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  • 101.
    Pinto, Rui Climaco
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gerber, Lorenz
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Eliasson, Mattias
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Sundberg, Björn
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Strategy for minimizing between-study variation of large-scale phenotypic experiments using multivariate analysis2012Inngår i: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 84, nr 20, s. 8675-8681Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We have developed a multistep strategy that integrates data from several large-scale experiments that suffer from systematic between-experiment variation. This strategy removes such variation that would otherwise mask differences of interest. It was applied to the evaluation of wood chemical analysis of 736 hybrid aspen trees: wild-type controls and transgenic trees potentially involved in wood formation. The trees were grown in four different greenhouse experiments imposing significant variation between experiments. Pyrolysis coupled to gas chromatography/mass spectrometry (Py-GC/MS) was used as a high throughput-screening platform for fingerprinting of wood chemotype. Our proposed strategy includes quality control, outlier detection, gene specific classification, and consensus analysis. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to generate the consensus chemotype profiles for each transgenic line. These were thereafter compiled to generate a global dataset. Multivariate analysis and cluster analysis techniques revealed a drastic reduction in between-experiment variation that enabled a global analysis of all transgenic lines from the four independent experiments. Information from in-depth analysis of specific transgenic lines and independent peak identification validated our proposed strategy.

  • 102.
    Pinto, Rui Climaco
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gottfries, Johan
    Department of Chemistry and Molecular Biology, Gothenburg University.
    Advantages of orthogonal inspection in chemometrics2012Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 26, nr 6, s. 231-235Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The demand for chemometrics tools and concepts to study complex problems in modern biology and medicine has prompted chemometricians to shift their focus away from a traditional emphasis on model predictive capacity toward optimizing information exchange via model interpretation for biological validation. The interpretation of projection-based latent variable models is not straightforward because of its confounding of different systematic variations in the model components. Over the last 15 years, this has spurred the development of orthogonal-based methods that are capable of separating the correlated variation (to Y) from the noncorrelated (orthogonal to Y) variations in a single model. Here, we aim to provide a conceptual explanation of the advantages of orthogonal variation inspection in the context of Partial Least Squares (PLS) in multivariate classification and calibration. We propose that by inspecting the orthogonal variation, both model interpretation and information quality are improved by enhancement of the resulting level of knowledge. Although the predictive capacity of PLS using orthogonal methods may be identical to that of PLS alone, the combined result can be superior when it comes to the model interpretation. By discussing theory and examples, several new advantages revealed by inspection of orthogonal variation are highlighted.

  • 103. Rahnama, Leila
    et al.
    Peterson, Gunnel
    Kazemnejad, Anoshirvan
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Peolsson, Anneli
    Alterations in the Mechanical Response of Deep Dorsal Neck Muscles in Individuals Experiencing Whiplash-Associated Disorders Compared to Healthy Controls: An Ultrasound Study2018Inngår i: American Journal of Physical Medicine & Rehabilitation, ISSN 0894-9115, E-ISSN 1537-7385, Vol. 97, nr 2, s. 75-82Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objective: The aim of this study was to investigate and compare the mechanical responses of dorsal neck muscles in individuals with whiplash-associated disorders (WAD) versus healthy individuals.

    Design: This study included 36 individuals with WAD (26 women and 10 men) and 36 healthy controls (26 women and 10 men). Ultrasound imaging with speckle tracking was used to measure deformation and deformation rate in five dorsal neck muscles during a neck extension task.

    Results: Compared with controls, individuals with WAD showed higher deformations of the semispinalis cervicis (P = 0.02) and multifidus (P = 0.002) muscles and higher deformation rates (P = 0.03 and 0.0001, respectively). Among individuals with WAD, multifidus deformation and deformation rate were significantly associated with pain, disability, and fatigue (r = 0.31-0.46, P = 0.0001-0.01).

    Conclusions: These findings indicate that the mechanical responses of the deep dorsal neck muscles differ between individuals with WAD and healthy controls, possibly reflecting that these muscles use altered strategies while performing a neck extension task. This finding provides new insight into neck muscles pathology in patients with chronic WAD and may help improve rehabilitation programs.

  • 104. Rantalainen, Mattias
    et al.
    Bylesjö, Max
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Cloarec, Olivier
    Nicholson, Jeremy K
    Holmes, Elaine
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Kernel-based orthogonal projections to latent structures (K-OPLS)2007Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 21, nr 7-9, s. 379-385Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The orthogonal projections to latent structures (OPLS) method has been successfully applied in various chemical and biological systems for modeling and interpretation of linear relationships between a descriptor matrix and response matrix. A kernel-based reformulation of the original OPLS algorithm is presented where the kernel Gram matrix is utilized as a replacement for the descriptor matrix. This enables usage of the kernel trick to efficiently transform the data into a higher-dimensional feature space where predictive and response-orthogonal components are calculated. This strategy has the capacity to improve predictive performance considerably in situations where strong non-linear relationships exist between descriptor and response variables while retaining the OPLS model framework. We put particular focus on describing properties of the rearranged algorithm in relation to the original OPLS algorithm. Four separate problems, two simulated and two real spectroscopic data sets, are employed to illustrate how the algorithm enables separate modeling of predictive and response-orthogonal variation in the feature space. This separation can be highly beneficial for model interpretation purposes while providing a flexible framework for supervised regression.

  • 105. Rantalainen, Mattias
    et al.
    Cloarec, Olivier
    Beckonert, Olaf
    Wilson, I. D.
    Jackson, David
    Tonge, Robert
    Rowlinson, Rachel
    Rayner, Steve
    Nickson, Janice
    Wilkinson, Robert W.
    Mills, Jonathan D.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Nicholson, Jeremy K.
    Holmes, Elaine
    Statistically Integrated Metabonomic-Proteomic Studies on a Human Prostate Cancer Xenograft Model in Mice2006Inngår i: Journal of Proteome Research, Vol. 10, s. 2642-55Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A novel statistically integrated proteometabonomic method has been developed and applied to a human tumor xenograft mouse model of prostate cancer. Parallel 2D-DIGE proteomic and 1H NMR metabolic profile data were collected on blood plasma from mice implanted with a prostate cancer (PC-3) xenograft and from matched control animals. To interpret the xenograft-induced differences in plasma profiles, multivariate statistical algorithms including orthogonal projection to latent structure (OPLS) were applied to generate models characterizing the disease profile. Two approaches to integrating metabonomic data matrices are presented based on OPLS algorithms to provide a framework for generating models relating to the specific and common sources of variation in the metabolite concentrations and protein abundances that can be directly related to the disease model. Multiple correlations between metabolites and proteins were found, including associations between serotransferrin precursor and both tyrosine and 3-D-hydroxybutyrate. Additionally, a correlation between decreased concentration of tyrosine and increased presence of gelsolin was also observed. This approach can provide enhanced recovery of combination candidate biomarkers across multi-omic platforms, thus, enhancing understanding of in vivo model systems studied by multiple omic technologies

  • 106. Rantalainen, Mattias
    et al.
    Cloarec, Olivier
    Ebbels, Timothy
    Lundstedt, Torbjörn
    Nicholson, Jeremy
    Holmes, Elaine
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Piecewise multivariate modelling of sequential metabolic profiling data2008Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 9, artikkel-id 105Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints.

    Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted.

    Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

  • 107. Reinke, Stacey N.
    et al.
    Galindo-Prieto, Beatriz
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Skotare, Tomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Broadhurst, David I.
    Singhania, Akul
    Horowitz, Daniel
    Djukanovic, Ratko
    Hinks, Timothy S. C.
    Geladi, Paul
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Wheelock, Craig E.
    OnPLS-Based Multi-Block Data Integration: A Multivariate Approach to Interrogating Biological Interactions in Asthma2018Inngår i: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 90, nr 22, s. 13400-13408Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Integration of multiomics data remains a key challenge in fulfilling the potential of comprehensive systems biology. Multiple-block orthogonal projections to latent structures (OnPLS) is a Multi projection method that simultaneously models multiple data matrices, reducing feature space without relying on a priori biological knowledge. In order to improve the interpretability of OnPLS models, the associated multi-block variable influence on orthogonal projections (MB-VIOP) method is used to identify variables with the highest contribution to the model. This study combined OnPLS and MB-VIOP with interactive visualization methods to interrogate an exemplar multiomics study, using a subset of 22 individuals from an asthma cohort. Joint data structure in six data blocks was assessed: transcriptomics; metabolomics; targeted assays for sphingolipids, oxylipins, and fatty acids; and a clinical block including lung function, immune cell differentials, and cytokines. The model identified seven components, two of which had contributions from all blocks (globally joint structure) and five that had contributions from two to five blocks (locally joint structure). Components 1 and 2 were the most informative, identifying differences between healthy controls and asthmatics and a disease sex interaction, respectively. The interactions between features selected by MB-VIOP were visualized using chord plots, yielding putative novel insights into asthma disease pathogenesis, the effects of asthma treatment, and biological roles of uncharacterized genes. For example, the gene ATP6 V1G1, which has been implicated in osteoporosis, correlated with metabolites that are dysregulated by inhaled corticoid steroids (ICS), providing insight into the mechanisms underlying bone density loss in asthma patients taking ICS. These results show the potential for OnPLS, combined with MB-VIOP variable selection and interaction visualization techniques, to generate hypotheses from multiomics studies and inform biology.

  • 108.
    Rentoft, Matilda
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lindell, Kristoffer
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Tran, Phong
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Chabes, Anna Lena
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Buckland, Robert
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Watt, Danielle L.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Marjavaara, Lisette
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Nilsson, Anna Karin
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Melin, Beatrice
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Johansson, Erik
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Chabes, Andrei
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Heterozygous colon cancer-associated mutations of SAMHD1 have functional significance2016Inngår i: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 113, nr 17, s. 4723-4728Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Even small variations in dNTP concentrations decrease DNA replication fidelity, and this observation prompted us to analyze genomic cancer data for mutations in enzymes involved in dNTP metabolism. We found that sterile alpha motif and histidine-aspartate domain-containing protein 1 (SAMHD1), a deoxyribonucleoside triphosphate triphosphohydrolase that decreases dNTP pools, is frequently mutated in colon cancers, that these mutations negatively affect SAMHD1 activity, and that severalSAMHD1mutations are found in tumors with defective mismatch repair. We show that minor changes in dNTP pools in combination with inactivated mismatch repair dramatically increase mutation rates. Determination of dNTP pools in mouse embryos revealed that inactivation of oneSAMHD1allele is sufficient to elevate dNTP pools. These observations suggest that heterozygous cancer-associatedSAMHD1mutations increase mutation rates in cancer cells.

  • 109.
    Rentoft, Matilda
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Svensson, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Sjödin, Andreas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, SE Umeå, Sweden.
    Olason, Pall I.
    Sjöström, Olle
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi. Unit of research, education and development, Region Jämtland Härjedalen, SE Östersund, Sweden.
    Nylander, Carin
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Osterman, Pia
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Sjögren, Rickard
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Netotea, Sergiu
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Science for Life Laboratory, Department of Biology and Biological Engineering, Chalmers University of Technology, SE Göteborg, Sweden.
    Wibom, Carl
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Cederquist, Kristina
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk biovetenskap, Medicinsk och klinisk genetik.
    Chabes, Andrei
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Melin, Beatrice S.
    Umeå universitet, Medicinska fakulteten, Institutionen för strålningsvetenskaper, Onkologi.
    Johansson, Erik
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    A geographically matched control population efficiently limits the number of candidate disease-causing variants in an unbiased whole-genome analysis2019Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, nr 3, artikkel-id e0213350Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e.g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide.

  • 110.
    Rui Climaco, Pinto
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Stenlund, Hans
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Hertzberg, Magnus
    Lundstedt, Torbjörn
    Johansson, Erik
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Design of experiments on 135 cloned poplar trees to map environmental influence in greenhouse2011Inngår i: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 685, nr 2, s. 127-131Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    To find and ascertain phenotypic differences, minimal variation between biological replicates is always desired. Variation between the replicates can originate from genetic transformation but also from environmental effects in the greenhouse. Design of experiments (DoE) has been used in field trials for many years and proven its value but is underused within functional genomics including greenhouse experiments. We propose a strategy to estimate the effect of environmental factors with the ultimate goal of minimizing variation between biological replicates, based on DoE. DoE can be analyzed in many ways. We present a graphical solution together with solutions based on classical statistics as well as the newly developed OPLS methodology.In this study, we used DoE to evaluate the influence of plant specific factors (plant size, shoot type, plant quality, amount of fertilizer) and rotation of plant positions on height and section area of 135 cloned wild type poplar trees grown in the greenhouse. Statistical analysis revealed that plant position was the main contributor to variability among biological replicates and applying a plant rotation scheme could reduce this variation.

  • 111. Shi, Leming
    et al.
    Campbell, Gregory
    Jones, Wendell D
    Campagne, Fabien
    Wen, Zhining
    Walker, Stephen J
    Su, Zhenqiang
    Chu, Tzu-Ming
    Goodsaid, Federico M
    Pusztai, Lajos
    Shaughnessy, John D
    Oberthuer, André
    Thomas, Russell S
    Paules, Richard S
    Fielden, Mark
    Barlogie, Bart
    Chen, Weijie
    Du, Pan
    Fischer, Matthias
    Furlanello, Cesare
    Gallas, Brandon D
    Ge, Xijin
    Megherbi, Dalila B
    Symmans, W Fraser
    Wang, May D
    Zhang, John
    Bitter, Hans
    Brors, Benedikt
    Bushel, Pierre R
    Bylesjö, Max
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Chen, Minjun
    Cheng, Jie
    Cheng, Jing
    Chou, Jeff
    Davison, Timothy S
    Delorenzi, Mauro
    Deng, Youping
    Devanarayan, Viswanath
    Dix, David J
    Dopazo, Joaquin
    Dorff, Kevin C
    Elloumi, Fathi
    Fan, Jianqing
    Fan, Shicai
    Fan, Xiaohui
    Fang, Hong
    Gonzaludo, Nina
    Hess, Kenneth R
    Hong, Huixiao
    Huan, Jun
    Irizarry, Rafael A
    Judson, Richard
    Juraeva, Dilafruz
    Lababidi, Samir
    Lambert, Christophe G
    Li, Li
    Li, Yanen
    Li, Zhen
    Lin, Simon M
    Liu, Guozhen
    Lobenhofer, Edward K
    Luo, Jun
    Luo, Wen
    McCall, Matthew N
    Nikolsky, Yuri
    Pennello, Gene A
    Perkins, Roger G
    Philip, Reena
    Popovici, Vlad
    Price, Nathan D
    Qian, Feng
    Scherer, Andreas
    Shi, Tieliu
    Shi, Weiwei
    Sung, Jaeyun
    Thierry-Mieg, Danielle
    Thierry-Mieg, Jean
    Thodima, Venkata
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Vishnuvajjala, Lakshmi
    Wang, Sue Jane
    Wu, Jianping
    Wu, Yichao
    Xie, Qian
    Yousef, Waleed A
    Zhang, Liang
    Zhang, Xuegong
    Zhong, Sheng
    Zhou, Yiming
    Zhu, Sheng
    Arasappan, Dhivya
    Bao, Wenjun
    Lucas, Anne Bergstrom
    Berthold, Frank
    Brennan, Richard J
    Buness, Andreas
    Catalano, Jennifer G
    Chang, Chang
    Chen, Rong
    Cheng, Yiyu
    Cui, Jian
    Czika, Wendy
    Demichelis, Francesca
    Deng, Xutao
    Dosymbekov, Damir
    Eils, Roland
    Feng, Yang
    Fostel, Jennifer
    Fulmer-Smentek, Stephanie
    Fuscoe, James C
    Gatto, Laurent
    Ge, Weigong
    Goldstein, Darlene R
    Guo, Li
    Halbert, Donald N
    Han, Jing
    Harris, Stephen C
    Hatzis, Christos
    Herman, Damir
    Huang, Jianping
    Jensen, Roderick V
    Jiang, Rui
    Johnson, Charles D
    Jurman, Giuseppe
    Kahlert, Yvonne
    Khuder, Sadik A
    Kohl, Matthias
    Li, Jianying
    Li, Li
    Li, Menglong
    Li, Quan-Zhen
    Li, Shao
    Li, Zhiguang
    Liu, Jie
    Liu, Ying
    Liu, Zhichao
    Meng, Lu
    Madera, Manuel
    Martinez-Murillo, Francisco
    Medina, Ignacio
    Meehan, Joseph
    Miclaus, Kelci
    Moffitt, Richard A
    Montaner, David
    Mukherjee, Piali
    Mulligan, George J
    Neville, Padraic
    Nikolskaya, Tatiana
    Ning, Baitang
    Page, Grier P
    Parker, Joel
    Parry, R Mitchell
    Peng, Xuejun
    Peterson, Ron L
    Phan, John H
    Quanz, Brian
    Ren, Yi
    Riccadonna, Samantha
    Roter, Alan H
    Samuelson, Frank W
    Schumacher, Martin M
    Shambaugh, Joseph D
    Shi, Qiang
    Shippy, Richard
    Si, Shengzhu
    Smalter, Aaron
    Sotiriou, Christos
    Soukup, Mat
    Staedtler, Frank
    Steiner, Guido
    Stokes, Todd H
    Sun, Qinglan
    Tan, Pei-Yi
    Tang, Rong
    Tezak, Zivana
    Thorn, Brett
    Tsyganova, Marina
    Turpaz, Yaron
    Vega, Silvia C
    Visintainer, Roberto
    von Frese, Juergen
    Wang, Charles
    Wang, Eric
    Wang, Junwei
    Wang, Wei
    Westermann, Frank
    Willey, James C
    Woods, Matthew
    Wu, Shujian
    Xiao, Nianqing
    Xu, Joshua
    Xu, Lei
    Yang, Lun
    Zeng, Xiao
    Zhang, Jialu
    Zhang, Li
    Zhang, Min
    Zhao, Chen
    Puri, Raj K
    Scherf, Uwe
    Tong, Weida
    Wolfinger, Russell D
    The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models2010Inngår i: Nature Biotechnology, ISSN 1087-0156, E-ISSN 1546-1696, Vol. 28, nr 8, s. 827-838Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

  • 112.
    Sjödin, Andreas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Wissel, Kirsten
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Bylesjö, Max
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Jansson, Stefan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Global expression profiling in leaves of free-growing aspen2008Inngår i: BMC Plant Biology, ISSN 1471-2229, E-ISSN 1471-2229, Vol. 8, s. 61-Artikkel i tidsskrift (Annet vitenskapelig)
    Abstract [en]

    Background

    Genomic studies are routinely performed on young plants in controlled environments which is very different from natural conditions. In reality plants in temperate countries are exposed to large fluctuations in environmental conditions, in the case of perennials over several years. We have studied gene expression in leaves of a free-growing aspen (Populus tremula) throughout multiple growing seasons.

    Results

    We show that gene expression during the first month of leaf development was largely determined by a developmental program although leaf expansion, chlorophyll accumulation and the speed of progression through this program was regulated by the temperature. We were also able to define "transcriptional signatures" for four different substages of leaf development. In mature leaves, weather factors were important for gene regulation.

    Conclusions

    This study shows that multivariate methods together with high throughput transcriptional methods in the field can provide additional, novel information as to plant status under changing environmental conditions that is impossible to mimic in laboratory conditions. We have generated a dataset that could be used to e.g. identify marker genes for certain developmental stages or treatments, as well as to assess natural variation in gene expression.

  • 113.
    Sjögren, Rickard
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Stridh, Kjell
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Skotare, Tomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics, Umeå, Sweden.
    Multivariate patent analysis: using chemometrics to analyze collections of chemical and pharmaceutical patents2018Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, artikkel-id e3041Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Abstract Patents are an important source of technological knowledge, but the amount of existing patents is vast and quickly growing. This makes development of tools and methodologies for quickly revealing patterns in patent collections important. In this paper, we describe how structured chemometric principles of multivariate data analysis can be applied in the context of text analysis in a novel combination with common machine learning preprocessing methodologies. We demonstrate our methodology in 2 case studies. Using principal component analysis (PCA) on a collection of 12338 patent abstracts from 25 companies in big pharma revealed sub-fields which the companies are active in. Using PCA on a smaller collection of patents retrieved by searching for a specific term proved useful to quickly understand how patent classifications relate to the search term. By using orthogonal projections to latent structures (O-PLS) on patent classification schemes, we were able to separate patents on a more detailed level than using PCA. Lastly, we performed multi-block modeling using OnPLS on bag-of-words representations of abstracts, claims, and detailed descriptions, respectively, showing that semantic variation relating to patent classification is consistent across multiple text blocks, represented as globally joint variation. We conclude that using machine learning to transform unstructured data into structured data provide a good preprocessing tool for subsequent chemometric multivariate data analysis and provides an easily interpretable and novel workflow to understand large collections of patents. We demonstrate this on collections of chemical and pharmaceutical patents.

  • 114.
    Skotare, Tomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Nilsson, David
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Xiong, Shaojun
    Geladi, Paul
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Corporate Research, Sartorius AG, 37079 Göttingen, Germany.
    Joint and unique multiblock analysis for integration and calibration transfer of NIR instruments2019Inngår i: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 91, nr 5, s. 3516-3524Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the present paper, we introduce an end-to-end workflow called joint and unique multiblock analysis (JUMBA), which allows multiple sources of data to be analyzed simultaneously to better understand how they complement each other. In near-infrared (NIR) spectroscopy, calibration models between NIR spectra and responses are used to replace wet-chemistry methods, and the models tend to be instrument-specific. Calibration-transfer techniques are used for standardization of NIR-instrumentation, enabling the use of one model on several instruments. The current paper investigates both the similarities and differences among a variety of NIR instruments using JUMBA. We demonstrate JUMBA on both a previously unpublished data set in which five NIR instruments measured mushroom substrate and a publicly available data set measured on corn samples. We found that NIR spectra from different instrumentation largely shared the same underlying structures, an insight we took advantage of to perform calibration transfer. The proposed JUMBA transfer displayed excellent calibration-transfer performance across the two analyzed data sets and outperformed existing methods in terms of both prediction accuracy and stability. When applied to a multi-instrument environment, JUMBA transfer can integrate all instruments in the same model and will ensure higher consistency among them compared with existing calibration-transfer methods.

  • 115.
    Skotare, Tomas
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Sjögren, Rickard
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Surowiec, Izabella
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Nilsson, David
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics, 907 36 Umeå, Sweden.
    Visualization of descriptive multiblock analysis2018Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128XArtikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Abstract Understanding and making the most of complex data collected from multiple sources is a challenging task. Data integration is the procedure of describing the main features in multiple data blocks, and several methods for multiblock analysis have been previously developed, including OnPLS and JIVE. One of the main challenges is how to visualize and interpret the results of multiblock analyses because of the increased model complexity and sheer size of data. In this paper, we present novel visualization tools that simplify interpretation and overview of multiblock analysis. We introduce a correlation matrix plot that provides an overview of the relationships between blocks found by multiblock models. We also present a multiblock scatter plot, a metadata correlation plot, and a variation distribution plot, that simplify the interpretation of multiblock models. We demonstrate our visualizations on an industrial case study in vibration spectroscopy (NIR, UV, and Raman datasets) as well as a multiomics integration study (transcript, metabolite, and protein datasets). We conclude that our visualizations provide useful tools to harness the complexity of multiblock analysis and enable better understanding of the investigated system.

  • 116.
    Souihi, Nabil
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Dumarey, Melanie
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Wikström, Håkan
    Tajarobi, Pirjo
    Fransson, Magnus
    Svensson, Olof
    Josefson, Mats
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    A Quality by Design approach to investigate the effect of mannitol and dicalcium phosphate qualities on roll compaction2013Inngår i: International Journal of Pharmaceutics, ISSN 0378-5173, E-ISSN 1873-3476, Vol. 447, nr 1-2, s. 47-61Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Roll compaction is a continuous process for solid dosage form manufacturing increasingly popular within pharmaceutical industry. Although roll compaction has become an established technique for dry granulation, the influence of material properties is still not fully understood. In this study, a quality by design (QbD) approach was utilized, not only to understand the influence of different qualities of mannitol and dicalcium phosphate (DCP), but also to predict critical quality attributes of the drug product based solely on the material properties of that filler. By describing each filler quality in terms of several representative physical properties, orthogonal projections to latent structures (OPLS) was used to understand and predict how those properties affected drug product intermediates as well as critical quality attributes of the final drug product. These models were then validated by predicting product attributes for filler qualities not used in the model construction. The results of this study confirmed that the tensile strength reduction, known to affect plastic materials when roll compacted, is not prominent when using brittle materials. Some qualities of these fillers actually demonstrated improved compactability following roll compaction. While direct compression qualities are frequently used for roll compacted drug products because of their excellent flowability and good compaction properties, this study revealed that granules from these qualities were more poor flowing than the corresponding powder blends, which was not seen for granules from traditional qualities. The QbD approach used in this study could be extended beyond fillers. Thus any new compound/ingredient would first be characterized and then suitable formulation characteristics could be determined in silico, without running any additional experiments.

  • 117.
    Souihi, Nabil
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Josefson, Mats
    Tajarobi, Pirjo
    Gururajan, Bindhu
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Design Space Estimation of the Roller Compaction Process2013Inngår i: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 52, nr 35, s. 12408-12419Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Roller compaction (RC) is a continuous process for solid dosage form manufacturing within the pharmaceutical industry achieving similar goals as wet granulation while avoiding liquid exposure. From a quality by design perspective, the aim of the present study was to demonstrate the applicability of statistical design of experiments (DoE) and multivariate modeling principles to identify the Design Space of a roller compaction process using a predictive risk-based approach. For this purpose, a reduced central composite face-centered (CCF) design was used to evaluate the influence of roll compaction process variables (roll force, roll speed, gap width, and screen size) on the different intermediate and final products (ribbons, granules, and tablets) obtained after roll compaction, milling, and tableting. After developing a regression model for each response, optimal settings were found which comply with the response criteria. Finally, a predictive risk based approach using Monte Carlo simulation of the factor variability and its influence on the responses was applied which fulfill the criteria for the responses in a space where there is a low risk for failure. Responses were as follows: granule throughput, ribbon porosity, granules particle size, and tablets tensile strength. The multivariate method orthogonal partial least-squares (OPLS) was used to model product dependencies between process steps e.g. granule properties with tablet properties. Those results confirmed that the tensile strength reduction, known to affect plastic materials when roll compacted, was not prominent when using brittle materials. While direct compression qualities are frequently used for roll compacted drug products because of their excellent flowability and good compaction properties, this study confirmed earlier findings that granules from these qualities were more poor flowing than the corresponding powder blend.

  • 118.
    Souihi, Nabil
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lindegren, Anders
    Eriksson, Lennart
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    OPLS in batch monitoring - Opens up new opportunities2015Inngår i: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 857, s. 28-38Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In batch statistical process control (BSPC), data from a number of "good" batches are used to model the evolution (trajectory) of the process and they also define model control limits, against which new batches may be compared. The benchmark methods used in BSPC include partial least squares (PLS) and principal component analysis (PCA). In this paper, we have used orthogonal projections to latent structures (OPLS) in BSPC and compared the results with PLS and PCA. The experimental study used was a batch hydrogenation reaction of nitrobenzene to aniline characterized by both UV spectroscopy and process data. The key idea is that OPLS is able to separate the variation in data that is correlated to the process evolution (also known as 'batch maturity index') from the variation that is uncorrelated to process evolution. This separation of different types of variations can generate different batch trajectories and hence lead to different established model control limits to detect process deviations. The results demonstrate that OPLS was able to detect all process deviations and provided a good process understanding of the root causes for these deviations. PCA and PLS on the other handwere shown to provide different interpretations for several of these process deviations, or in some cases they were unable to detect actual process deviations. Hence, the use of OPLS in BSPC can lead to better fault detection and root cause analysis as compared to existing benchmark methods and may therefore be used to complement the existing toolbox.

  • 119.
    Souihi, Nabil
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Nilsson, David
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Josefson, Mats
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Near-infrared chemical imaging (NIR-CI) on roll compacted ribbons and tablets - multivariate mapping of physical and chemical properties2015Inngår i: International Journal of Pharmaceutics, ISSN 0378-5173, E-ISSN 1873-3476, Vol. 483, nr 1-2, s. 200-211Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Near-infrared chemical imaging (NIR-CI) is an attractive technique within the pharmaceutical industry, where tools are continuously in demand to assess the quality of the intermediate and final products. The present paper demonstrates how NIR-CI in combination with multivariate methods was utilized to spatially map physical properties and content of roll compacted ribbons and tablets. Additionally, extracted textural parameters from tablet images were correlated to the design parameters of the roll compaction process as well as to the physical properties of the granules. The results established the use of NIR-CI as a complementary nondestructive tool to determine the ribbon density and map the density distribution across the width and along the length of the ribbons. For the tablets, the compaction pressure developed during compression increased with the lateral distance from the center. Therefore, NIR-CI can be an effective tool to provide information about the spatial distribution of the compaction pressures on the surface of the tablet. Moreover, low roll compaction roll force correlated to a heterogeneous type of texture in the API chemical image. Overall, texture analysis of the tablets enabled efficient investigation of the spatial variation and could be used to advance process understanding. Finally, orthogonal projections to latent structures (O2PLS) model facilitated the understanding of the interrelationships between textural features, design parameters and physical properties data by separately joint and unique variations.

  • 120.
    Souihi, Nabil
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Reynolds, Gavin
    Tajarobi, Pirjo
    Wikström, Håkan
    Haeffler, Gunnar
    Josefson, Mats
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Roll compaction process modeling: transfer between equipment and impact of process parameters2015Inngår i: International Journal of Pharmaceutics, ISSN 0378-5173, E-ISSN 1873-3476, Vol. 484, nr 1-2, s. 192-206Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this study the roll compaction of an intermediate drug load formulation was performed using horizontally and vertically force fed roll compactors. The horizontally fed roll compactor was equipped with an instrumented roll technology allowing the direct measurement of normal stress at the roll surface, while the vertically fed roll compactor was equipped with a force gauge between the roll axes. Furthermore, characterization of ribbons, granules and tablets was also performed. Ribbon porosity was primarily found to be a function of normal stress, exhibiting a quadratic relationship thereof. A similar quadratic relationship was also observed between roll force and ribbon porosity of the vertically fed roll compactor. The predicted peak pressure (Pmax) using the Johanson model was found to be higher than the measured normal stress, however, the predicted Pmax correlated well with the ribbon relative density/porosity and the majority of downstream properties of granules and tablets, demonstrating its use as a scale-independent parameter. A latent variable model was developed for both the horizontal and vertical fed roll compactors to express ribbon porosity as a function of geometric and process parameters. The model validation, performed with new data, resulted in overall good predictions. This study successfully demonstrated the scale up/transfer between two different roll compactors and revealed that the combined use of design of experiments, latent variable models and in silico predictions result in better understanding of the critical process parameters in roll compaction.

  • 121. Srivastava, Vaibhav
    et al.
    Obudulu, Ogonna
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational life science cluster (CLiC), Umeå University and Swedish University of Agricultural Sciences.
    Bygdell, Joakim
    Löfstedt, Tommy
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational life science cluster (CLiC), Umeå University.
    Rydén, Patrik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för matematik och matematisk statistik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational life science cluster (CLiC), Umeå University.
    Nilsson, Robert
    Ahnlund, Maria
    Johansson, Annika
    Jonsson, Pär
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational life science cluster (CLiC), Umeå University.
    Freyhult, Eva
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Klinisk bakteriologi. Computational life science cluster (CLiC), Umeå University.
    Qvarnström, Johanna
    Karlsson, Jan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Melzer, Michael
    Moritz, Thomas
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational life science cluster (CLiC), Umeå University.
    Hvidsten, Torgeir R
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational life science cluster (CLiC), Umeå University and Department of Chemistry, Biotechnology; Food Science, Norwegian, University of Life Sciences, Ås Norwegian, Norway.
    Wingsle, Gunnar
    OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants2013Inngår i: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 14, artikkel-id 893Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    BACKGROUND: Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to oxidative stress in Populus by integrating data from analyses of the cambial region of wild-type controls and plants expressing high-isoelectric-point superoxide dismutase (hipI-SOD) transcripts in antisense orientation showing a higher production of superoxide. The cambium, a thin cell layer, generates cells that differentiate to form either phloem or xylem and is hypothesized to be a major reason for phenotypic perturbations in the transgenic plants. Data from multiple platforms including transcriptomics (microarray analysis), proteomics (UPLC/QTOF-MS), and metabolomics (GC-TOF/MS, UPLC/MS, and UHPLC-LTQ/MS) were integrated using the most recent development of orthogonal projections to latent structures called OnPLS. OnPLS is a symmetrical multi-block method that does not depend on the order of analysis when more than two blocks are analysed. Significantly affected genes, proteins and metabolites were then visualized in painted pathway diagrams.

    RESULTS: The main categories that appear to be significantly influenced in the transgenic plants were pathways related to redox regulation, carbon metabolism and protein degradation, e.g. the glycolysis and pentose phosphate pathways (PPP). The results provide system-level information on ROS metabolism and responses to oxidative stress, and indicate that some initial responses to oxidative stress may share common pathways.

    CONCLUSION: The proposed data evaluation strategy shows an efficient way of compiling complex, multi-platform datasets to obtain significant biological information.

  • 122.
    Stenlund, Hans
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gorzsás, András
    Swedish Agricultural University.
    Persson, Per
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Sundberg, Björn
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Orthogonal Projections to Latent Structures Discriminant Analysis Modeling on in Situ FT-IR Spectral Imaging of Liver Tissue for Identifying Sources of Variability.2008Inngår i: Analytical Chemistry, ISSN 1520-6882, Vol. 80, nr 18, s. 6898-906Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this study, the orthogonal projections to latent structures discriminant analysis (OPLS-DA) method was used to assess the in situ chemical composition of two different cell types in mouse liver samples, hepatocytes and erythrocytes. High spatial resolution FT-IR microspectroscopy equipped with a focal plan array (FPA) detector is capable of simultaneously recording over 4000 spectra from 64 x 64 pixels with a maximum spatial resolution of about 5 mum x 5 mum, which allows for the differentiation of individual cells. The main benefit with OPLS-DA lies in the ability to separate predictive variation (between cell type) from variation that is uncorrelated to cell type in order to facilitate understanding of different sources of variation. OPLS-DA was able to differentiate between chemical properties and physical properties (e.g., edge effects). OPLS-DA model interpretation of the chemical features that separated the two cell types clearly highlighted proteins and lipids/bile acids. The modeled variation that was uncorrelated to cell type made up a larger portion of the total variation and displayed strong variability in the amide I region. This could be traced back to a gradient in the high intensity (high-density) areas vs the low intensity areas (close to empty areas) that as a result of normalization had an adverse effect on FT-IR spectral profiles. This highlights that OPLS-DA provides an effective solution to identify different sources of variability, both predictive and uncorrelated, and also facilitates understanding of any sampling, experimental, or preprocessing issues.

  • 123.
    Stenlund, Hans
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Johansson, Erik
    Gottfries, Johan
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Unlocking Interpretation in Near Infrared Multivariate Calibrations by Orthogonal Partial Least Squares2009Inngår i: Analytical Chemistry, Vol. 81, nr 1, s. 203-9Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Near infrared spectroscopy (NIR) was developed primarily for applications such as the quantitative determination of nutrients in the agricultural and food industries. Examples include the determination of water, protein, and fat within complex samples such as grain and milk. Because of its useful properties, NIR analysis has spread to other areas such as chemistry and pharmaceutical production. NIR spectra consist of infrared overtones and combinations thereof, making interpretation of the results complicated. It can be very difficult to assign peaks to known constituents in the sample. Thus, multivariate analysis (MVA) has been crucial in translating spectral data into information, mainly for predictive purposes. Orthogonal partial least squares (OPLS), a new MVA method, has prediction and modeling properties similar to those of other MVA techniques, e.g., partial least squares (PLS), a method with a long history of use for the analysis of NIR data. OPLS provides an intrinsic algorithmic improvement for the interpretation of NIR data. In this report, four sets of NIR data were analyzed to demonstrate the improved interpretation provided by OPLS. The first two sets included simulated data to demonstrate the overall principles; the third set comprised a statistically replicated design of experiments (DoE), to demonstrate how instrumental difference could be accurately visualized and correctly attributed to Wood’s anomaly phenomena; the fourth set was chosen to challenge the MVA by using data relating to powder mixing, a crucial step in the pharmaceutical industry prior to tabletting. Improved interpretation by OPLS was demonstrated for all four examples, as compared to alternative MVA approaches. It is expected that OPLS will be used mostly in applications where improved interpretation is crucial; one such area is process analytical technology (PAT). PAT involves fewer independent samples, i.e., batches, than would be associated with agricultural applications; in addition, the Food and Drug Administration (FDA) demands “process understanding” in PAT. Both these issues make OPLS the ideal tool for a multitude of NIR calibrations. In conclusion, OPLS leads to better interpretation of spectrometry data (e.g., NIR) and improved understanding facilitates cross-scientific communication. Such improved knowledge will decrease risk, with respect to both accuracy and precision, when using NIR for PAT applications.

  • 124.
    Stenlund, Hans
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Madsen, Rasmus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Vivi, Antonio
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Calderisi, Marco
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Lundstedt, Torbjörn
    AcureOmics AB, Umeå, Sweden.
    Tassini, Maria
    Nuclear Magnetic Resonance Centre of Siena University, Siena, Italy.
    Carmellini, Mario
    Department of Surgery and Bioengineering, University of Siena, Siena, Italy.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Monitoring kidney-transplant patients using metabolomics and dynamic modeling2009Inngår i: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 98, nr 1, s. 45-50Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A kidney transplant provides the only hope for a normal life for patients with end-stage renal disease, i.e., kidney failure. Unfortunately, the lack of available organs leaves some patients on the waiting list for years. In addition, the post-transplant treatment is extremely important for the final outcome of the surgery, since immune responses, drug toxicity and other complications pose a real and present threat to the patient. In this article, we describe a novel strategy for monitoring kidney transplanted patients for immune responses and adverse drug effects in their early recovery. Nineteen patients were followed for two weeks after renal transplantation, two of them experienced problems related to kidney function, both of whom were correctly identified by means of nuclear magnetic resonance spectroscopic analysis of urine samples and multivariate data analysis.

  • 125.
    Street, Nathaniel Robert
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Sjödin, Andreas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik.
    Bylesjö, Max
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gustafsson, Petter
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Jansson, Stefan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    A cross-species transcriptomics approach to identify genes involved in leaf development2008Inngår i: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 9, nr 1, s. 539-Artikkel i tidsskrift (Annet (populærvitenskap, debatt, mm))
    Abstract [en]

    Background

    We have made use of publicly available gene expression data to identify transcription factors and transcriptional modules (regulons) associated with leaf development in Populus. Different tissue types were compared to identify genes informative in the discrimination of leaf and non-leaf tissues. Transcriptional modules within this set of genes were identified in a much wider set of microarray data collected from leaves in a number of developmental, biotic, abiotic and transgenic experiments.

    Results

    Transcription factors that were over represented in leaf EST libraries and that were useful for discriminating leaves from other tissues were identified, revealing that the C2C2-YABBY, CCAAT-HAP3 and 5, MYB, and ZF-HD families are particularly important in leaves. The expression of transcriptional modules and transcription factors was examined across a number of experiments to select those that were particularly active during the early stages of leaf development. Two transcription factors were found to collocate to previously published Quantitative Trait Loci (QTL) for leaf length. We also found that miRNA family 396 may be important in the control of leaf development, with three members of the family collocating with clusters of leaf development QTL.

    Conclusion

    This work provides a set of candidate genes involved in the control and processes of leaf development. This resource can be used for a wide variety of purposes such as informing the selection of candidate genes for association mapping or for the selection of targets for reverse genetics studies to further understanding of the genetic control of leaf size and shape.

  • 126.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gjesdal, Clara Gram
    Jonsson, Grete
    Norheim, Katrine Braekke
    Lundstedt, Torbjorn
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Omdal, Roald
    Metabolomics study of fatigue in patients with rheumatoid arthritis na < ve to biological treatment2016Inngår i: Rheumatology International, ISSN 0172-8172, E-ISSN 1437-160X, Vol. 36, nr 5, s. 703-711Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Fatigue occurs in all chronic inflammatory diseases, in cancer, and in some neurological conditions. Patients often regard fatigue as one of their most debilitating problems, but currently there is no established treatment and the mechanisms that lead to and regulate fatigue are incompletely understood. Our objective was to more completely understand the physiology of this phenomenon. Twenty-four patients with rheumatoid arthritis (RA) na < ve to treatment with biological drugs were enrolled for the study. Fatigue was measured with a fatigue visual analogue scale (fVAS). Ethylenediaminetetraacetic acid (EDTA) plasma samples were subjected to gas chromatography-time-of-flight mass spectrometry (GC/MS-TOF)-based metabolite profiling. Obtained metabolite data were evaluated by multivariate data analysis with orthogonal projections to latent structures (OPLS) method to pinpoint metabolic changes related to fatigue severity. A significant multivariate OPLS model was obtained between the fVAS scores and the measured metabolic levels. Increasing fatigue scores were associated with a metabolic pattern characterized by down-regulation of metabolites from the urea cycle, fatty acids, tocopherols, aromatic amino acids, and hypoxanthine. Uric acid levels were increased. Apart from fatigue, we found no other disease-related variables that might be responsible for these changes. Our MS-based metabolomic approach demonstrated strong associations between fatigue and several biochemical patterns related to oxidative stress.

  • 127.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gouveia-Figueira, Sandra
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Orikiiriza, Judy
    Lindquist, Elisabeth
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Bonde, Mari
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Magambo, Jimmy
    Muhinda, Charles
    Bergström, Sven
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR).
    Normark, Johan
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Infektionssjukdomar.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    The oxylipin and endocannabidome responses in acute phase Plasmodium falciparum malaria in children2017Inngår i: Malaria Journal, ISSN 1475-2875, E-ISSN 1475-2875, Vol. 16, artikkel-id 358Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Oxylipins and endocannabinoids are low molecular weight bioactive lipids that are crucial for initiation and resolution of inflammation during microbial infections. Metabolic complications in malaria are recognized contributors to severe and fatal malaria, but the impact of malaria infection on the production of small lipid derived signalling molecules is unknown. Knowledge of immunoregulatory patterns of these molecules in malaria is of great value for better understanding of the disease and improvement of treatment regimes, since the action of these classes of molecules is directly connected to the inflammatory response of the organism.

    Methods: Detection of oxylipins and endocannabinoids from plasma samples from forty children with uncomplicated and severe malaria as well as twenty controls was done after solid phase extraction followed by chromatography mass spectrometry analysis. The stable isotope dilution method was used for compound quantification. Data analysis was done with multivariate (principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA (R)) and univariate approaches (receiver operating characteristic (ROC) curves, t tests, correlation analysis).

    Results: Forty different oxylipin and thirteen endocannabinoid metabolites were detected in the studied samples, with one oxylipin (thromboxane B2, TXB2) in significantly lower levels and four endocannabinoids (OEA, PEA, DEA and EPEA) at significantly higher levels in infected individuals as compared to controls according to t test analysis with Bonferroni correction. Three oxylipins (13-HODE, 9-HODE and 13-oxo-ODE) were higher in severe compared to uncomplicated malaria cases according to the results from multivariate analysis. Observed changes in oxylipin levels can be connected to activation of cytochrome P450 (CYP) and 5-lipoxygenase (5-LOX) metabolic pathways in malaria infected individuals compared to controls, and related to increased levels of all linoleic acid oxylipins in severe patients compared to uncomplicated ones. The endocannabinoids were extremely responsive to malaria infection with majority of this class of molecules found at higher levels in infected individuals compared to controls.

    Conclusions: It was possible to detect oxylipin and endocannabinoid molecules that can be potential biomarkers for differentiation between malaria infected individuals and controls and between different classes of malaria. Metabolic pathways that could be targeted towards an adjunctive therapy in the treatment of malaria were also pinpointed.

  • 128.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Johansson, Erik
    Stenlund, Hans
    Rantapää-Dahlqvist, Solbritt
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Reumatologi.
    Bergström, Sven
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Normark, Johan
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics, Umeå, Sweden.
    Quantification of run order effect on chromatography: mass spectrometry profiling data2018Inngår i: Journal of Chromatography A, ISSN 0021-9673, E-ISSN 1873-3778, Vol. 1568, s. 229-234Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Chromatographic systems coupled with mass spectrometry detection are widely used in biological studies investigating how levels of biomolecules respond to different internal and external stimuli. Such changes are normally expected to be of low magnitude and therefore all experimental factors that can influence the analysis need to be understood and minimized. Run order effect is commonly observed and constitutes a major challenge in chromatography-mass spectrometry based profiling studies that needs to be addressed before the biological evaluation of measured data is made. So far there is no established consensus, metric or method that quickly estimates the size of this effect. In this paper we demonstrate how orthogonal projections to latent structures (OPLS®) can be used for objective quantification of the run order effect in profiling studies. The quantification metric is expressed as the amount of variation in the experimental data that is correlated to the run order. One of the primary advantages with this approach is that it provides a fast way of quantifying run-order effect for all detected features, not only internal standards. Results obtained from quantification of run order effect as provided by the OPLS can be used in the evaluation of data normalization, support the optimization of analytical protocols and identification of compounds highly influenced by instrumental drift. The application of OPLS for quantification of run order is demonstrated on experimental data from plasma profiling performed on three analytical platforms: GCMS metabolomics, LCMS metabolomics and LCMS lipidomics.

  • 129.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Johansson, Erik
    Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Torell, Frida
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Idborg, Helena
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Gunnarsson, Iva
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Svenungsson, Elisabet
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Jakobsson, Per-Johan
    Rheumatology Unit, Department of Medicine, Solna, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics AB, Umeå, Sweden.
    Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics2017Inngår i: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, nr 10, artikkel-id 114Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput 'omics' technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

    Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

    Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC-TOFMS). For each batch OPLS-DA (R) was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

    Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

    Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

  • 130.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Karimpour, Masoumeh
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gouveia-Figueira, Sandra
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Wu, Junfang
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Unosson, Jon
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Bosson, Jenny A.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Blomberg, Anders
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Pourazar, Jamshid
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Sandström, Thomas
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Behndig, Annelie F.
    Umeå universitet, Medicinska fakulteten, Institutionen för folkhälsa och klinisk medicin, Medicin.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Nording, Malin L.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Multi-platform metabolomics assays for human lung lavage fluids in an air pollution exposure study2016Inngår i: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 408, nr 17, s. 4751-4764Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Metabolomics protocols are used to comprehensively characterize the metabolite content of biological samples by exploiting cutting-edge analytical platforms, such as gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS) assays, as well as nuclear magnetic resonance (NMR) assays. We have developed novel sample preparation procedures combined with GC-MS, LC-MS, and NMR metabolomics profiling for analyzing bronchial wash (BW) and bronchoalveolar lavage (BAL) fluid from 15 healthy volunteers following exposure to biodiesel exhaust and filtered air. Our aim was to investigate the responsiveness of metabolite profiles in the human lung to air pollution exposure derived from combustion of biofuels, such as rapeseed methyl ester biodiesel, which are increasingly being promoted as alternatives to conventional fossil fuels. Our multi-platform approach enabled us to detect the greatest number of unique metabolites yet reported in BW and BAL fluid (82 in total). All of the metabolomics assays indicated that the metabolite profiles of the BW and BAL fluids differed appreciably, with 46 metabolites showing significantly different levels in the corresponding lung compartments. Furthermore, the GC-MS assay revealed an effect of biodiesel exhaust exposure on the levels of 1-monostearylglycerol, sucrose, inosine, nonanoic acid, and ethanolamine (in BAL) and pentadecanoic acid (in BW), whereas the LC-MS assay indicated a shift in the levels of niacinamide (in BAL). The NMR assay only identified lactic acid (in BW) as being responsive to biodiesel exhaust exposure. Our findings demonstrate that the proposed multi-platform approach is useful for wide metabolomics screening of BW and BAL fluids and can facilitate elucidation of metabolites responsive to biodiesel exhaust exposure.

  • 131.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Orikiiriza, Judy
    Karlsson, Elisabeth
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Nelson, Maria
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Bonde, Mari
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Kyamanwa, Patrick
    Karenzi, Ben
    Bergström, Sven
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS).
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Normark, Johan
    Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Infektionssjukdomar. nfectious Diseases Institute, School of Medicine and Health Sciences, Makerere University, Uganda.
    Metabolic signature profiling as a diagnostic and prognostic tool in paediatric Plasmodium falciparum malaria2015Inngår i: Open Forum Infectious Diseases, ISSN 2328-8957, Vol. 2, nr 2Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Accuracy in malaria diagnosis and staging is vital in order to reduce mortality and post infectious sequelae. Herein we present a metabolomics approach to diagnostic staging of malaria infection, specifically Plasmodium falciparum infection in children. Methods: A group of 421 patients between six months and six years of age with mild and severe states of malaria with age-matched controls were included in the study, 107, 192 and 122 individuals respectively. A multivariate design was used as basis for representative selection of twenty patients in each category. Patient plasma was subjected to Gas Chromatography-Mass Spectrometry analysis and a full metabolite profile was produced from each patient. In addition, a proof-of-concept model was tested in a Plasmodium berghei in-vivo model where metabolic profiles were discernible over time of infection. Results: A two-component principal component analysis (PCA) revealed that the patients could be separated into disease categories according to metabolite profiles, independently of any clinical information. Furthermore, two sub-groups could be identified in the mild malaria cohort who we believe represent patients with divergent prognoses. Conclusion: Metabolite signature profiling could be used both for decision support in disease staging and prognostication.

  • 132.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.
    Skotare, Tomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Sjögren, Rickard
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.
    Gouveia-Figueira, Sandra C.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Orikiiriza, Judy Tatwan
    Bergström, Sven
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Normark, Johan
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten).
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Sartorius Stedim Data Analytics, Tvistevägen 48, 907 36 Umeå, Sweden.
    Joint and unique multiblock analysis of biological data: multiomics malaria study2019Inngår i: Faraday discussions (Online), ISSN 1359-6640, E-ISSN 1364-5498, Vol. 218, s. 268-283Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Modern profiling technologies enable obtaining large amounts of data which can be later used for comprehensive understanding of the studied system. Proper evaluation of such data is challenging, and cannot be faced by bare analysis of separate datasets. Integrated approaches are necessary, because only data integration allows finding correlation trends common for all studied data sets and revealing hidden structures not known a priori. This improves understanding and interpretation of the complex systems. Joint and Unique MultiBlock Analysis (JUMBA) is an analysis method based on the OnPLS-algorithm that decomposes a set of matrices into joint parts containing variation shared with other connected matrices and variation that is unique for each single matrix. Mapping unique variation is important from a data integration perspective, since it certainly cannot be expected that all variation co-varies. In this work we used JUMBA for integrated analysis of lipidomic, metabolomic and oxylipin datasets obtained from profiling of plasma samples from children infected with P. falciparum malaria. P. falciparum is one of the primary contributors to childhood mortality and obstetric complications in the developing world, what makes development of the new diagnostic and prognostic tools, as well as better understanding of the disease, of utmost importance. In presented work JUMBA made it possible to detect already known trends related to disease progression, but also to discover new structures in the data connected to food intake and personal differences in metabolism. By separating the variation in each data set into joint and unique, JUMBA reduced complexity of the analysis, facilitated detection of samples and variables corresponding to specific structures across multiple datasets and by doing this enabled fast interpretation of the studied system. All this makes JUMBA a perfect choice for multiblock analysis of systems biology data.

  • 133.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Vikström, Ludvig
    Hector, Gustaf
    Johansson, Erik
    Vikström, Conny
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Generalized Subset Designs in Analytical Chemistry2017Inngår i: Analytical Chemistry, ISSN 0003-2700, E-ISSN 1520-6882, Vol. 89, nr 12, s. 6491-6497Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Design of experiments (DOE) is an established methodology in research, development, manufacturing, and production for screening, optimization, and robustness testing. Two-level fractional factorial designs remain the preferred approach due to high information content while keeping the number of experiments low. These types of designs, however, have never been extended to a generalized multilevel reduced design type that would be capable to include both qualitative and quantitative factors. In this Article we describe a novel generalized fractional factorial design. In addition, it also provides complementary and balanced subdesigns analogous to a fold-over in two-level reduced factorial designs. We demonstrate how this design type can be applied with good results in three different applications in analytical chemistry including (a) multivariate calibration using microwave resonance spectroscopy for the determination of water in tablets, (b) stability study in drug product development, and (c) representative sample selection in clinical studies. This demonstrates the potential of generalized fractional factorial designs to be applied in many other areas of analytical chemistry where representative, balanced, and complementary subsets are required, especially when a combination of quantitative and qualitative factors at multiple levels exists.

  • 134.
    Surowiec, Izabella
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Ärlestig, Lisbeth
    Rantapää-Dahlqvist, Solbritt
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Metabolite and Lipid Profiling of Biobank Plasma Samples Collected Prior to Onset of Rheumatoid Arthritis2016Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, nr 10, artikkel-id e0164196Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objective: The early diagnosis of rheumatoid arthritis (RA) is desirable to install treatment to prevent disease progression and joint destruction. Autoantibodies and immunological markers pre-date the onset of symptoms by years albeit not all patients will present these factors, even at disease onset. Additional biomarkers would be of high value to improve early diagnosis and understanding of the process, leading to disease development. Methods: Plasma samples donated before the onset of RA were identified in the Biobank of Northern Sweden, a collection within national health survey programs. Thirty samples from pre-symptomatic individuals and nineteen from controls were subjected to liquid chromatography-mass spectrometry (LCMS) metabolite and lipid profiling. Lipid and metabolite profiles discriminating samples from pre-symptomatic individuals from controls were identified after univariate and multivariate OPLS-DA based analyses. Results: The OPLS-DA models including pre-symptomatic individuals and controls identified profiles differentiating between the groups that was characterized by lower levels of acyl-carnitines and fatty acids, with higher levels of lysophospatidylcholines (LPCs) and metabolites from tryptophan metabolism in pre-symptomatic individuals compared with controls. Lipid profiling showed that the majority of phospholipids and sphingomyelins were at higher levels in pre-symptomatic individuals in comparison with controls. Conclusions: Our LCMS based approach demonstrated that there are changes in small molecule and lipid profiles detectable in plasma samples collected from the pre-symptomatic individuals who subsequently developed RA, which point to an up-regulation of levels of lysophospatidylcholines, and of tryptophan metabolism, perturbation of fatty acid beta-oxidation and increased oxidative stress in pre-symptomatic individuals' years before onset of symptoms.

  • 135.
    Svensson, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Sjögren, Rickard
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Corporate Research, Sartorius AG, Umeå, Sweden.
    Sundell, David
    Sjödin, Andreas
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Corporate Research, Sartorius AG, Umeå, Sweden.
    doepipeline: a systematic approach to optimizing multi-level and multi-step data processing workflows2019Inngår i: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 20, nr 1, artikkel-id 498Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Selecting the proper parameter settings for bioinformatic software tools is challenging. Not only will each parameter have an individual effect on the outcome, but there are also potential interaction effects between parameters. Both of these effects may be difficult to predict. To make the situation even more complex, multiple tools may be run in a sequential pipeline where the final output depends on the parameter configuration for each tool in the pipeline. Because of the complexity and difficulty of predicting outcomes, in practice parameters are often left at default settings or set based on personal or peer experience obtained in a trial and error fashion. To allow for the reliable and efficient selection of parameters for bioinformatic pipelines, a systematic approach is needed.

    Results: We present doepipeline, a novel approach to optimizing bioinformatic software parameters, based on core concepts of the Design of Experiments methodology and recent advances in subset designs. Optimal parameter settings are first approximated in a screening phase using a subset design that efficiently spans the entire search space, then optimized in the subsequent phase using response surface designs and OLS modeling. Doepipeline was used to optimize parameters in four use cases; 1) de-novo assembly, 2) scaffolding of a fragmented genome assembly, 3) k-mer taxonomic classification of Oxford Nanopore Technologies MinION reads, and 4) genetic variant calling. In all four cases, doepipeline found parameter settings that produced a better outcome with respect to the characteristic measured when compared to using default values. Our approach is implemented and available in the Python package doepipeline.

    Conclusions: Our proposed methodology provides a systematic and robust framework for optimizing software parameter settings, in contrast to labor- and time-intensive manual parameter tweaking. Implementation in doepipeline makes our methodology accessible and user-friendly, and allows for automatic optimization of tools in a wide range of cases. The source code of doepipeline is available at https://github.com/clicumu/doepipeline and it can be installed through conda-forge.

  • 136.
    Svensson, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden; Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Öhrman, Caroline
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Bäckman, Stina
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Karlsson, Edvin
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Nilsson, Elin
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Byström, Mona
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Lärkeryd, Adrian
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Myrtennäs, Kerstin
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Stenberg, Per
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Qu, Ping-hua
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden.
    Scholz, Holger C.
    Forsman, Mats
    Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Sjödin, Andreas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Computational Life Science Cluster (CLiC), Umeå University, Umeå, Sweden; Division of CBRN Security and Defence, FOI–Swedish Defence Research Agency, Umeå, Sweden.
    Complete Genome Sequence of Francisella guangzhouensis Strain 08HL01032(T), Isolated from Air-Conditioning Systems in China2015Inngår i: Microbiology Resource Announcements, ISSN 2576-098X, Vol. 3, nr 2, artikkel-id e00024-15Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We present the complete genome sequence of Francisella guangzhouensis strain 08HL01032(T), which consists of one chromosome (1,658,482 bp) and one plasmid ( 3,045 bp) with G+C contents of 32.0% and 28.7%, respectively.

  • 137.
    Torell, Frida
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Accelerator Lab (ACL), Karlsruhe Institute of Technology, Karlsruhe 76344, Germany.
    Bennet, Kate
    Cereghini, Silvia
    Fabre, Mélanie
    Rännar, Stefan
    Lundstedt-Enkel, Katrin
    Moritz, Thomas
    Haumaitre, Cécile
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, Torbjörn
    Metabolic Profiling of Multiorgan Samples: Evaluation of MODY5/RCAD Mutant Mice2018Inngår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 17, nr 7, s. 2293-2306Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In the present study, we performed a metabolomics analysis to evaluate a MODY5/RCAD mouse mutant line as a potential model for HNF1B-associated diseases. Gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) of gut, kidney, liver, muscle, pancreas, and plasma samples uncovered the tissue specific metabolite distribution. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) was used to identify the differences between MODY5/RCAD and wild-type mice in each of the tissues. The differences included, for example, increased levels of amino acids in the kidneys and reduced levels of fatty acids in the muscles of the MODY5/RCAD mice. Interestingly, campesterol was found in higher concentrations in the MODY5/RCAD mice, with a four-fold and three-fold increase in kidneys and pancreas, respectively. As expected, the MODY5/RCAD mice displayed signs of impaired renal function in addition to disturbed liver lipid metabolism, with increased lipid and fatty acid accumulation in the liver. From a metabolomics perspective, the MODY5/RCAD model was proven to display a metabolic pattern similar to what would be suspected in HNF1B-associated diseases. These findings were in line with the presumed outcome of the mutation based on the different anatomy and function of the tissues as well as the effect of the mutation on development.

  • 138.
    Torell, Frida
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Bennett, Kate
    Cereghini, Silvia
    Raennar, Stefan
    Lundstedt-Enkel, Katrin
    Moritz, Thomas
    Haumaitre, Cecile
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, Torbjoern
    Multi-Organ Contribution to the Metabolic Plasma Profile Using Hierarchical Modelling2015Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, nr 6, artikkel-id e0129260Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Hierarchical modelling was applied in order to identify the organs that contribute to the levels of metabolites in plasma. Plasma and organ samples from gut, kidney, liver, muscle and pancreas were obtained from mice. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS) at the Swedish Metabolomics centre, Umea University, Sweden. The multivariate analysis was performed by means of principal component analysis (PCA) and orthogonal projections to latent structures (OPLS). The main goal of this study was to investigate how each organ contributes to the metabolic plasma profile. This was performed using hierarchical modelling. Each organ was found to have a unique metabolic profile. The hierarchical modelling showed that the gut, kidney and liver demonstrated the greatest contribution to the metabolic pattern of plasma. For example, we found that metabolites were absorbed in the gut and transported to the plasma. The kidneys excrete branched chain amino acids (BCAAs) and fatty acids are transported in the plasma to the muscles and liver. Lactic acid was also found to be transported from the pancreas to plasma. The results indicated that hierarchical modelling can be utilized to identify the organ contribution of unknown metabolites to the metabolic profile of plasma.

  • 139.
    Torell, Frida
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Bennett, Kate
    AcureOmics AB, Umeå, Sweden.
    Cereghini, Silvia
    Paris, France.
    Rännar, Stefan
    AcureOmics AB, Umeå, Sweden.
    Lundstedt-Enkel, Katrin
    AcureOmics AB, Umeå, Sweden; Uppsala, Sweden.
    Moritz, Thomas
    AcureOmics AB, Umeå, Sweden.
    Haumaitre, Cecile
    Paris, France.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, Torbjörn
    AcureOmics AB, Umeå, Sweden.
    Tissue sample stability: thawing effect on multi-organ samples2016Inngår i: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 12, nr 2, artikkel-id 19Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Correct handling of samples is essential in metabolomic studies. Improper handling and prolonged storage of samples has unwanted effects on the metabolite levels. The aim of this study was to identify the effects that thawing has on different organ samples. Organ samples from gut, kidney, liver, muscle and pancreas were analyzed for a number of endogenous metabolites in an untargeted metabolomics approach, using gas chromatography time of flight mass spectrometry at the Swedish Metabolomics Centre, Umeå University, Sweden. Multivariate data analysis was performed by means of principal component analysis and orthogonal projection to latent structures discriminant analysis. The results showed that the metabolic changes caused by thawing were almost identical for all organs. As expected, there was a marked increase in overall metabolite levels after thawing, caused by increased protein and cell degradation. Cholesterol was one of the eight metabolites found to be decreased in the thawed samples in all organ groups. The results also indicated that the muscles are less susceptible to oxidation compared to the rest of the organ samples.

  • 140.
    Torell, Frida
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Karlsruhe Institute of Technology, Karlsruhe, Germany.
    Bennett, Kate
    Rännar, Stefan
    Lundstedt-Enkel, Katrin
    Lundstedt, Torbjörn
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    The effects of thawing on the plasma metabolome: evaluating differences between thawed plasma and multi-organ samples2017Inngår i: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, nr 6, artikkel-id 66Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Introduction: Post-collection handling, storage and transportation can affect the quality of blood samples. Pre-analytical biases can easily be introduced and can jeopardize accurate profiling of the plasma metabolome. Consequently, a mouse study must be carefully planned in order to avoid any kind of bias that can be introduced, in order not to compromise the outcome of the study. The storage and shipment of the samples should be made in such a way that the freeze–thaw cycles are kept to a minimum. In order to keep the latent effects on the stability of the blood metabolome to a minimum it is essential to study the effect that the post-collection and pre-analytical error have on the metabolome. Objectives: The aim of this study was to investigate the effects of thawing on the metabolic profiles of different sample types. Methods: In the present study, a metabolomics approach was utilized to obtain a thawing profile of plasma samples obtained on three different days of experiment. The plasma samples were collected from the tail on day 1 and 3, while retro-orbital sampling was used on day 5. The samples were analysed using gas chromatography time-of-flight mass spectrometry (GC TOF-MS). Results: The thawed plasma samples were found to be characterized by higher levels of amino acids, fatty acids, glycerol metabolites and purine and pyrimidine metabolites as a result of protein degradation, cell degradation and increased phospholipase activity. The consensus profile was thereafter compared to the previously published study comparing thawing profiles of tissue samples from gut, kidney, liver, muscle and pancreas. Conclusions: The comparison between thawed organ samples and thawed plasma samples indicate that the organ samples are more sensitive to thawing, however thawing still affected all investigated sample types.

  • 141.
    Torell, Frida
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Eketjäll, Susanna
    Idborg, Helena
    Jakobsson, Per-Johan
    Gunnarsson, Iva
    Svenungsson, Elisabet
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen. Corporate Research, Sartorius AG, Göttingen, Germany.
    Cytokine Profiles in Autoantibody Defined Subgroups of Systemic Lupus Erythematosus2019Inngår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 18, nr 3, s. 1208-1217Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The aim of this study was to evaluate how the cytokine profiles differed between autoantibody based subgroups of systemic lupus erythematosus (SLE). SLE is a systemic autoimmune disease, characterized by periods of flares (active disease) and remission (inactive disease). The disease can affect many organ systems, e.g., skin, joints, kidneys, heart, and the central nervous system (CNS). SLE patients often have an overproduction of cytokines, e.g., interferons, chemokines, and interleukins. The high cytokine levels are part of the systemic inflammation, which can lead to tissue injury. In the present study, SLE patients were divided into five groups based on their autoantibody profiles. We thus defined these five groups: ANA negative, antiphospholipid (aPL) positive, anti-Sm/anti-RNP positive, Sjögren’s syndrome (SS) antigen A and B positive, and patients positive for more than one type of autoantibodies (other SLE). Cytokines were measured using Mesoscale Discovery (MSD) multiplex analysis. On the basis of the cytokine data, ANA negative patients were the most deviating subgroup, with lower levels of interferon (IFN)-γ, tumor necrosis factor (TNF)-α, interleukin (IL)-12/IL-23p40, and interferon gamma-induced protein (IP)-10. Despite low cytokine levels in the ANA negative group, autoantibody profiles did not discriminate between different cytokine patterns.

  • 142.
    Torell, Frida
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Skotare, Tomas
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Multi-Tissue Metabolomics Integration Utilising Hierarchical Modelling and Data Integration MethodsManuskript (preprint) (Annet vitenskapelig)
  • 143.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    O2-PLS for qualitative and quantitative analysis in multivariate calibration2002Inngår i: Journal of Chemometrics: 6 , Pages, Vol. 16, nr 6, s. 283-93Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In this paper the O-PLS method [1] has been modified to further improve its interpretational functionality to give (a) estimates of the pure constituent profiles in X as well as model (b) the Y-orthogonal variation in X, (c) the X-orthogonal variation in Y and (d) the joint X-Y covariation. It is also predictive in both ways, X Y. We call this the O2-PLS approach. In earlier papers we discussed the improved interpretation using O-PLS compared to the partial least squares projections to latent structures (PLS) when systematic Y-orthogonal variation in X exists, i.e. when a PLS model has more components than the number of Y variables. In this paper we show how the parameters in the PLS model are affected and to what degree the interpretational ability of the PLS components changes with the amount of Y-orthogonal variation. In both real and synthetic examples, the O2-PLS method provided improved interpretation of the model and gave a good estimate of the pure constituent profiles, and the prediction ability was similar to the standard PLS model. The method is discussed from geometric and algebraic points of view, and a detailed description of this modified O2-PLS method is given and reviewed.

  • 144.
    Trygg, Johan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Prediction and spectral profile estimation in multivariate calibration2004Inngår i: Journal of Chemometrics, Vol. 18, nr 3-4, s. 166-172Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Direct and indirect calibration have been compared with respect to both prediction and model interpretation. This included their ability to estimate the pure spectral profile of each known constituent in a mixture of different metal-ion complexes. In the examples, the predictions by indirect calibration, represented by the PLS and O-PLS methods, were consistently better than those of direct calibration, exemplified by the CLS method. It was further demonstrated that indirect calibration is equally capable to direct calibration in estimating the pure spectral profiles, as long as the unknown systematic variation is properly handled. A linear transformation of the regression coefficient matrix, given by K = B(BTB)-1, is all that is needed. Note that this does not only apply to spectral data, but any situation where the Y-variables can be assumed to additively contribute to the variation in the X matrix. Throughout the examples, the O-PLS method was able to maintain good spectral profile estimates and predictions. This indicates that O-PLS may be the approach for simultaneous good prediction and interpretation of complex multivariate systems.

  • 145.
    Trygg, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Aberg, K. Magnus
    13th Scandinavian Symposium on Chemometrics2014Inngår i: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 28, nr 8, s. 604-605Artikkel i tidsskrift (Annet vitenskapelig)
  • 146.
    Trygg, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gabrielsson, Jon
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, T.
    Data Preprocessing: Background Estimation, Denoising, and Preprocessing ...2009Inngår i: Comprehensive Chemometrics: Chemical and Biochemical Data Analysis, VOL 1-4 / [ed] Steven Brown, Romà Tauler, Beata Walczak, AMSTERDAM: Elsevier, 2009, s. A1-A8Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 147.
    Trygg, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Gullberg, J
    Johansson, A I
    Jonsson, Pär
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Moritz, Thomas
    II.2 Chemometrics in Metabolomics - An Introduction2006Inngår i: Plant Metabolomics: Biotechnology in Agriculture and Forestry 57, Springer Verlag , 2006, s. 117-128Kapittel i bok, del av antologi (Fagfellevurdert)
  • 148.
    Trygg, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Holmes, Elaine
    Lundstedt, Torbjörn
    Chemometrics in metabonomics2007Inngår i: Journal Proteome Research, ISSN 1535-3893, Vol. 6, nr 2, s. 469-79Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

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

  • 149.
    Trygg, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Kettaneh-Wold, Nouna
    Wallbäcks, Lars
    2D wavelet analysis and compression of on-line industrial process data2001Inngår i: Journal of Chemometrics: SPECIAL ISSUE: Dedicated to Harald Martens-The Third Recipient of the Herman Wold Medal. Issue Edited by Lennart Eriksson, Torbjörn Lundstedt, Vol. 15, nr 4, s. 299-319Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In recent years the wavelet transform (WT) has interested a large number of scientists from many different fields. Pattern recognition, signal processing, signal compression, process monitoring and control, and image analysis are some areas where wavelets have shown promising results. In this paper, 2D wavelet analysis and compression of near-infrared spectra for on-line monitoring of wood chips is reviewed. We introduce a new parameter for outlier detection, distance to model in wavelet space (DModW), which is analogous to the residual parameter (DModX) used in principal component analysis (PCA) and partial least squares analysis (PLS). Additionally, we describe the wavelet power spectrum (WPS), the wavelet analogue of the power spectrum. The WPS gives an overview of the time-frequency content in a signal. In the example given, wavelets improved the detection of spectral shift and compressed data 1000-fold without degrading the quality of the 2D wavelet-compressed PCA model. The example concerned an industrial process-monitoring situation where near-infrared spectra are measured on-line on top of a conveyer belt filled with wood chips at a Swedish pulp plant.

  • 150.
    Trygg, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Lundstedt, Torbjörn
    Chapter 6 - Chemometrics Techniques for Metabonomics2007Inngår i: THE HANDBOOK OF METABONOMICS AND METABOLOMICS, Elsevier Science , 2007Kapittel i bok, del av antologi (Fagfellevurdert)
    Abstract [en]

    In biology, as well as in other branches of science and technology, these is a steady trend towards the use of more variables (properties) to characterize observations (e.g. samples, experiments, time points). Often, these measurements can be arranged into a data table, where each row constitutes an observation and the columns represent the variables or factors we have measured (e.g. intensities at a specific wavelength, mass-to-charge ratio, NMR chemical shift). This development generates increasingly complex data tables, which are hard to summarize and overview without appropriate tools. Thus, in this chapter we will try to guide the reader through a chemometrical approach for extracting information out of data.

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