Janko Dietzsch

Differential gene expression in peripheral blood of patients suffering from post-traumatic stress disorder

Differential gene expression in peripheral blood of patients suffering from post-traumatic stress disorder

Mayday-a microarray data analysis workbench

UNLABELLED Mayday is a workbench for visualization, analysis and storage of microarray data. It features a graphical user interface and supports the development and integration of existing and new analysis methods. Besides the infrastructural core functionality, Mayday offers a variety of plug-ins, such as various interactive viewers, a connection to the R statistical environment, a connection to SQL-based databases and different data mining methods, including WEKA-library based methods for classification and various clustering methods. In addition, so-called meta information objects are provided for annotation of the microarray data allowing integration of data from different sources, which is a feature that, for instance, is employed in the enhanced heatmap visualization. SUPPLEMENTARY INFORMATION The software and more detailed information including screenshots and a user guide as well as test data can be found on the Mayday home page http://www.zbit.uni-tuebingen.de/pas/mayday. The core is published under the GPL (GNU Public License) and the associated plug-ins under the LGPL (Lesser GNU Public License).

Identification of a blood-based biomarker panel for classification of Alzheimer's disease.

An ideal diagnostic test for Alzheimers disease (AD) should be non-invasive and easily applicable. Thus, there is a clear need to search for biomarkers in blood. In the present study, we have used multivariate data analysis [support vector machine (SVM)] to investigate whether a blood-based biomarker panel allows discrimination between AD patients and healthy controls at the individual level. We collected a total of 155 serum samples from individuals with early AD and age-matched healthy controls and measured serum levels of 24 markers involved in several biological pathways by ELISA. The dataset was randomly split into a training set for predictor discovery and classification training and a test set for class prediction of blinded samples (3:1 ratio) to evaluate the chosen predictors and parameters. After selection of a feature group of the three most discriminative parameters (cortisol, von Willebrand factor, oxidized LDL antibodies) in the training set, the application of SVM to the training/independent test dataset resulted in an 81.7%/87.1% correct classification for AD and control subjects. In conclusion, we identified a panel of three blood markers, which allowed SVM-based distinguishing of AD patients from healthy controls on a single-subject classification level with clinically relevant accuracy and validity. Blood-based biomarkers might have utility in AD diagnostics as screening tool before further classification with CSF biomarkers and imaging. Future studies should examine whether blood-based biomarkers may also be useful to differentiate AD patients from other dementias.

A framework for visualization of microarray data and integrated meta information

We have developed a methodology that allows integration of microarray data and meta information within a visualization in order to guide the investigator during data exploration and analysis. A simple mathematical framework is introduced that uses scoring functions to map meta information to relevance ratings of genes. To explore the potential of this framework we extended the traditional heatmap with new features to graphically represent the relevance ratings. These ratings are visualized by an additional color gradient, by scaling the vertical height of matrix rows, by rearranging rows or by inserting new columns into the heatmap. This visualization is called an enhanced heatmap. We have applied our approach to microarray data of the Saccharomyces cerevisiae cell cycle, complemented with supplemental data that we both derived from the microarray data itself and retrieved from public databases. Using these data we demonstrate how this visualization concept can be efficiently used to identify certain features of genes and to detect inconsistencies in the data. Thus, the investigator has the possibility to get an overview of data from various sources and at the same time can gain a deeper insight into the structure of the combined data. The concept is not restricted to heatmaps, and can be used to extend further visualization techniques, such as profile plots. We found that our method is a powerful tool to integrate supplemental data into microarray visualizations and that it increases the efficiency of visual data exploration, which is a fundamental part of microarray data analyses.

SpRay: A visual analytics approach for gene expression data

We present a new application, SpRay, designed for the visual exploration of gene expression data. It is based on an extension and adaption of parallel coordinates to support the visual exploration of large and high-dimensional datasets. In particular, we investigate the visual analysis of gene expression data as generated by micro-array experiments; We combine refined visual exploration with statistical methods to a visual analytics approach that proved to be particularly successful in this application domain. We will demonstrate the usefulness on several multidimensional gene expression datasets from different bioinformatics applications.

Immune Profiling in Blood Identifies sTNF-R1 Performing Comparably Well as Biomarker Panels for Classification of Alzheimer's Disease Patients

BACKGROUND Alzheimers disease (AD) has been linked to a state of cerebral and systemic inflammation. The objective of the present study was to determine whether singular markers or a set of inflammatory biomarkers in peripheral blood allow discrimination between AD patients and healthy controls at the individual level. METHODS Using bead based multiplexed sandwich immunoassays, 25 inflammatory biomarkers were measured in 164 serum samples from individuals with early AD and age-matched cognitively healthy elderly controls. The data set was randomly split into a training set for feature selection and classification training and a test set for class prediction of blinded samples (1 : 1 ratio) to evaluate the chosen predictors and parameters. Multivariate data analysis was performed with use of a support vector machine (SVM). RESULTS After selection of sTNF-R1 as most discriminative parameter in the training set, the application of SVM to the independent test dataset resulted in a 90.0% correct classification for individual AD and control subjects. CONCLUSIONS We identified sTNF-R1 from a marker set consisting of 25 inflammatory biomarkers, which allowed SVM-based discrimination of AD patients from healthy controls on a single-subject classification level comparably well as biomarker panels with a clinically relevant accuracy and validity. Although larger sample populations will be needed to confirm this diagnostic accuracy, our study suggests that sTNF-R1 in serum-either as singular marker or incorporated into a biomarker panel-could be a powerful new biomarker for detection of AD. In addition, selective inhibition of TNF-R1 function may represent a new therapeutic approach in AD.

Associating the magnitude of relative afferent pupillary defect (RAPD) with visual field indices in glaucoma patients

Purpose To identify the variable with the strongest association between the magnitude of the relative afferent pupillary defect (RAPD) and visual field indices in patients with glaucomatous optic neuropathy. Methods Seventy-nine consecutive subjects with manifest glaucomatous optic neuropathy at least in one eye were enrolled in this retrospective study. RAPD was assessed with the swinging flashlight test and quantified with a neutral density filter. Perimetry was performed using the fast thresholding strategy German Adaptive Threshold Estimation. The values of the central differential luminance sensitivity (DLS), of the MD (mean defect) and of the ‘loss volume’ (LVOL) based on the individually modelled 3D hill of vision—the latter two within the eccentricities of 10°, 20° and 30°, respectively—were entered into a linear regression model without intercept as a function of RAPD. Results An absolute value of RAPD of 0.3 log10 units or more was present in 20 out of 79 glaucoma subjects (25%). The magnitude of RAPD was most closely associated with LVOL-30° (R2=0.77), followed by MD-30° (R2=0.73), MD-20° (R2=0.71), LVOL-20° (R2=0.67), MD-10° (R2=0.58), LVOL-10° (R2=0.54) and central DLS (R2=0.04). Conclusions The prevalence of RAPD in glaucoma patients is comparatively small (25%). The magnitude of RAPD in glaucoma subjects is associated most closely with the LVOL within 30° eccentricity (which is the maximum visual field region tested in this study) and most loosely with central DLS, underscoring the impact of the entire (30°) visual field area on the afferent pupillary system.

Imaging and Perimetry Society Standards and Guidelines

Purpose. To provide readers with standards, recommendations, guidelines, and requirements for the application of perimetry to clinical ophthalmic practice and scientific study. Methods. A working group of perimetry and visual field specialists from many parts of the world constructed a document that would allow current and future perimeters to be assessed by the same criteria. Because hardware and software technology, statistical procedures and clinical conditions are constantly changing, the characteristics in this paper emphasize general concepts rather than specific implementations employed by current devices. Results. Critical aspects of perimetry included indications for perimetry, perimetric techniques, stimulus characteristics, test administration, patient preparation, data display, statistical analysis, interpretation of visual field findings, a glossary of terms and definitions, and standards for comparison of different perimetric tests. Each of these topics is discussed, along with their advantages and disadvantages. Conclusions. These guidelines serve as a basis for practitioners to evaluate their perimetric needs in relation to their clinical practice and patient population so that informed decisions can be made for visual field testing. In addition, these issues should be used as a cornerstone for future technological and practical improvements to the visual field diagnostic procedures.

Normal Values for the Full Visual Field, Corrected for Age- and Reaction Time, Using Semiautomated Kinetic Testing on the Octopus 900 Perimeter

Purpose To determine normal values of the visual field (VF), corrected for age and reaction time (RT) for semiautomated kinetic perimetry (SKP) on the Octopus 900 perimeter, create a model describing the age-dependency of these values, and assess test–retest reliability for each isopter. Methods Eighty-six eyes of 86 ophthalmologically healthy subjects (age 11–79 years, 34 males, 52 females) underwent full-field kinetic perimetry with the Octopus 900 instrument. Stimulus size, luminance, velocity, meridional angle, subject age, and their interactions, were used to create a smooth multiple regression mathematical model (V/4e, III/4e, I/4e, I/3e, I/2e, I/1e, and I/1a isopters). Fourteen subjects (2 from each of 7 age groups) were evaluated on three separate sessions to assess test–retest reliability of the isopters. Reaction time (RT) was tested by presenting 12 designated RT-vectors between 10° and 20° within the seeing areas for the III/4e isopter (stimulus velocity, 3°/second). Four RT- vectors were presented at the nasal (0° or 180°), superotemporal (45°), and inferior (270°) meridians. Results The model fit was excellent (r2 = 0.94). The test–retest variability was less than 5°, and the median decrease in this deviation attributed to aging, per decade, for all age groups and for all stimulus sizes was 0.8°. No significant learning effect was observed for any age group or isopter. Conclusion Age-corrected and RT-corrected normative threshold values for full-field kinetic perimetry can be adequately described by a smooth multiple linear regression mathematical model. Translational Relevance A description of the entire kinetic VF is useful for assessing a full characterization of VF sensitivity, determining function losses associated with ocular and neurologic diseases, and for providing a more comprehensive analysis of structure–function relationships.

RELIABLE CLASSIFICATION OF VISUAL FIELD DEFECTS IN AUTOMATED PERIMETRY USING CLUSTERING

Automated perimetry allows examination of the visual field for diagnostic purposes. Location, shape and size of defects in the visual field detected during a perimetric examination are characteristic hints for the underlying disease of the visual system. Thus a reliable identification of defect types is essential for the proper treatment. We present a classifying system based on cluster analysis and Self-Organizing Maps for the automatic classification of visual field defects. The classifying system distinguishes between eight defect classes and was evaluated on over 8.800 perimetric examinations with a mean classification success of 78%. The classification algorithm is integrated into a software package that can be run on common computers using minor resources; its output can be considered as a suggestion for the physician. As the classification framework is decoupled from the perimetric hardware, it can also be used for the remote classification of perimetric examinations, e.g. in tele-medicine.