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  • 1.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hassan, Saadia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Nygren, Peter
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Radiology, Oncology and Radiation Science, Oncology.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Label free quantification of time evolving morphologies using time-lapse video microscopy enables identity control of cell lines and discovery of chemically induced differential activity in iso-genic cell line pairs2015In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 141, p. 24-32Article in journal (Refereed)
    Abstract [en]

    Label free time-lapse video microscopy based monitoring of time evolving cell population morphology has potential to offer a simple and cost effective method for identity control of cell lines. Such morphology monitoring also has potential to offer discovery of chemically induced differential changes between pairs of cell lines of interest, for example where one in a pair of cell lines is normal/sensitive and the other malignant/resistant. A new simple algorithm, pixel histogram hierarchy comparison (PHHC), for comparison of time evolving morphologies (TEM) in phase contrast time-lapse microscopy movies was applied to a set of 10 different cell lines and three different iso-genic colon cancer cell line pairs, each pair being genetically identical except for a single mutation. PHHC quantifies differences in morphology by comparing pixel histogram intensities at six different resolutions. Unsupervised clustering and machine learning based classification methods were found to accurately identify cell lines, including their respective iso-genic variants, through time-evolving morphology. Using this experimental setting, drugs with differential activity in iso-genic cell line pairs were likewise identified. Thus, this is a cost effective and expedient alternative to conventional molecular profiling techniques and might be useful as part of the quality control in research incorporating cell line models, e.g. in any cell/tumor biology or toxicology project involving drug/agent differential activity in pairs of cell line models.

  • 2.
    Danielsson, Rolf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry, Analytical Chemistry.
    Allard, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry, Analytical Chemistry.
    Sjöberg, Per
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry, Analytical Chemistry.
    Bergquist, Jonas
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Physical and Analytical Chemistry, Analytical Chemistry.
    Exploring liquid chromatography-mass spectrometry fingerprints of urine samples from patients with prostate or urinary bladder cancer2011In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 108, no 1, p. 33-48Article in journal (Refereed)
    Abstract [en]

    Data processing and analysis have become true rate and success limiting factors for molecular research where a large number of samples of high complexity are included in the data set. In general rather complicated methodologies are needed for the combination and comparison of information as obtained from selected analytical platforms. Although commercial as well as freely accessible software for high-throughput data processing are available for most platforms, tailored in-house solutions for data management and analysis can provide the versatility and transparency eligible for e.g. method development and pilot studies. This paper describes a procedure for exploring metabolic fingerprints in urine samples from prostate and bladder cancer patients with a set of in-house developed Matlab tools. In spite of the immense amount of data produced by the LC-MS platform, in this study more than 1010 data points, it is shown that the data processing tasks can be handled with reasonable computer resources. The preprocessing steps include baseline subtraction and noise reduction, followed by an initial time alignment. In the data analysis the fingerprints are treated as 2-D images, i.e. pixel by pixel, in contrast to the more common list-based approach after peak or feature detection. Although the latter approach greatly reduces the data complexity, it also involves a critical step that may obscure essential information due to undetected or misaligned peaks. The effects of remaining time shifts after the initial alignment are reduced by a binning and [‘]blurring’ procedure prior to the comparative multivariate and univariate data analyses. Other factors than cancer assignment were taken into account by ANOVA applied to the PCA scores as well as to the individual variables (pixels). It was found that the analytical day-to-day variations in our study had a large confounding effect on the cancer related differences, which emphasizes the role of proper normalization and/or experimental design. While PCA could not establish significant cancer related patterns, the pixel-wise univariate analysis could provide a list of about a hundred [‘]hotspots’ indicating possible biomarkers. This was also the limited goal for this study, with focus on the exploration of a really huge and complex data set. True biomarker identification, however, needs thorough validation and verification in separate patient sets.

  • 3.
    Danielsson, Rolf
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry, Analytical Chemistry.
    Bäckström, Daniel
    Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry, Analytical Chemistry.
    Ullsten, Sara
    Rapid multivariate analysis of LC/GC/CE data (single or multiple channel detection) without prior peak alignment2006In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 84, no 1-2, p. 33-39Article in journal (Refereed)
    Abstract [en]

    One- or two-dimensional data obtained with LC/GC/CE and single or multiple channel detection (MS, UV/VIS) are often used as 'fingerprints' in order to characterize complex samples. The relation between samples is then explored by multivariate data analysis (PCA, hierarchical clustering), but inevitable more or less random variation in separation conditions obstructs the analysis. Several methods for peak alignment have been developed, with more or less increase of time and efforts for computations. In this work another approach is presented, based on a correlation measure less sensitive for variations in retention/migration time. The merits of the method as a fast initial data exploration tool are demonstrated for a case study of urine profiling with CE/MS.

  • 4.
    Forshed, Jenny
    et al.
    Department of Analytical Chemistry, Stockholm University.
    Stolt, Ragnar
    Department of Analytical Chemistry, Stockholm University.
    Idborg, Helena
    Department of Analytical Chemistry, Stockholm University.
    Jacobsson, Sven P.
    Department of Analytical Chemistry, Stockholm University.
    Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H NMR data2007In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 85, no 2, p. 179-185Article in journal (Refereed)
    Abstract [en]

    A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1

  • 5.
    Lundstedt-Enkel, Katrin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Physiology and Developmental Biology, Environmental Toxicology.
    Gabrielsson, Jon
    Olsman, Helena
    Seifert, Elisabeth
    Petterssen, Jarle
    Lek, Per M.
    Boman, Arne
    Lundstedt, Torbjörn
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Medicinal Chemistry.
    Different multivariate approaches to material discovery, process development, PAT and environmental process monitoring2006In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 84, no 1-2, p. 201-207Article in journal (Refereed)
    Abstract [en]

    The aim with the present paper is to illustrate the use of multivariate strategies (i.e. integration of different multivariate methods) with five examples, four from the pharmaceutical industry and one from environmental research.

    In the first part, two examples wherein hierarchical models are applied to quality control (QC) and process control are discussed. In the second part a more complex problem and a strategy for material discovery/development are presented wherein a combination of multivariate calibration, multivariate analysis and multivariate design is needed. In the third part, a process analytical/optimization problem is illustrated with a two-step process, demanding that different multivariate tools are combined in a sequential way so that a useful model can be established and the process can be understood. In the final part the usefulness of principal component analysis followed by soft independent modelling of class analogy is illustrated with an example from environmental process monitoring. The five examples from quite different areas show that the chemometric tools are even more powerful if used integrated. However, different strategies and combinations of the tools have to be applied, depending on the problem and the aim.

  • 6.
    Shoombuatong, Watshara
    et al.
    Mahidol Univ, Fac Med Technol, Ctr Data Min & Biomed Informat, Bangkok 10700, Thailand..
    Nabu, Sunanta
    Mahidol Univ, Fac Med Technol, Ctr Data Min & Biomed Informat, Bangkok 10700, Thailand..
    Simeon, Saw
    Mahidol Univ, Fac Med Technol, Ctr Data Min & Biomed Informat, Bangkok 10700, Thailand..
    Prachayasittikul, Virapong
    Mahidol Univ, Fac Med Technol, Dept Clin Microbiol & Appl Technol, Bangkok 10700, Thailand..
    Lapins, Maris
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Nantasenamat, Chanin
    Mahidol Univ, Fac Med Technol, Ctr Data Min & Biomed Informat, Bangkok 10700, Thailand..
    Extending proteochemometric modeling for unraveling the sorption behavior of compound-soil interaction2016In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 151, p. 219-227Article in journal (Refereed)
    Abstract [en]

    Contamination of ground water by industrial chemicals presents a major environmental and health problem. Soil sorption plays an important role in the transport and movement of such pollutant chemicals. In this study, proteochemometric (PCM) modeling was used to unravel the origins of interactions of 17 phthalic acid esters (PAEs) against 3 soil types by predicting the organic carbon content normalized sorption coefficient (logK(oc)) values as a function of fingerprint descriptors of 17 PAEs and physical and textural properties of 3 soils. The results showed that PCM models provided excellent predictivity (R-2 = 0.94, Q(2) = 0.89,Q(Ext)(2) = 0.85). In further validation of the model, our proposed PCM model was assessed by leave-one-compound-out (Q(LOCO)(2) = 0.86) and leave-one-soil-out (Q(LOCO)(2) = 0.86) cross-validations. The transparency of the PCM model allowed interpretation of the underlying importance of descriptors, which potentially contributes to a better understanding on the outcome of PAEs in the environment. A thorough analysis of descriptor importance revealed the contribution of secondary carbon atoms on the hydrophobicity and flexibility of PAEs as significant properties in influencing the soil sorption capacity.

  • 7. Stehlik, M.
    et al.
    Strelec, L.
    Thulin, Måns
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics.
    On robust testing for normality in chemometrics2014In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 130, p. 98-108Article in journal (Refereed)
    Abstract [en]

    The assumption that the data has been generated by a normal distribution underlies many statistical methods used in chemometrics. While such methods can be quite robust to small deviations from normality, for instance caused by a small number of outliers, common tests for normality are not and will often needlessly reject normality. It is therefore better to use tests from the little-known class of robust tests for normality. We illustrate the need for robust normality testing in chemometrics with several examples, review a class of robustified omnibus Jarque-Bera tests and propose a new class of robustified directed Lin-Mudholkar tests. The robustness and power of several tests for normality are compared in a large simulation study. The new tests are robust and have high power in comparison with both classic tests and other robust tests. A new graphical method for assessing normality is also introduced.

1 - 7 of 7
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