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  • 1. Hasman, A
    et al.
    Andersen, S K
    Klein, Gunnar O
    Örebro universitet, Handelshögskolan vid Örebro Universitet.
    Schulz, S
    Aarts, J
    Mazzoleni, M C
    MIE 2008: eHealth beyond the horizon-get IT there.2009Inngår i: Methods of Information in Medicine, ISSN 0026-1270, Vol. 48, nr 2, s. 135-136Artikkel i tidsskrift (Fagfellevurdert)
  • 2.
    Jacobsen, Marc
    et al.
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Repsilber, Dirk
    Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany; Institute for Biology and Biochemistry, University Potsdam, Potsdam-Golm, Germany.
    Gutschmidt, Andrea
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Neher, A
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Feldmann, K
    Asklepios Center for Respiratory Medicine and Thoracic Surgery, Munich-Gauting, Germany .
    Mollenkopf, H J
    Microarray Core Facilities, Max Planck Institute for Infection Biology, Berlin, Germany.
    Kaufmann, S H E
    Department of Immunology, Max Planck Institute for Infection Biology, Berlin, Germany .
    Ziegler, Andreas
    Institute of Medical Biometry and Statistics, University at Lübeck, Lübeck, Germany.
    Deconfounding microarray analysis: independent measurements of cell type proportions used in a regression model to resolve tissue heterogeneity bias2006Inngår i: Methods of Information in Medicine, ISSN 0026-1270, Vol. 45, nr 5, s. 557-63Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives: Microarray analysis requires standardized specimens and evaluation procedures to achieve acceptable results. A major limitation of this method is caused by heterogeneity in the cellular composition of tissue specimens, which frequently confounds data analysis. We introduce a linear model to deconfound gene expression data from tissue heterogeneity for genes exclusively expressed by a single cell type.

    Methods: Gene expression data are deconfounded from tissue heterogeneity effects by analyzing them using an appropriate linear regression model. In our illustrating data set tissue heterogeneity is being measured using flow cytometry. Gene expression data are determined in parallel by real time quantitative polymerase chain reaction (qPCR) and microarray analyses. Verification of deconfounding is enabled using protein quantification for the respective marker genes.

    Results: For our illustrating dataset, quantification of cell type proportions for peripheral blood mononuclear cells (PBMC) from tuberculosis patients and controls revealed differences in B cell and monocyte proportions between both study groups, and thus heterogeneity for the tissue under investigation. Gene expression analyses reflected these differences in celltype distribution. Fitting an appropriate linear model allowed us to deconfound measured transcriptome levels from tissue heterogeneity effects. In the case of monocytes, additional differential expression on the single cell level could be proposed. Protein quantification verified these deconfounded results.

    Conclusions: Deconfounding of transcriptome analyses for cellular heterogeneity greatly improves interpretability, and hence the validity of transcriptome profiling results.

  • 3.
    Klein, Gunnar O
    Centre for Health Telematics, Karolinska Institutet, Stockholm, Sweden.
    Standardization of health informatics - Results and challenges2002Inngår i: Methods of Information in Medicine, ISSN 0026-1270, Vol. 41, nr 4, s. 261-270Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives: This review article aims to highlight the importance of standards for effective communication and provides an overview of international standardization activities. Methods: This article is based on the experience of the author of European standardization in CEN, which he leads, and the global work of ISO, where he is leading the security working group, and an overview of the work of DICOM, IEEE and HL7, partly using their web presentations. Results: Health communication is highly dependent of the general development of information technology with standards coming from ISQ/IEC ITC1, ITU and several other organizations e.g. IETE, the World Wide Web consortium and Open group. A number of standardization initiatives have been in progress for more than ten years with the aim to facilitate different aspects of the exchange of health information. Electronic record architecture, Message structures, Concept representation, Device communication including imaging and Security are the main areas. Conclusions. Important results have been achieved, and in some fields and parts of the worked, standards are widely used today. Unfortunately, we are still facing the fact that most healthcare information systems cannot exchange information with all systems for which this would be desired. Either the existing standards are not sufficiently implemented, or the required standards and necessary national implementation guidelines do not yet exist. This causes unacceptable risks to patients, inefficient use of healthcare resources, and sub optimal development of medical knowledge. Fortunately, the different bodies are now largely co-operating to achieve global consensus.

  • 4.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Fink, Ludger
    Institut für Pathologie, Justus-Liebig-Universität Gießen, Gießen, Germany.
    Jacobsen, Marc
    Max-Planck-Institut für Infektionsbiologie, Berlin, Germany .
    Bläsing, Oliver
    Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam, Germany.
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Sample selection for microarray gene expression studies2005Inngår i: Methods of Information in Medicine, ISSN 0026-1270, Vol. 44, nr 3, s. 461-7Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives: The choice of biomedical samples for microarray gene expression studies is decisive for both validity and interpretability of results. We present a consistent, comprehensive framework to deal with the typical selection problems in microarray studies.

    Methods: Microarray studies are designed either as case-control studies or as comparisons of parallel groups from cohort studies, since high levels of random variation in the experimental approach thwart absolute measurements of gene expression levels. Validity and results of gene expression studies heavily rely on the appropriate choice of these study groups. Therefore, the so-called principles of comparability, which are well known from both clinical and epidemiological studies, need to be applied to microarray experiments.

    Results: The principles of comparability are the study-base principle, the principle of deconfounding and the principle of comparable accuracy in measurements. We explain each of these principles, show how they apply to microarray experiments, and illustrate them with examples. The examples are chosen as to represent typical stumbling blocks of microarray experimental design, and to exemplify the benefits of implementing the principles of comparability in the setting of microarray experiments.

    Conclusions: Microarray studies are closely related to classical study designs and therefore have to obey the same principles of comparability as these. Their validity should not be compromised by selection, confounding or information bias. The so-called study-base principle, calling for comparability and thorough definition of the compared cell populations, is the key principle for the choice of biomedical samples and controls in microarray studies.

  • 5.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Mansmann, Ulrich
    Institut für Medizinische Biometrie und Informatik, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany .
    Brunner, Edgar
    Abteilung Medizinische Statistik, Georg-August-Universität Göttingen, Göttingen, Germany .
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Tutorial on microarray gene expression experiments: An introduction2005Inngår i: Methods of Information in Medicine, ISSN 0026-1270, Vol. 44, nr 3, s. 392-9Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives: With the collection of articles presented in this special issue, we aim at educating interested statisticians and biometricians on the one hand as well as biologists and medical researchers on the other with respect to basic necessities in planning, conducting and analyzing microarray gene expression experiments. The reader should get comprehensive directions to understand both the overall structure of this approach as well as the decisive details, which enable--or thwart--a meaningful data analysis.

    Methods: For a one-day workshop with tutorial character we brought together experts in design, conduct and analysis of microarray gene expression experiments who prepared a series of comprehensive lessons. These contributions were then reworked into a series of introductory articles and bundled in form and content as a Special Topic.

    Results: It was possible to present a tutorial overview of the field. The interested reader was able to learn the basic necessities and was referred to further references for details on the possible alternatives. A recipe style all-embracing plan, covering all eventualities and possibilities was not only beyond the scope of an introductory tutorial-like presentation, but was also not yet agreed upon by the scientific society.

    Conclusions: It proved feasible to find a framework for integrating the interdisciplinary approaches to the challenging field of gene expression analysis with microarrays, hopefully contributing to a rapid and comprehensive introduction for novices.

  • 6.
    Repsilber, Dirk
    et al.
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany.
    Ziegler, Andreas
    Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Lübeck, Germany .
    Two-color microarray experiments. Technology and sources of variance.2005Inngår i: Methods of Information in Medicine, ISSN 0026-1270, Vol. 44, nr 3, s. 400-4Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Objectives: Microarray gene expression experiments have a complex technical background. Knowledge about certain technical details is inevitable to judge alternatives for both experimental design and analysis. Here, we introduce the necessary details for the so-called two-color microarray experiments and review major sources of technical variance.

    Methods: We follow the sequence of experimental steps during a typical two-color microarray gene expression experiment, stressing decisive points in the choice of technique, experimental handling and biophysical basics. We point out where technical variation is to be expected.

    Results: Tissue storage, RNA extraction techniques, as well as the microarray hybridization represent major components of technical variance to be considered. Depending on the possibilities for access to the biomedical material under investigation, choice of amplification and labeling techniques can also be decisive to avoid additional technical variance. The two-color microarray experimental approach seeks to avoid a group of probe-level technical biases making use of the advantages of an incomplete block-design.

    Conclusions: It is worth to know the major sources of technical variance during the typical experimental sequence, both for choice of experimental design and techniques of molecular biology, as well as for the understanding of quality control and normalization approaches. Here, early investments pay at the level of reduced technical variance, allowing for enhanced detection levels for the effects under investigation.

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