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  • 1. Artursson, Tom
    et al.
    Hagman, Anders
    Björk, Seth
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jacobsson, Sven P
    Study of Preprocessing Methods for the Determination of Crystalline Phases in Binary Mixtures of Drug Substances by X-ray Powder Diffraction and Multivariate Calibration2000In: Applied Spectroscopy, ISSN 0003-7028, E-ISSN 1943-3530, Vol. 54, no 8, p. 272A-301AArticle in journal (Refereed)
    Abstract [en]

    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.

  • 2.
    Benedict, Catherine
    et al.
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Geisler, Matt
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Huner, Norman
    Hurry, Vaughan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Consensus by democracy. Using meta-analyses of microarray and genomic data to model the cold acclimation signaling pathway in Arabidopsis.2006In: Plant Physiology, ISSN 0032-0889, E-ISSN 1532-2548, Vol. 141, no 4, p. 1219-1232Article in journal (Refereed)
    Abstract [en]

    The whole-genome response of Arabidopsis (Arabidopsis thaliana) exposed to different types and durations of abiotic stress has now been described by a wealth of publicly available microarray data. When combined with studies of how gene expression is affected in mutant and transgenic Arabidopsis with altered ability to transduce the low temperature signal, these data can be used to test the interactions between various low temperature-associated transcription factors and their regulons. We quantized a collection of Affymetrix microarray data so that each gene in a particular regulon could vote on whether a cis-element found in its promoter conferred induction (+1), repression (–1), or no transcriptional change (0) during cold stress. By statistically comparing these election results with the voting behavior of all genes on the same gene chip, we verified the bioactivity of novel cis-elements and defined whether they were inductive or repressive. Using in silico mutagenesis we identified functional binding consensus variants for the transcription factors studied. Our results suggest that the previously identified ICEr1 (induction of CBF expression region 1) consensus does not correlate with cold gene induction, while the ICEr3/ICEr4 consensuses identified using our algorithms are present in regulons of genes that were induced coordinate with observed ICE1 transcript accumulation and temporally preceding genes containing the dehydration response element. Statistical analysis of overlap and cis-element enrichment in the ICE1, CBF2, ZAT12, HOS9, and PHYA regulons enabled us to construct a regulatory network supported by multiple lines of evidence that can be used for future hypothesis testing.

  • 3.
    Bruce, Stephen J
    et al.
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Cloarec, Olivier
    Technologie Servier, 45000 Orleans, France.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marklund, Stefan L
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Moritz, Thomas
    Umeå Plant Science Center, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences, Umeå, Sweden.
    Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined ultra-performance liquid chromatography/mass spectrometry: From extraction to data analysis2009In: Analytical Biochemistry, ISSN 0003-2697, E-ISSN 1096-0309, Vol. 372, no 2, p. 237-249Article in journal (Refereed)
    Abstract [en]

    The investigation presented here describes a protocol designed to perform high-throughput metabolic profiling analysis on human blood plasma by ultra-performance liquid chromatography/mass spectrometry (UPLC/MS). To address whether a previous extraction protocol for gas chromatography (GC)/MS-based metabolic profiling of plasma could be used for UPLC/MS-based analysis, the original protocol was compared with similar methods for extraction of low-molecular-weight compounds from plasma via protein precipitation. Differences between extraction methods could be observed, but the previously published extraction method was considered the best. UPLC columns with three different stationary phases (C8, C18, and phenyl) were used in identical experimental runs consisting of a total of 60 injections of extracted male and female plasma samples. The C8 column was determined to be the best for metabolic profiling analysis on plasma. The acquired UPLC/MS data of extracted male and female plasma samples was subjected to principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS–DA). Furthermore, a strategy for compound identification was applied here, demonstrating the strength of high-mass-accuracy time-of-flight (TOF)/MS analysis in metabolic profiling.

  • 4.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nilsson, David
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Henriksson, Roger
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Hauksson, Jon
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Birgander, Richard
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    ADC texture-An imaging biomarker for high-grade glioma?2014In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 10, p. 101903-Article in journal (Refereed)
    Abstract [en]

    Purpose:

    Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers.

    Methods:

    Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression.

    Results:

    The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001.

    Conclusions:

    By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort. (C) 2014 Author(s).

  • 5.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Daniel
    Kusano, Miyako
    Moritz, Thomas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Data integration in plant biology the O2PLS method for combined modeling of transcript and metabolite data2007In: The Plant Journal, ISSN 0960-7412, E-ISSN 1365-313X, Vol. 52, no 6, p. 1181-1191Article in journal (Refereed)
    Abstract [en]

    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.

  • 6.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Daniel
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Sjödin, Andreas
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Jansson, Stefan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Orthogonal projections to latent structures as a strategy for microarray data normalization2007In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 8, no 207Article in journal (Refereed)
    Abstract [en]

    Background

    During 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.

    Results

    We 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.

    Conclusions

    The 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.

  • 7.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Daniel
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Sjödin, Andreas
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jansson, Stefan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    MASQOT: a method for cDNA microarray spot quality control.2005In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 6, p. 250-Article in journal (Refereed)
    Abstract [en]

    Background

    cDNA 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.

    Results

    A 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.

    Conclusion

    The 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.

  • 8.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Nilsson, Robert
    Umeå University, Faculty of Medicine, Umeå Life Science Centre (ULSC).
    Srivastava, Vaibhav
    Grönlund, Andreas
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Johansson, Annika I
    Jansson, Stefan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Karlsson, Jan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Wingsle, Gunnar
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Integrated analysis of transcript, protein and metabolite data to study lignin biosynthesis in hybrid aspen2009In: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 8, no 1, p. 199-210Article in journal (Refereed)
    Abstract [en]

    Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber, energy and wood products. We are now ready to make the transition from identifying candidate genes, controlling important biological processes, to discovering the detailed molecular function of these genes on a broader, more holistic, systems biology level. In this paper, a strategy is outlined for informative data generation and integrated modeling of systematic changes in transcript, protein and metabolite profiles measured from hybrid aspen samples. The aim is to study characteristics of common changes in relation to genotype-specific perturbations affecting the lignin biosynthesis and growth. We show that a considerable part of the systematic effects in the system can be tracked across all platforms and that the approach has a high potential value in functional characterization of candidate genes.

  • 9.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rantalainen, Mattias
    Cloarec, Olivier
    Nicholson, Jeremy K.
    Holmes, Elaine
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification2006In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 20, no 8-10, p. 341-351Article in journal (Refereed)
    Abstract [sv]

    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 classification performance in cases where the individual classes exhibit divergence in within-class variation, in analogy with soft independent modelling of class analogy (SIMCA) classification. The prediction results will be largely equivalent to traditional supervised classification using PLS-DA if no such variation is present in the classes. A discriminatory strategy is thus outlined, combining the strengths of PLS-DA and SIMCA classification within the framework of the OPLS-DA method. Furthermore, resampling methods have been employed to generate distributions of predicted classification results and subsequently assess classification belief. This enables utilisation of the class-orthogonal variation in a proper statistical context. The proposed decision rule is compared to common decision rules and is shown to produce comparable or less class-biased classification results.

  • 10.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rantalainen, Mattias
    Nicholson, Jeremy K
    Holmes, Elaine
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    K-OPLS package: Kernel-based orthogonal projections to latent structures for prediction and interpretation in feature space2008In: BMC Bioinformatics 2008 9, 106:1-7, Vol. 9, no 106, p. 1-7Article in journal (Refereed)
    Abstract [en]

    Background

    Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.

    Results

    We demonstrate an implementation of the K-OPLS algorithm for MATLAB and R, licensed under the GNU GPL and available at http://www.sourceforge.net/projects/kopls/. The package includes essential functionality and documentation for model evaluation (using cross-validation), training and prediction of future samples. Incorporated is also a set of diagnostic tools and plot functions to simplify the visualisation of data, e.g. for detecting trends or for identification of outlying samples. The utility of the software package is demonstrated by means of a metabolic profiling data set from a biological study of hybrid aspen.

    Conclusion

    The properties of the K-OPLS method are well suited for analysis of biological data, which in conjunction with the availability of the outlined open-source package provides a comprehensive solution for kernel-based analysis in bioinformatics applications.

  • 11.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Segura, Vincent
    Soolanayakanahally, Raju Y
    Rae, Anne M
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gustafsson, Petter
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Jansson, Stefan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Street, Nathaniel R
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    LAMINA: a tool for rapid quantification of leaf size and shape parameters2008In: BMC Plant Biology, ISSN 1471-2229, Vol. 8, no 82, p. 1-9Article in journal (Refereed)
    Abstract [en]

    Background

    An increased understanding of leaf area development is important in a number of fields: in food and non-food crops, for example short rotation forestry as a biofuels feedstock, leaf area is intricately linked to biomass productivity; in paleontology leaf shape characteristics are used to reconstruct paleoclimate history. Such fields require measurement of large collections of leaves, with resulting conclusions being highly influenced by the accuracy of the phenotypic measurement process.

    Results

    We have developed LAMINA (Leaf shApe deterMINAtion), a new tool for the automated analysis of images of leaves. LAMINA has been designed to provide classical indicators of leaf shape (blade dimensions) and size (area), which are typically required for correlation analysis to biomass productivity, as well as measures that indicate asymmetry in leaf shape, leaf serration traits, and measures of herbivory damage (missing leaf area). In order to allow Principal Component Analysis (PCA) to be performed, the location of a chosen number of equally spaced boundary coordinates can optionally be returned.

    Conclusion

    We demonstrate the use of the software on a set of 500 scanned images, each containing multiple leaves, collected from a common garden experiment containing 116 clones of Populus tremula (European trembling aspen) that are being used for association mapping, as well as examples of leaves from other species. We show that the software provides an efficient and accurate means of analysing leaf area in large datasets in an automated or semi-automated work flow.

  • 12.
    Bylesjö, Max
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjödin, Andreas
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Eriksson, Daniel
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Jansson, Stefan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    MASQOT-GUI: spot quality assessment for the two-channel microarray platform2006In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 22, no 20, p. 2554-2555Article in journal (Refereed)
    Abstract [en]

    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/

  • 13. Cloarec, Olivier
    et al.
    Dumas, Marc E
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Craig, Andrew
    Barton, Richard H
    Lindon, John C
    Nicholson, Jeremy K
    Holmes, Elaine
    Evaluation of the Orthogonal Projection on Latent Structure Model Limitations Caused by Chemical Shift Variability and Improved Visualization of Biomarker Changes in 1H NMR Spectroscopic Metabonomic Studies2005In: Analytical Chemistry, Vol. 77, no 2, p. 517-26Article in journal (Refereed)
    Abstract [en]

    In general, applications of metabonomics using biofluid NMR spectroscopic analysis for probing abnormal biochemical profiles in disease or due to toxicity have all relied on the use of chemometric techniques for sample classification. However, the well-known variability of some chemical shifts in 1H NMR spectra of biofluids due to environmental differences such as pH variation, when coupled with the large number of variables in such spectra, has led to the situation where it is necessary to reduce the size of the spectra or to attempt to align the shifting peaks, to get more robust and interpretable chemometric models. Here, a new approach that avoids this problem is demonstrated and shows that, moreover, inclusion of variable peak position data can be beneficial and can lead to useful biochemical information. The interpretation of chemometric models using combined back-scaled loading plots and variable weights demonstrates that this peak position variation can be handled successfully and also often provides additional information on the physicochemical variations in metabonomic data sets.

  • 14. Cloarec, Olivier
    et al.
    Dumas, Marc-Emmanuel
    Craig, Andrew
    Barton, Richard H
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hudson, Jane
    Blancher, Christine
    Gauguier, Dominique
    Lindon, John C
    Holmes, Elaine
    Nicholson, Jeremy
    Statistical Total Correlation Spectroscopy: An Exploratory Approach for Latent Biomarker Identification from Metabolic 1H NMR Data Sets2005In: Analytical Chemistry, Vol. 77, no 5, p. 1282-89Article in journal (Refereed)
    Abstract [en]

    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 analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.

  • 15.
    Druart, Nathalie
    et al.
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Johansson, Annika
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Baba, Kyoko
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Schrader, Jarmo
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Sjödin, Andreas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Bhalerao, Rupali R
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Resman, Lars
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Bhalerao, Rishikesh P
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Environmental and hormonal regulation of the activity–dormancy cycle in the cambial meristem involves stage-specific modulation of transcriptional and metabolic networks2007In: The Plant Journal, Vol. 50, p. 557-73Article in journal (Refereed)
    Abstract [en]

    We have performed transcript and metabolite profiling of isolated cambial meristem cells of the model tree aspen during the course of their activity–dormancy cycle to better understand the environmental and hormonal regulation of this process in perennial plants. Considerable modulation of cambial transcriptome and metabolome occurs throughout the activity–dormancy cycle. However, in addition to transcription, post-transcriptional control is also an important regulatory mechanism as exemplified by the regulation of cell-cycle genes during the reactivation of cambial cell division in the spring. Genes related to cold hardiness display temporally distinct induction patterns in the autumn which could explain the step-wise development of cold hardiness. Factors other than low temperature regulate the induction of early cold hardiness-related genes whereas abscisic acid (ABA) could potentially regulate the induction of late cold hardiness-related genes in the autumn. Starch breakdown in the autumn appears to be regulated by the ‘short day’ signal and plays a key role in providing substrates for the production of energy, fatty acids and cryoprotectants. Catabolism of sucrose and fats provides energy during the early stages of reactivation in the spring, whereas the reducing equivalents are generated through activation of the pentose phosphate shunt. Modulation of gibberellin (GA) signaling and biosynthesis could play a key role in the regulation of cambial activity during the activity–dormancy cycle as suggested by the induction of PttRGA which encodes a negative regulator of growth in the autumn and that of a GA-20 oxidase, a key gibberellin biosynthesis gene during reactivation in spring. In summary, our data reveal the dynamics of transcriptional and metabolic networks and identify potential targets of environmental and hormonal signals in the regulation of the activity–dormancy cycle in cambial meristem.

  • 16.
    Dumarey, Melanie
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Galindo-Prieto, Beatriz
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Fransson, Magnus
    Pharmaceutical Development, AstraZeneca R&D, Mölndal, Sweden.
    Josefson, Mats
    Pharmaceutical Development, AstraZeneca R&D, Mölndal, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OPLS methods for the analysis of hyperspectral images—comparison with MCR-ALS2014In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 8, p. 687-696Article in journal (Refereed)
    Abstract [en]

    Two new orthogonal projections to latent structures (OPLS) based methods were proposed to analyze hyperspectral images, enabling the visualization ofmultiple chemical compounds in onematrix without the need of extensive preprocessing. Both proposed methods delivered images representing the chemical distribution in the ribbon similar to the more traditional multivariate curve resolution–alternating least squares (MCR-ALS) method, but their image background was less dynamic resulting in a stronger chemical contrast. This indicated that the methods successfully removed structured variation orthogonal to the chemical information (pure spectra of individual compounds), which was confirmed by the fact that physical scattering effects caused by grooves and edges were captured in the images visualizing the orthogonal components of the model. Hereby, the OPLS-based method employing the pure spectra as weights in the OPLS algorithm was more successful in distinguishing compounds with a similar spectral signal than the transposed OPLS algorithm(pure spectra of individual compounds were used as response in OPLS model). It should be noted that for the main compounds, the MCR-ALS method enabled easier visual interpretation compared to the OPLS-based methods by setting all values below zero to zero, resulting in a higher contrast between pixels containing the studied compound and pixels not containing that compound.

  • 17.
    Dumarey, Melanie
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wikström, Håkan
    Pharmaceutical Development, AstraZeneca R&D Mölndal, Sweden .
    Fransson, Magnus
    Pharmaceutical Development, AstraZeneca R&D Mölndal, Sweden .
    Sparén, Anders
    Pharmaceutical Development, AstraZeneca R&D Mölndal, Sweden .
    Tajarobi, Pirjo
    Pharmaceutical Development, AstraZeneca R&D Mölndal, Sweden .
    Josefson, Mats
    Pharmaceutical Development, AstraZeneca R&D Mölndal, Sweden .
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Combining experimental design and orthogonal projections to latent structures to study the influence of microcrystalline cellulose properties on roll compaction2011In: International Journal of Pharmaceutics, ISSN 0378-5173, E-ISSN 1873-3476, Vol. 416, no 1, p. 110-119Article in journal (Refereed)
    Abstract [en]

    Roll compaction is gaining importance in pharmaceutical industry for the dry granulation of heat or moisture sensitive powder blends with poor flowing properties prior to tabletting. We studied the influence of microcrystalline cellulose (MCC) properties on the roll compaction process and the consecutive steps in tablet manufacturing. Four dissimilar MCC grades, selected by subjecting their physical characteristics to principal components analysis, and three speed ratios, i.e. the ratio of the feed screw speed and the roll speed of the roll compactor, were included in a full factorial design. Orthogonal projection to latent structures was then used to model the properties of the resulting roll compacted products (ribbons, granules and tablets) as a function of the physical MCC properties and the speed ratio. This modified version of partial least squares regression separates variation in the design correlated to the considered response from the variation orthogonal to that response. The contributions of the MCC properties and the speed ratio to the predictive and orthogonal components of the models were used to evaluate the effect of the design variation. The models indicated that several MCC properties, e.g. bulk density and compressibility, affected all granule and tablet properties, but only one studied ribbon property: porosity. After roll compaction, Ceolus KG 1000 resulted in tablets with obvious higher tensile strength and lower disintegration time compared to the other MCC grades. This study confirmed that the particle size increase caused by roll compaction is highly responsible for the tensile strength decrease of the tablets.

  • 18.
    Eliasson, Mattias
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rännar, Stefan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Madsen, Rasmus
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Donten, Magdalena A
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marsden-Edwards, Emma
    Waters Corp, Milford, MA 01757 USA .
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Shockcor, John P
    Waters Corp, Milford, MA 01757 USA .
    Johansson, Erik
    Umetr AB, Umeå, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Strategy for optimizing LC-MS data processing in Metabolomics: A design of experiments approach2012In: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 84, no 15, p. 6869-6876Article in journal (Refereed)
    Abstract [en]

    A strategy for optimizing LC-MS metabolomics data processing is proposed. We applied this strategy on the XCMS open source package written in R on both human and plant biology data. The strategy is a sequential design of experiments (DoE) based on a dilution series from a pooled sample and a measure of correlation between diluted concentrations and integrated peak areas. The reliability index metric, used to define peak quality, simultaneously favors reliable peaks and disfavors unreliable peaks using a weighted ratio between peaks with high and low response linearity. DoE optimization resulted in the case studies in more than 57% improvement in the reliability index compared to the use of the default settings. The proposed strategy can be applied to any other data processing software involving parameters to be tuned, e.g., MZmine 2. It can also be fully automated and used as a module in a complete metabolomics data processing pipeline.

  • 19.
    Eliasson, Mattias
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Rännar, Stefan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    From data processing to multivariate validation: essential steps in extracting interpretable information from metabolomics data2011In: Current Pharmaceutical Biotechnology, ISSN 1389-2010, E-ISSN 1873-4316, Vol. 12, no 7, p. 996-1004Article, review/survey (Refereed)
    Abstract [en]

    In metabolomics studies there is a clear increase of data. This indicates the necessity of both having a battery of suitable analysis methods and validation procedures able to handle large amounts of data. In this review, an overview of the metabolomics data processing pipeline is presented. A selection of recently developed and most cited data processing methods is discussed. In addition, commonly used chemometric and machine learning analysis methods as well as validation approaches are described.

  • 20. Eriksson, L.
    et al.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    A chemometrics toolbox based on projections and latent variables2014In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 5, p. 332-346Article in journal (Refereed)
    Abstract [en]

    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.

  • 21. Eriksson, Lennart
    et al.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gottfries, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Holmes, Elaine
    Johansson, Erik
    Lindgren, Fredrik
    Long, Ingrid
    Lundstedt, Torbjörn
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Using chemometrics for navigating in the large data sets of genomics, proteomics, and metabonomics (gpm)2004In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642 (Print) 1618-2650 (Online), Vol. 380, no 3, p. 419-29Article in journal (Refereed)
    Abstract [en]

    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.

  • 22. Eriksson, Lennart
    et al.
    Damborsky, J
    Earll, M
    Johansson, Erik
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Three-block bi-focal PLS (3BIF-PLS) and its application in QSAR2004In: SAR and QSAR in Environmental Research, ISSN 1062-936X, Vol. 15, no 5 & 6, p. 481-99Article in journal (Refereed)
    Abstract [en]

    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.

  • 23. Eriksson, Lennart
    et al.
    Gottfries, Johan
    Lundstedt, Torbjörn
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Editorial2007In: Journal of Chemometrics, Vol. 21, no 10-11, p. 397-Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

     

     

     

     

  • 24. Eriksson, Lennart
    et al.
    Johansson, Erik
    Kettaneh-Wold, Nouna
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wikström, Conny
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multi- and Megavariate Data Analysis: Part II: Advanced Applications and Method Extensions2006Book (Refereed)
    Abstract [en]

    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.

  • 25. Eriksson, Lennart
    et al.
    Kettaneh-Wold, Nouna
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wikström, Conny
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multi- and Megavariate Data Analysis: Part I: Basic Principles and Applications2006Book (Refereed)
  • 26. Eriksson, Lennart
    et al.
    Rosén, Josefin
    Johansson, Erik
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Orthogonal PLS (OPLS) Modeling for Improved Analysis and Interpretation in Drug Design2012In: Molecular informatics, ISSN 1868-1751, Vol. 31, no 6-7, p. 414-419Article in journal (Refereed)
    Abstract [en]

    Partial least squares (PLS) regression is a flexible data analytical approach, which can be made even more versatile and useful by various modifications. In this article we describe the extension into orthogonal PLS modeling, in terms of two new methods, called OPLS and O2PLS, with similar prediction capacity but improved model interpretation.

  • 27. Eriksson, Lennart
    et al.
    Toft, Marianne
    Johansson, Erik
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Separating Y-predictive and Y-orthogonal variation in multi-block spectral data2006In: Journal of Chemometrics, Vol. 20, p. 352-61Article in journal (Refereed)
    Abstract [en]

    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.

  • 28. Eriksson, Lennart
    et al.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Bro, Rasmus
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data2000In: Analytica Chimica Acta, Vol. 420, no 2, p. 181-95Article in journal (Refereed)
    Abstract [en]

    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.

  • 29. Eriksson, Lennart
    et al.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    CV-ANOVA for significance testing of PLS and OPLS® models2008In: Journal of Chemometrics, Vol. 22, no 11-12, p. 594-600Article in journal (Refereed)
    Abstract [en]

    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.

  • 30. Eriksson, Lennart
    et al.
    Wold, Svante
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multivariate analysis of congruent images (MACI)2005In: Journal of Chemometrics, Vol. 19, no 5-7, p. 393-403Article in journal (Refereed)
    Abstract [en]

    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.

  • 31.
    Fahlén, Jessica
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Landfors, Mattias
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Freyhult, Eva
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).
    Bylesjö, Max
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hvidsten, Torgeir
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Bioinformatics strategies for cDNA-microarray data processing2009In: Batch effects and noise in microarray experiments: sources and solutions / [ed] Scherer, Andreas, Wiley and Sons , 2009, 1, , p. 272p. 61-74Chapter in book (Other academic)
    Abstract [en]

    

    Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper pre-processing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment’s sensitivity andits bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.

  • 32.
    Gabrielsson, Jon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Airiau, Christian
    Schmidt, Bernd
    Escott, Richard
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    The OPLS methodology for analysis of multi-block batch process data2006In: Journal of Chemometrics, Vol. 20, no 8-10, p. 362-9Article in journal (Refereed)
    Abstract [en]

    With increasing availability of different process analysers multiple data sources are commonly available and this will impose new challenges and enable new types of investigations. The ability to separate joint, complementary and redundant information in multiple block data will be of increasing importance. In this study data from a batch mini plant were collected and O2PLS was implemented to facilitate a combined analysis of spectroscopic and process data. This enables assessment of both the joint and complementary variations in the respective data sets. The different types of variation that were separated were then modelled together to evaluate their individual correlation to a time response. By combining data of different origin an uncomplicated summary of the variation was accomplished and a deeper understanding of process interactions was gained. The analysis of separated variation with a response variable proved useful for verifying the supposed correlation between the joint variation and time.

  • 33.
    Gabrielsson, Jon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Airiaub, Christian
    Schmidt, Bernd
    Escott, Richard
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OPLS methodology for analysis of pre-processing effects on spectroscopic data2006In: Chemometrics and Intelligent Laboratory Systems, Vol. 84, no 1-2, p. 153-8Article in journal (Refereed)
    Abstract [en]

    Pre-processing of spectroscopic data is commonly applied to remove unwanted systematic variation. Possible loss of information and ambiguity regarding discarded variation are issues that complicate pre-treatment of data. In this paper, OPLS methodology is applied to evaluate different techniques for pre-processing of spectroscopic data gathered from a batch process. The objective is to present a rational scheme for analysis of pre-processing in order to understand the influence and effect of pre-treatment.

    O2PLS uses linear regression to divide the systematic variation in X and Y into three parts; one part with joint X–Y covariation, i.e. related to both X and Y, one part of X with Y-orthogonal variation and one part of Y with X-orthogonal variation.

    All of the investigated pre-treatment methods removed an additive baseline as expected. In the analysis of raw and differentiated data variation associated with the baseline was found in the Y-orthogonal part of X. Orthogonal information was also found in Y, which suggests that this pre-processing procedure not only removed variation. This would have been more difficult to detect without the O2PLS model since both raw and differentiated data must be analysed simultaneously.

    Development of a knowledge based strategy with OPLS methodology is an important step towards eliminating trial and error approaches to pre-processing.

  • 34.
    Gabrielsson, Jon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Airiau, Christian
    Schmidt, Bernd
    Escott, Richard
    Combining process and spectroscopic data to improve batch modeling2006In: AIChE Journal, Vol. 52, no 9, p. 3164-72Article in journal (Refereed)
    Abstract [en]

    Pharmaceutical production is at present characterized by static processes where quality is guaranteed by controlling the purity of the final product. Achieving better control throughout the process, as a means for improving product quality, is one of the objectives of the PAT initiative by the FDA. A data set consisting of 11 batches characterized by UV spectroscopy together with process data was used in this study. Design of experiments was used to introduce controlled process variation in test batches. The objective was to investigate possible advantages of MSPC using a combined data set, compared to separate models of the respective data sets. Individual models for the separate data sets show that they contain complementary information. A major advantage of combining spectroscopic and process data is that deviations that would go unnoticed using just an individual model can be detected and interpreted. All process manipulations were detected by the combined data set model. Implementation of these methods to batch processes in primary and secondary pharmaceutical production is feasible. An enhanced understanding of the process together with control tools should lead to a well-understood process and, ultimately, real time release. © 2006 American Institute of Chemical Engineers AIChE J, 2006

  • 35.
    Gabrielsson, Jon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Recent Developments in Multivariate Calibration2006In: Critical Reviews in Analytical Chemistry, Vol. 36, no 3-4, p. 243-55Article in journal (Refereed)
    Abstract [en]

    This review covers the area of multivariate calibration; from pre-processing of data prior to modeling and applications of regression methods for calibration and prediction. The importance of pre-treatment of data is highlighted with many of the recently developed methods together with traditional methods. Several articles provide comparisons between different pre-processing methods. Methods for data from coupled chromatographic methods, which have found increasing use and where data pre-processing is a prerequisite for multivariate modeling, are also included. Many of the novel chemometric methods deal with model complexity and interpretation. A diverse set of applications are also presented and references are also given to early papers, making it possible to acquire a deeper knowledge of methods of interest.

  • 36.
    Galindo-Prieto, Beatriz
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Lennart
    MKS Umetrics AB, Umeå, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis2015In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 146, p. 297-304Article in journal (Refereed)
    Abstract [en]

    Abstract Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this parameter is here applied in multivariate time series analysis to achieve an improved diagnosis of process dynamics. To this end, OPLS-VIP has been tested in three real-world industrial data sets; the first data set corresponds to a pulp manufacturing process using a continuous digester, the second one involves data from an industrial heater that experienced problems, and the third data set contains measures of the chemical oxygen demand into the effluent of a newsprint mill. The outcomes obtained using OPLS-VIP are benchmarked against classical PLS-VIP results. It is demonstrated how OPLS-VIP provides a better diagnosis and understanding of the time series behavior than PLS-VIP.

  • 37.
    Galindo-Prieto, Beatriz
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Lennart
    MKS Umetrics, Umeå, Sweden.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Variable influence on projection (VIP) for orthogonal projections to latent structures (OPLS)2014In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, no 8, p. 623-632Article in journal (Refereed)
    Abstract [en]

    A new approach for variable influence on projection (VIP) is described, which takes full advantage of the orthogonal projections to latent structures (OPLS) model formalism for enhanced model interpretability. This means that it will include not only the predictive components in OPLS but also the orthogonal components. Four variants of variable influence on projection (VIP) adapted to OPLS have been developed, tested and compared using three different data sets, one synthetic with known properties and two real-world cases.

  • 38. Gerber, Lorenz
    et al.
    Eliasson, Mattias
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Sundberg, Björn
    Multivariate curve resolution provides a high-throughput data processing pipeline for pyrolysis-gas chromatography/mass spectrometry2012In: Journal of Analytical and Applied Pyrolysis, ISSN 0165-2370, E-ISSN 1873-250X, Vol. 95, p. 95-100Article in journal (Refereed)
    Abstract [en]

    We present a data processing pipeline for Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) data that is suitable for high-throughput analysis of lignocellulosic samples. The aproach applies multivariate curve resolution by alternate regression (MCR-AR) and automated peak assignment. MCR-AR employs parallel processing of multiple chromatograms, as opposed to sequential processing used in prevailing applications. Parallel processing provides a global peak list that is consistent for all chromatograms, and therefore does not require tedious manual curation. We evaluated this approach on wood samples from aspen and Norway spruce, and found that parallel processing results in an overall higher precision of peak area from integrated peaks. To further increase the speed of data processing we evaluated automated peak assignment solely based on basepeak mass. This approach gave estimates of the proportion of lignin (as syringyl-, guaiacyl and p-hydroxyphenyl-type lignin) and carbohydrate polymers in the wood samples that were in high agreement with those where peak assignments were based on full spectra. This method establishes Py-GC/MS as a sensitive, robust and versatile high-throughput screening platform well suited to a non-specialist operator.

  • 39. Goodacre, Royston
    et al.
    Broadhurst, David
    Smilde, Age K
    Kristal, Bruce S
    Baker, J David
    Beger, Richard
    Bessant, Conrad
    Connor, Susan
    Capuani, Giorgio
    Craig, Andrew
    Ebbels, Tim
    Kell, Douglas B
    Manetti, Cesare
    Newton, Jack
    Paternostro, Giovanni
    Somorjai, Ray
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Wulfert, Florian
    Proposed minimum reporting standards for data analysis in metabolomics2007In: Metabolomics, Vol. 3, p. 231-41Article in journal (Refereed)
    Abstract [en]

    The goal of this group is to define the reporting requirements associated with the statistical analysis (including univariate, multivariate, informatics, machine learning etc.) of metabolite data with respect to other measured/collected experimental data (often called metadata). These definitions will embrace as many aspects of a complete metabolomics study as possible at this time. In chronological order this will include: Experimental Design, both in terms of sample collection/matching, and data acquisition scheduling of samples through whichever spectroscopic technology used; Deconvolution (if required); Pre-processing, for example, data cleaning, outlier detection, row/column scaling, or other transformations; Definition and parameterization of subsequent visualizations and Statistical/Machine learning Methods applied to the dataset; If required, a clear definition of the Model Validation Scheme used (including how data are split into training/validation/test sets); Formal indication on whether the data analysis has been Independently Tested (either by experimental reproduction, or blind hold out test set). Finally, data interpretation and the visual representations and hypotheses obtained from the data analyses.

  • 40.
    Gorzsás, András
    et al.
    Swedish University of Agricultural Sciences (SLU), Sweden.
    Stenlund, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Persson, Per
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sundberg, Björn
    Umeå Plant Science Centre, Department of Forest Genetics and Plant Physiology, Swedish University of Agricultural Sciences (SLU), Sweden.
    Cell specific chemotyping and multivariate imaging by combined FT-IR microspectroscopy and OPLS analysis reveals the chemical landscape of secondary xylem2011In: The Plant Journal, ISSN 0960-7412, E-ISSN 1365-313X, Vol. 66, no 5, p. 903-914Article in journal (Refereed)
    Abstract [en]

    Fourier-transform infrared (FT-IR) spectroscopy combined with microscopy enables acquiring chemical information from native plant cell walls with high spatial resolution. Combined with a 64 x 64 focal plane array (FPA) detector 4096 spectra from a 0.3 x 0.3 mm image can be simultaneously obtained, where each spectrum represents a compositional and structural "fingerprint" of all cell wall components. For optimal use and analysis of such large amount of information, multivariate approaches are preferred. Here, FT-IR microspectroscopy with FPA detection is combined with orthogonal projections to latent structures discriminant analysis (OPLS-DA). This allows for 1) the extraction of spectra from specific cell types, 2) identification and characterization of different chemotypes using the full spectral information, and 3) further visualising the pattern of identified chemotypes by multivariate imaging. As proof of concept, the chemotypes of Populus tremula xylem cell types are described. The approach revealed unknown features about chemical plasticity and patterns of lignin composition in wood fibers that would have remained hidden in the dataset with traditional data analysis. The applicability of the method on Arabidopsis xylem, and its usefulness in mutant chemotyping is also demonstrated. The methodological approach is not limited to xylem tissues but can be applied to any plant organ/tissue also using other microspectroscopy techniques such as Raman- and UV-microspectroscopy.

  • 41.
    Gottfries, Johan
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Johansson, Erik
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    On the impact of uncorrelated variation in regression mathematics2008In: Journal of Chemometrics, Vol. 22, no 11-12, p. 565-70Article in journal (Refereed)
    Abstract [en]

    The objective of the present study is to investigate if, and if so, how uncorrelated variation relates to regression mathematics as exemplified by partial least squares (PLS) methodology. In contrast to previous methods, orthogonal partial least squares (OPLS) method requires a multi-focus, in the sense that in parallel to calculation of correlation it requires an analysis of orthogonal variation, i.e. the uncorrelated structure in a comprehensive way. Subsequent to the estimation of the correlation is the remaining orthogonal variation, i.e. uncorrelated data, divided into uncorrelated structure and stochastic noise by the OPLS component. Thus, it appears obvious that it is of interest to understand how the uncorrelated variation can influence the interpretation of the regression model. We have scrutinized three examples that pinpoint additional value from OPLS regarding the modelling of the orthogonal, i.e. uncorrelated, variation in regression mathematics. In agreement with the results, we conclude that uncorrelated variations do impact interpretations of regression analyses output and provides not only opportunities by OPLS but also an obligation for the user to maximize benefit from OPLS.

  • 42.
    Hauksson, Jón B
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Edlund, Ulf
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    NMR processing techniques based on multivariate data analysis and orthogonal signal correction. 13C CP/MAS NMR spectroscopic characterization of softwood kraft pulp2001In: Magnetic Resonance in Chemistry, Vol. 39, no 5, p. 267-75Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel way of extracting information from a series of severely overlapped NMR spectra using multivariate data analysis techniques. A number of softwood pulps were prepared from wood chips that were subjected to kraft cooking conditions in laboratory digesters. In addition to measurements of traditional physical parameters, the pulps were characterized using standard 13C CP/MAS NMR spectroscopy. The relationship between the kappa number and both the NMR time domain and frequency domain data was modeled using multivariate data analysis techniques. The variation in the NMR spectra that was not correlated with the kappa number was removed using a new preprocessing tool, orthogonal signal correction (OSC). The resulting OSC-treated NMR spectra were used as descriptors to generate partial least-squares projections to latent structures (PLS) models for the variation of the kappa number. PLS weights were used to generate NMR sub-spectra which correspond to changes in the pulps that occur as the pulping process proceeds from high to low values of the kappa number. The sub-spectra were used to gain insight into the changes in the pulps occurring at the molecular level. Concomitant changes in cellulose crystallinity and the amounts of hemicellulose and lignin were observed in these sub-spectra. Copyright © 2001 John Wiley & Sons, Ltd.

  • 43. Hoffman, Daniel E
    et al.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bylesjö, Max
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Maria E
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Moritz, Thomas
    Changes in diurnal patterns within the Populus transcriptome and metabolome in response to photoperiod variation2010In: Plant, Cell and Environment, ISSN 0140-7791, E-ISSN 1365-3040, Vol. 33, no 8, p. 1298-1313Article in journal (Refereed)
    Abstract [en]

    Changes in seasonal photoperiod provides an important environmental signal that affects the timing of winter dormancy in perennial, deciduous, temperate tree species, such as hybrid aspen (Populus tremula x Populus tremuloides). In this species, growth cessation, cold acclimation and dormancy are induced in the autumn by the detection of day-length shortening that occurs at a given critical day length. Important components in the detection of such day-length changes are photoreceptors and the circadian clock, and many plant responses at both the gene regulation and metabolite levels are expected to be diurnal. To directly examine this expectation and study components in these events, here we report transcriptomic and metabolomic responses to a change in photoperiod from long to short days in hybrid aspen. We found about 16% of genes represented on the arrays to be diurnally regulated, as assessed by our pre-defined criteria. Furthermore, several of these genes were involved in circadian-associated processes, including photosynthesis and primary and secondary metabolism. Metabolites affected by the change in photoperiod were mostly involved in carbon metabolism. Taken together, we have thus established a molecular catalog of events that precede a response to winter.

  • 44. Idborg, Helena
    et al.
    Oliynyk, Ganna
    Rännar, Stefan
    AcureOmics AB.
    Forshed, Jenny
    Branca, Rui Mamede
    Donten, Magdalena
    Gustafsson, Johanna
    Vikerfors, Anna
    Gunnarsson, Iva
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lehtiö, Janne
    Lundstedt, Torbjörn
    Svenungsson, Elisabet
    Jakobsson, Per-Johan
    Systems biology of SLE: biochemical characterisation of subgroups within SLE for improved diagnosis and treatment2012In: Annals of the Rheumatic Diseases, ISSN 0003-4967, E-ISSN 1468-2060, Vol. 71, p. A12-Article in journal (Other academic)
  • 45. Idborg, Helena
    et al.
    Rannar, Stefan
    Oliynyk, Ganna
    Forshed, Jenny
    Branca, Rui Mamede
    Donten, Magdalena
    Bennett, Kate
    Gustafsson, Johanna
    Vikerfors, Anna
    Truedsson, Lennart
    Nilsson, Bo
    Gunnarsson, Iva
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lehtio, Janne
    Lundstedt, Torbjorn
    Svenungsson, Elisabet
    Jakobsson, Per-Johan
    STRATIFICATION OF SLE PATIENTS FOR IMPROVED DIAGNOSIS AND TREATMENT2013In: Annals of the Rheumatic Diseases, ISSN 0003-4967, E-ISSN 1468-2060, Vol. 72, p. A80-A80Article in journal (Other academic)
    Abstract [en]

    Background. Systemic autoimmune diseases (SAIDs) affect about 2% of the population in Western countries. Sufficient diagnostic criteria are lacking due to the heterogeneity within diagnostic categories and apparent overlap regarding symptoms and patterns of autoantibodies between different diagnoses. Systemic lupus erythematosus (SLE) is regarded as a prototype for SAIDs and we hypothesise that subgroups of patients with SLE may have different pathogenesis and should consequently be subject to different treatment strategies.

    Objectives. Our goal is to find new biomarkers to be used for the identification of more homogenous patient populations for clinical trials and to identify sub-groups of patients with high risk of for example cardiovascular events.

    Methods. In this study we have utilised 320 SLE patients from the Karolinska lupus cohort and 320 age and gender matched controls. The SLE cohort was characterised based on clinical, genetic and serological data and combined by multivariate data analysis in a systems biology approach to study possible subgroups. A pilot study was designed to verify and investigate suggested subgroups of SLE. Two main subgroups were defined: One group was defined as having SSA and SSB antibodies and a negative lupus anticoagulant test (LAC), i.e., a “Sjögren-like” group. The other group was defined as being negative for SSA and SSB antibodies but positive in the LAC test.i.e. an “APS-like” group. EDTA-plasma from selected patients in these two groups and controls were analysed using a mass spectrometry (MS) based proteomic and metabolomic approach. Pathway analysis was then performed on the obtained data.

    Results. Our pilot study showed that differences in levels of proteins and metabolites could separate disease groups from population controls. The profile/pattern of involved factors in the complement system supported a division of SLE in two major subgroups, although each individual factor was not significantly different between subgroups. Complement factor 2 (C2) and membrane attack complex (MAC) were analysed in the entire cohort with complementary methods and C2 verifies our results while the levels of MAC did not differ between SLE subgroups. The generated metabolomics data clearly separated SLE patients from controls in both gas chromatography (GC)-MS and liquid chromatography (LC)-MS data. We found for example that tryptophan was lower in the SLE patients compared to controls.

    Conclusions. Our systems biology approach may lead to a better understanding of the disease and its pathogenesis, and assigning patients into subgroups will result in improved diagnosis and better outcome measures of SLE.

  • 46. Jakobsson, P-J
    et al.
    Svenungsson, E.
    Idborg, H.
    Nilsson, P.
    Wheelock, C.
    Gunnarsson, I.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Lehtio, J.
    Koistinen, I. S.
    Proteomics and metabolomics in the classification of SLE subsets2014In: Scandinavian Journal of Rheumatology, ISSN 0300-9742, E-ISSN 1502-7732, Vol. 43, no Suppl. 127 Meeting Abstract PP267, p. 95-95Article in journal (Other academic)
    Abstract [en]

    Background: Systemic autoimmune diseases (SAIDs) affect about 0.5–1% of Europeans with a remarkable female predominance (80–90%). Present diagnostic entities are vague and rely on fairly old and unspecific criteria that do not use state-of-the-art laboratory parameters. New diagnostic tools and therapeutic/prognostic biomarkers are needed. Systemic lupus erythematosus (SLE) is regarded as a prototype for SAIDs and we hypothesized that subgroups of patients with SLE may have different pathogenesis and should consequently be subject to different treatment strategies. Our aim was to find new biomarkers to be used for the identification of more homogeneous patient populations.

    Method: This study involved 320 SLE patients from the Karolinska lupus cohort and 320 age- and gender-matched controls. Plasma samples were analysed using an antibody Luminex assay with 367 antibodies targeting 281 unique selected proteins. Subsets of the SLE cohort and controls were also analysed for their sphingolipid content, as well as by a metabolomic and mass spectrometry-based proteomic approach.

    Results: The Luminex platform revealed 66 proteins found at higher or lower levels in SLE. Mass spectrometry-based proteomics has shown very promising data for the components of the complement and coagulation cascades. Metabolomics identified patterns of plasma metabolites that separate SLE from controls. Finally, analysis of >30 sphingolipids demonstrated a specific group of these lipids at significantly higher concentrations in SLE compared to controls. Following treatment, these differences were normalized.

    Conclusions: Preliminary data demonstrate the involvement of several distinct biochemical pathways in SLE that can be used for biomarker discovery and a better understanding of the pathophysiological events underlying the disease.

  • 47.
    Jiye, A
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Granström, Micael
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Marklund, Stefan
    Johansson, Annika
    Stenlund, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jiye, A
    Guangji, Wang
    Dynamic modification of blood erythrocytes metabolism based on GC/TOF-MS analysis2006Other (Other (popular science, discussion, etc.))
  • 48.
    Jiye, A
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gullberg, Jonas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Johansson, Annika I.
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Marklund, Stefan L.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Clinical chemistry.
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Extraction and GC/MS analysis of the human blood plasma metabolome2005In: ANALYTICAL CHEMISTRY, ISSN 0003-2700, Vol. 77, no 24, p. 8086-94Article in journal (Refereed)
    Abstract [en]

    Analysis of the entire set of low molecular weight compounds (LMC), the metabolome, could provide deeper insights into mechanisms of disease and novel markers for diagnosis. In the investigation, we developed an extraction and derivatization protocol, using experimental design theory (design of experiment), for analyzing the human blood plasma metabolome by GC/MS. The protocol was optimized by evaluating the data for more than 500 resolved peaks using multivariate statistical tools including principal component analysis and partial least-squares projections to latent structures (PLS). The performance of five organic solvents (methanol, ethanol, acetonitrile, acetone, chloroform), singly and in combination, was investigated to optimize the LMC extraction. PLS analysis demonstrated that methanol extraction was particularly efficient and highly reproducible. The extraction and derivatization conditions were also optimized. Quantitative data for 32 endogenous compounds showed good precision and linearity. In addition, the determined amounts of eight selected compounds agreed well with analyses by independent methods in accredited laboratories, and most of the compounds could be detected at absolute levels of similar to 0.1 pmol injected, corresponding to plasma concentrations between 0.1 and 1 mu M. The results suggest that the method could be usefully integrated into metabolomic studies for various purposes, e.g., for identifying biological markers related to diseases.

  • 49. Jiye, A
    et al.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Gullberg, Jonas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Moritz, Thomas
    Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Marklund, Stefan
    Global analysis of low-molecular-weight compounds in human plasma using GC/TOF-MS2004In: DRUG METABOLISM REVIEWS, ISSN 0360-2532, Vol. 36, p. 246-246Article in journal (Other academic)
    Abstract [en]

    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 GC/TOF-MS. The overall strategy for the development of the method was based on using design-of-experimental (DOE), and data evaluation based on multivariate statistical tools like PCA and PLS. The results showed that the extraction efficiency for different solvents varied, and that methanol was important for high reproducibility. More than 300 compounds could be detected in one analysis. Forty five of them were identified, including amino acids, lipids and free fatty acids, organic acids, carbohydrates and so on. The quantitative data of these metabolites showed, with two exceptions, high precision and good linearity between response and concentration. By using this method it is now possible to analyze plasma samples with high throughput to identify metabolic biomarkers for different kinds of diseases.

  • 50.
    Jonsson, Pär
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bruce, Stephen J
    Moritz, Thomas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Sjöström, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Plumb, Robert
    Granger, Jennifer
    Maibaum, Elaine
    Nicholson, Jeremy K
    Holmes, Elaine
    Antti, Henrik
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Extraction, interpretation and validation of information for comparing samples in metabolic LC/MS data sets2005In: The Analyst, ISSN 0003-2654, E-ISSN 1364-5528, Vol. 130, no 5, p. 701-707Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    LC/MS is an analytical technique that, due to its high sensitivity, has become increasingly popular for the generation of metabolic signatures in biological samples and for the building of metabolic data bases. However, to be able to create robust and interpretable ( transparent) multivariate models for the comparison of many samples, the data must fulfil certain specific criteria: (i) that each sample is characterized by the same number of variables, (ii) that each of these variables is represented across all observations, and (iii) that a variable in one sample has the same biological meaning or represents the same metabolite in all other samples. In addition, the obtained models must have the ability to make predictions of, e. g. related and independent samples characterized accordingly to the model samples. This method involves the construction of a representative data set, including automatic peak detection, alignment, setting of retention time windows, summing in the chromatographic dimension and data compression by means of alternating regression, where the relevant metabolic variation is retained for further modelling using multivariate analysis. This approach has the advantage of allowing the comparison of large numbers of samples based on their LC/MS metabolic profiles, but also of creating a means for the interpretation of the investigated biological system. This includes finding relevant systematic patterns among samples, identifying influential variables, verifying the findings in the raw data, and finally using the models for predictions. The presented strategy was here applied to a population study using urine samples from two cohorts, Shanxi (People's Republic of China) and Honolulu ( USA). The results showed that the evaluation of the extracted information data using partial least square discriminant analysis (PLS-DA) provided a robust, predictive and transparent model for the metabolic differences between the two populations. The presented findings suggest that this is a general approach for data handling, analysis, and evaluation of large metabolic LC/MS data sets.

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