina Sjöström,Ewa J. Mellerowicz,Ulf Edlund,John P. Shockcor,Johan Gottfries,Thomas Moritz,Johan Trygg +8 more
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.
Journal ArticleDOI
Multivariate Analysis in Metabolomics.
Bradley Worley,Robert Powers +1 more
TL;DR: The use of multivariate analysis for metabolomics is discussed, as well as common pitfalls and misconceptions, and spectral features contributing most to variation or separation are identified for further analysis.
Journal ArticleDOI
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.
Piotr S. Gromski,Howbeer Muhamadali,David I. Ellis,Yun Xu,Elon Correa,Michael L. Turner,Royston Goodacre +6 more
TL;DR: This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA.
References
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Journal ArticleDOI
Principal component analysis
TL;DR: Principal Component Analysis is a multivariate exploratory analysis method useful to separate systematic variation from noise and to define a space of reduced dimensions that preserve noise.
Journal ArticleDOI
The Collinearity Problem in Linear Regression. The Partial Least Squares (PLS) Approach to Generalized Inverses
TL;DR: In this article, the use of Partial Least Squares (PLS) for handling collinearities among the independent variables X in multiple regression is discussed, and successive estimates are obtained using the residuals from previous rank as a new dependent variable y.
Journal ArticleDOI
Orthogonal projections to latent structures (O-PLS)
Johan Trygg,Svante Wold +1 more
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).
Journal ArticleDOI
Linear Model Selection by Cross-validation
TL;DR: In this article, the authors show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-n v -out crossvalidation with n v, the number of observations reserved for validation, satisfying n v /n → 1 as n → ∞.
Book ChapterDOI
The Generalization of Student’s Ratio
TL;DR: In this article, the distribution at which Student arrived was obtained in a more rigorous manner in 1925 by R.A. Fisher, who at the same time showed how to extend the application of the distribution beyond the problem of significance of means, which had been its original object, and applied it to examine regression coefficients and other quantities obtained by least squares, testing not only the deviation of a statistic from a hypothetical value but also the difference between two statistics.