| 1. |
- Eriksson, Lennart, et al.
(author)
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Editorial
- 2007
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In: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 21:10-11, s. 397-
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Journal article (pop. science, debate, etc.)abstract
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| 2. |
- Eriksson, Lennart, et al.
(author)
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Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)
- 2004
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In: Analytical and Bioanalytical Chemistry. - : Springer Science and Business Media LLC. - 1618-2642 .- 1618-2650. ; 380:3, s. 419-29
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Journal article (peer-reviewed)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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| 3. |
- Lundstedt, Torbjörn, et al.
(author)
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Editorial
- 2006
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In: Journal of Chemometrics. - : John Wiley & Sons, Ltd. - 0886-9383 .- 1099-128X. ; 20:8-10, s. 323-324
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Journal article (pop. science, debate, etc.)
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| 4. |
- Bylesjö, Max, et al.
(author)
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Data integration in plant biology the O2PLS method for combined modeling of transcript and metabolite data
- 2007
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In: The Plant Journal. - 0960-7412 .- 1365-313X. ; 52:6, s. 1181-1191
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Journal article (peer-reviewed)abstract
- The technological advances in the instrumentation employed in life sciences have enabled the collection of a virtually unlimited quantity of data from multiple sources. By gathering data from several analytical platforms, with the aim of parallel monitoring of, e.g. transcriptomic, metabolomic or proteomic events, one hopes to answer and understand biological questions and observations. This 'systems biology' approach typically involves advanced statistics to facilitate the interpretation of the data. In the present study, we demonstrate that the O2PLS multivariate regression method can be used for combining 'omics' types of data. With this methodology, systematic variation that overlaps across analytical platforms can be separated from platform-specific systematic variation. A study of Populus tremula x Populus tremuloides, investigating short-day-induced effects at transcript and metabolite levels, is employed to demonstrate the benefits of the methodology. We show how the models can be validated and interpreted to identify biologically relevant events, and discuss the results in relation to a pairwise univariate correlation approach and principal component analysis.
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| 5. |
- Bylesjö, Max, et al.
(author)
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MASQOT : a method for cDNA microarray spot quality control.
- 2005
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 6, s. 250-
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Journal article (peer-reviewed)abstract
- BackgroundcDNA microarray technology has emerged as a major player in the parallel detection of biomolecules, but still suffers from fundamental technical problems. Identifying and removing unreliable data is crucial to prevent the risk of receiving illusive analysis results. Visual assessment of spot quality is still a common procedure, despite the time-consuming work of manually inspecting spots in the range of hundreds of thousands or more.ResultsA novel methodology for cDNA microarray spot quality control is outlined. Multivariate discriminant analysis was used to assess spot quality based on existing and novel descriptors. The presented methodology displays high reproducibility and was found superior in identifying unreliable data compared to other evaluated methodologies.ConclusionThe proposed methodology for cDNA microarray spot quality control generates non-discrete values of spot quality which can be utilized as weights in subsequent analysis procedures as well as to discard spots of undesired quality using the suggested threshold values. The MASQOT approach provides a consistent assessment of spot quality and can be considered an alternative to the labor-intensive manual quality assessment process.
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| 6. |
- Bylesjö, Max, et al.
(author)
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MASQOT-GUI : spot quality assessment for the two-channel microarray platform
- 2006
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In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 22:20, s. 2554-2555
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Journal article (peer-reviewed)abstract
- MASQOT-GUI provides an open-source, platform-independent software pipeline for two-channel microarray spot quality control. This includes gridding, segmentation, quantification, quality assessment and data visualization. It hosts a set of independent applications, with interactions between the tools as well as import and export support for external software. The implementation of automated multivariate quality control assessment, which is a unique feature of MASQOT-GUI, is based on the previously documented and evaluated MASQOT methodology. Further abilities of the application are outlined and illustrated. AVAILABILITY: MASQOT-GUI is Java-based and licensed under the GNU LGPL. Source code and installation files are available for download at http://masqot-gui.sourceforge.net/
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| 7. |
- Bylesjö, Max, et al.
(author)
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Orthogonal projections to latent structures as a strategy for microarray data normalization
- 2007
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In: BMC Bioinformatics. - : Springer Science and Business Media LLC. - 1471-2105. ; 8:207
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Journal article (peer-reviewed)abstract
- BackgroundDuring generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded.ResultsWe introduce a normalization strategy for multi-channel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on single-channel Affymetrix data as well as dual-channel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spike-in) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods.ConclusionsThe OPLS methodology identifies joint variation within biological samples to enable the removal of sources of variation that are non-correlated (orthogonal) to the within-sample variation. This ensures that structured variation related to the underlying biological samples is separated from the remaining, bias-related sources of systematic variation. As a consequence, the methodology does not require any explicit knowledge regarding the presence or characteristics of certain biases. Furthermore, there is no underlying assumption that the majority of elements should be non-differentially expressed, making it applicable to specialized boutique arrays.
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| 8. |
- Eriksson, L., et al.
(author)
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A chemometrics toolbox based on projections and latent variables
- 2014
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In: Journal of Chemometrics. - : John Wiley & Sons. - 0886-9383 .- 1099-128X. ; 28:5, s. 332-346
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Journal article (peer-reviewed)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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| 9. |
- Eriksson, Lennart, et al.
(author)
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An Introduction to Some Basic Chemometric Tools
- 2021
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In: Chemometrics and Cheminformatics in Aquatic Toxicology. - : John Wiley & Sons. - 9781119681397 - 9781119681595 ; , s. 71-88
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Book chapter (peer-reviewed)abstract
- Design of experiments (DOE) and multivariate data analysis (MVDA) are two strong links in a chain of chemometrics and data analytics. DOE and MVDA can be applied well in aquatic toxicology and can be used independently of one another, but when used in conjunction they equip the researcher with high quality data and model results with a faithful interpretation. Two example aquatic toxicity datasets are used to describe the benefits of DOE and MVDA in aquatic toxicology. The popular data analytical tools principal components analysis (PCA) and partial least squares (PLS) are outlined in detail and exemplified. A modification of PLS called orthogonal partial least squares (OPLS) is introduced and it is shown how it can simplify model interpretation. When using chemometrics to establish quantitative structure-activity relationships (QSAR), a central questions is that of whether to derive a single-response QSAR model involving a single Y-variable, or a multi-response QSAR covering multiple responses. It is demonstrated how an initial PCA model on a matrix of response data can be used to figure out when a multi-response QSAR model might be a viable alternative to several single-response QSAR models.
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| 10. |
- Eriksson, Lennart, et al.
(author)
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CV-ANOVA for significance testing of PLS and OPLS® models
- 2008
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In: Journal of Chemometrics. - : Wiley. - 0886-9383 .- 1099-128X. ; 22:11-12, s. 594-600
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Journal article (peer-reviewed)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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