| 1. |
- Eriksson, Lennart, et al.
(författare)
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Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)
- 2004
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Ingår i: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 380:3, s. 419-29
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Artursson, Tom, et al.
(författare)
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Study of Preprocessing Methods for the Determination of Crystalline Phases in Binary Mixtures of Drug Substances by X-ray Powder Diffraction and Multivariate Calibration
- 2000
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Ingår i: Applied Spectroscopy. - : SAGE Publications. - 0003-7028 .- 1943-3530. ; 54:8, s. 272A-301A
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, various preprocessing methods were tested on data generated by X-ray powder diffraction (XRPD) in order to enhance the partial least-squares (PLS) regression modeling performance. The preprocessing methods examined were 22 different discrete wavelet transforms, Fourier transform, Savitzky-Golay, orthogonal signal correction (OSC), and combinations of wavelet transform and OSC, and Fourier transform and OSC. Root mean square error of prediction (RMSEP) of an independent test set was used to measure the performance of the various preprocessing methods. The best PLS model was obtained with a wavelet transform (Symmlet 8), which at the same time compressed the data set by a factor of 9.5. With the use of wavelet and X-ray powder diffraction, concentrations of less than 10% of one crystal from could be detected in a binary mixture. The linear range was found to be in the range 10-70% of the crystalline form of phenacetin, although semiquantitative work could be carried out down to a level of approximately 2%. Furthermore, the wavelet-pretreated models were able to handle admixtures and deliberately added noise.
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| 3. |
- Eriksson, L., et al.
(författare)
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A chemometrics toolbox based on projections and latent variables
- 2014
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 28:5, s. 332-346
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Tidskriftsartikel (refereegranskat)abstract
- A personal view is given about the gradual development of projection methods-also called bilinear, latent variable, and more-and their use in chemometrics. We start with the principal components analysis (PCA) being the basis for more elaborate methods for more complex problems such as soft independent modeling of class analogy, partial least squares (PLS), hierarchical PCA and PLS, PLS-discriminant analysis, Orthogonal projection to latent structures (OPLS), OPLS-discriminant analysis and more. From its start around 1970, this development was strongly influenced by Bruce Kowalski and his group in Seattle, and his realization that the multidimensional data profiles emerging from spectrometers, chromatographs, and other electronic instruments, contained interesting information that was not recognized by the current one variable at a time approaches to chemical data analysis. This led to the adoption of what in statistics is called the data analytical approach, often called also the data driven approach, soft modeling, and more. This approach combined with PCA and later PLS, turned out to work very well in the analysis of chemical data. This because of the close correspondence between, on the one hand, the matrix decomposition at the heart of PCA and PLS and, on the other hand, the analogy concept on which so much of chemical theory and experimentation are based. This extends to numerical and conceptual stability and good approximation properties of these models. The development is informally summarized and described and illustrated by a few examples and anecdotes.
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| 4. |
- Eriksson, Lennart, et al.
(författare)
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CV-ANOVA for significance testing of PLS and OPLS® models
- 2008
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 594-600
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Tidskriftsartikel (refereegranskat)abstract
- This report describes significance testing for PLS and OPLS® (orthogonal PLS) models. The testing is applicable to single-Y cases and is based on ANOVA of the cross-validated residuals (CV-ANOVA). Two variants of the CV-ANOVA are introduced. The first is based on the cross-validated predictive residuals of the PLS or OPLS model while the second works with the cross-validated predictive score values of the OPLS model. The two CV-ANOVA diagnostics are shown to work well in those cases where PLS and OPLS work well, that is, for data with many and correlated variables, missing data, etc. The utility of the CV-ANOVA diagnostic is demonstrated using three datasets related to (i) the monitoring of an industrial de-inking process; (ii) a pharmaceutical QSAR problem and (iii) a multivariate calibration application from a sugar refinery. Copyright © 2008 John Wiley & Sons, Ltd.
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| 5. |
- Eriksson, Lennart, et al.
(författare)
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Multi- and Megavariate Data Analysis : Part II: Advanced Applications and Method Extensions
- 2006
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Bok (refereegranskat)abstract
- This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely:QSAR (quantitative structure-activity relationships) describes how series of molecular structures can be translated to quantitative data and how these data then are used to model and predict biological activity measurements made on the corresponding molecules. Chapters on how the QSAR concept applies in peptide QSAR, lead finding and optimization, combinatorial chemistry, and chem-and bio-informatics, are included.The multi- and megavariate analysis of “omics” data, has a special chapter, i.e., data from metabonomics, proteomics, genomics and other areas.Then follow six chapters on extensions of the basic projection methods (PCA and PLS):Orthogonal PLS (OPLS) showing how a PLS model can be “rotated” so that all y-related information appears in the first component, which facilitates the model interpretation.Hierarchical modeling, both PC and PLS, allowing variables of different types to be handled in separate blocks, which greatly simplifies the handling of datasets with very many variables.Non-linear PLS describes various approaches to the modeling of non-linear relationships between predictors X and responses Y.The Image Analysis chapter shows how multivariate analysis applies to the analysis of series of digital images.Data Mining and Integration has a discussion of how to get useful information out of large and complicated data sets, and how to manage and organize data in complex investigations.The second volume ends with a chapter on preference and sensory data, followed by an appendix summarizing the multivariate approach, statistical notes, and references.
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| 6. |
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| 7. |
- Eriksson, Lennart, et al.
(författare)
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Multivariate analysis of congruent images (MACI)
- 2005
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Ingår i: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 19:5-7, s. 393-403
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Tidskriftsartikel (refereegranskat)abstract
- The multivariate analysis of congruent images (MACI) is discussed. Here, each image represents one observation and the data set contains a set of congruent images. With congruent images we mean a set of images, properly pre-processed, oriented and aligned, so that each data element (feature, pixel) corresponds to the same element across all images. An example may be a set of frames from a fixed video camera looking at a stable process. The purpose of a MACI is to find and express patterns over a set of images for the purpose of classification or quantitative regression-like relationships. This is in contrast to standard image analysis, which is usually concerned with a single image and the identification of parts of the image, for example tumour tissue versus normal. We also extend MACI to the case with a set of images that initially are not fully congruent, but are made so by the use of wavelet analysis and the distributions of the wavelet coefficients. Thus, the resulting description forms a set of congruent vectors amenable to multivariate data analysis. The MACI approach will be illustrated by four data sets, three easy-to-understand tutorial image data sets and one industrial image data set relating to quality control of steel rolls.
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| 8. |
- Eriksson, Lennart, et al.
(författare)
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Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data
- 2000
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Ingår i: Analytica Chimica Acta. ; 420:2, s. 181-95
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, multivariate calibration of complicated process fluorescence data is presented. Two data sets related to the production of white sugar are investigated. The first data set comprises 106 observations and 571 spectral variables, and the second data set 268 observations and 3997 spectral variables. In both applications, a single response, ash content, is modelled and predicted as a function of the spectral variables. Both data sets contain certain features making multivariate calibration efforts non-trivial. The objective is to show how principal component analysis (PCA) and partial least squares (PLS) regression can be used to overview the data sets and to establish predictively sound regression models. It is shown how a recently developed technique for signal filtering, orthogonal signal correction (OSC), can be applied in multivariate calibration to enhance predictive power. In addition, signal compression is tested on the larger data set using wavelet analysis. It is demonstrated that a compression down to 4% of the original matrix size - in the variable direction - is possible without loss of predictive power. It is concluded that the combination of OSC for pre-processing and wavelet analysis for compression of spectral data is promising for future use.
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| 9. |
- Eriksson, Lennart, et al.
(författare)
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PLS-trees (R), a top-down clustering approach
- 2009
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Ingår i: Journal of Chemometrics. - Chichester : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 23:11, s. 569-580
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Tidskriftsartikel (refereegranskat)abstract
- A hierarchical clustering approach based on a set of PLS models is presented. Called PLS-Trees (R), this approach is analogous to classification and regression trees (CART), but uses the scores of PLS regression models as the basis for splitting the clusters, instead of the individual X-variables. The split of one cluster into two is made along the sorted first X-score (t(1)) of a PLS model of the cluster, but may potentially be made along a direction corresponding to a combination of scores. The position of the split is selected according to the improvement of a weighted combination of (a) the variance of the X-score, (b) the variance of Y and (c) a penalty function discouraging an unbalanced split with very different numbers of observations. Cross-validation is used to terminate the branches of the tree, and to determine the number of components of each cluster PLS model. Some obvious extensions of the approach to OPLS-Trees and trees based on hierarchical PLS or OPLS models with the variables divided in blocks depending on their type, are also mentioned. The possibility to greatly reduce the number of variables in each PLS model on the basis of their PLS w-coefficients is also pointed out. The approach is illustrated by means of three examples. The first two examples are quantitative structure-activity relationship (QSAR) data sets, while the third is based on hyperspectral images of liver tissue for identifying different sources of variability in the liver samples.
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| 10. |
- Eriksson, Lennart, et al.
(författare)
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Separating Y-predictive and Y-orthogonal variation in multi-block spectral data
- 2006
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Ingår i: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 20, s. 352-61
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Tidskriftsartikel (refereegranskat)abstract
- Spectral data (X) may contain (a) variation that is correlated to concentrations or properties (Y) of samples and (b) variation that is unrelated to the same Y. This paper outlines an approach by which both such sources of variation may be resolved. The approach is based on a combination of hierarchical modelling and orthogonal partial least squares (OPLS). OPLS is first used at the base hierarchical level. The output is a labelling of the resulting score vectors as representing Y-predictive or Y-orthogonal variation. OPLS is then also used at the top hierarchical level together with principal components analysis (PCA). With PCA the Y-orthogonal X-variation is analysed and interpreted. With OPLS the Y-predictive X-variation is examined. The applicability of the proposed strategy is illustrated using one multi-block spectral data set.
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