Multi-dimensional Visualization Tools Research Articles

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry.

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.

Sensor Data Interpretation in Bridge Monitoring—A Case Study

Large amount of data is obtained during bridge monitoring using sensors. Interpreting this data in order to obtain useful information about the condition of the bridge is not straight forward. This paper describes a case study of a railway bridge in India and explains how multi-dimensional visualization tools were used to extract relevant information from data. Parallel axis plots were used to visually examine the data. Trends and patterns in data were observed, which were used for more detailed investigation. The case study shows the complexity in data interpretation even in the case of simple bridge configurations.

Discovering Oculometric Patterns to Detect Cognitive Performance Changes in Healthy Youth Football Athletes.

In this paper, we focus on the application of oculometric patterns extracted from raw eye movements during a mental workload task to assess changes in cognitive performance in healthy youth athletes over the course of a typical sport season. Oculometric features pertaining to fixations and saccades were measured on 116 athletes in pre- and post-season testing. Participants were between 7 and 14years of age at pre-season testing. Due to varied developmental rates, there were large interindividual performance differences during a mental workload task consisting of reading numbers. Based on different reading speeds, we classified three profiles (slow, moderate, and fast) and established their corresponding baselines for oculometric data. Within each profile, we describe changes in oculomotor function based on changes in cognitive performance during the season. To visualize these changes in multidimensional oculometric data, we also present a multidimensional visualization tool named DiViTo (diagnostic visualization tool). These experimental, computational informatics and visualization methodologies may serve to utilize oculometric information to detect changes in cognitive performance due to mild or severe cognitive impairment such as concussion/mild traumatic brain injury, as well as possibly other disorders such as attention deficit hyperactivity disorders, learning/reading disabilities, impairment of alertness, and neurocognitive function.

A Framework for Visualization-Driven Eco-Conscious Design Exploration

A large portion of design activity involves applying previous design knowledge in order to solve new problems. Therefore, facilitating eco-conscious exploration of archived designs is needed for advancing sustainable product design. It is thus necessary to create integrated exploration tools that share common data representations for design and sustainability-related product metadata. This can allow designers to observe covariations in design data and develop engineering intuition with regards to environmental sustainability performance. In this work, we present a framework for relating sustainability and product metadata using taxonomy-based representations of lifecycle data. This facilitates simultaneous visualization of environmental indicators along with part similarities. To demonstrate this framework, we implement shapeSIFT, an interactive multidimensional visualization tool for eco-conscious design exploration. shapeSIFT uses a visual analytics-based approach to represent part metadata and environmental indicators. This facilitates query-based dynamic exploration of part repositories.

Using spatial principles to optimize distributed computing for enabling the physical science discoveries

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.

Multidimensional Visualization Tools for Analysis of Expression Data

Expression data analysis is based mostly on the statistical approaches that are indispensable for the study of biological systems. Large amounts of multidimensional data resulting from the high-throughput technologies are not completely served by biostatistical techniques and are usually complemented with visual, knowledge discovery and other computational tools. In many cases, in biological systems we only speculate on the processes that are causing the changes, and it is the visual explorative analysis of data during which a hypothesis is formed. We would like to show the usability of multidimensional visualization tools and promote their use in life sciences. We survey and show some of the multidimensional visualization tools in the process of data exploration, such as parallel coordinates and radviz and we extend them by combining them with the self-organizing map algorithm. We use a time course data set of transitional cell carcinoma of the bladder in our examples. Analysis of data with these tools has the potential to uncover additional relationships and non-trivial structures.

56 An Integrated Information System: A Resource for Pediatric Tissue Banking, Clinical and Experimental Data Annotation and Analysis.

BACKGROUND: Our knowledge of childhood diseases has been greatly expanded by research on biologic specimens. Clinical researchers commonly collect multiple specimens per patient and use genomics and proteomics to study disease processes and to predict the response to treatment. The ability of such studies to generate meaningful results is dependent on a well–annotated biospecimen repository, which combines pathology data and experimental data from high throughput technologies and structured clinical data. We present the architecture and approach of an evolving information service at Indiana University for specimen collections with clinical and research data annotations. The services to be provided include: specimen inventory, clinical data annotations, experimental data annotations, analysis toolbox and data visualizationAPPROACH: A virtual specimen inventory was created for tracking specimens based on the National Cancer Institute's Cancer Bioinformatics Grid's (CaBIG) Tissue Banking and Pathology Taskforce's tool for specimen storage and distribution, CaTISSUE. Clinical data annotations are to be done through customized data entry forms. Customized data entry forms for pediatric oncology based on the College of American Pathology (CAP) cancer protocols are to be created and linked to SNOMED and LOINC concept codes suitable for standard data interchange. Additional clinical data from the Regenstrief Medical Records System can also be used through the applications generated for the Shared Pathology Information Network. The experimental data annotation currently consists of gene expression data and proteomics mass spectroscopy data. The analysis toolbox consists of commonly used algorithms hosted on a scalable computing server platform. All analysis workflows are catalogued and are available for reuse for future research projects. Temporal and multidimensional visualization tools allow clinicians to better explore and understand large datasets. This standardized information system can be expanded to a campus wide or inter-campus resource.CONCLUSION: An integrated specimen information system can greatly enhance our ability to identify key factors leading to childhood diseases by using powerful computational and visualization tools for co-analyses of high throughput data with clinical data. This standardized system will facilitate intra- and inter-campus collaboration, data sharing, and the gain of greater knowledge.

Data Visualization of Multiparameter Information in Acute Lymphoblastic Leukemia Expands the Ability To Explore Prognostic Factors.

In clinical studies and for patient care, data collections have become more and more complex. Clinical studies often include millions of data points, and even data collections about individual patients can include several thousand data points. To enable searches for meaningful relationships and patterns, and to gain understanding and knowledge of the data, state-of-the-art visualization approaches have to be adapted to the needs of clinicians and clinical researchers, in order to best reveal relevant patterns in the data. However, despite the progress that has been made in the field of information visualization, none of the currently available high dimensional visualization tools are used in clinical research or practice. The goal of this research was to develop visualization tools that allow clinical researchers to explore multidimensional datasets, as well as temporal clinical datasets. The dataset used for this presentation involves 300 pediatric patients with acute lymphoblastic leukemia diagnosed at Indiana University between 1992 and 2000. Clinical and laboratory data were extracted electronically from the Regenstrief Medical Records System. The dosages of all medications during the treatment period were extracted from the patients' charts. This information was supplemented, for a subset of 78 patients for whom Indiana Medicaid claims data were available, with actual fill dates and quantities dispensed. Cytogenetic data were extracted from the clinical genetic database, and immunophenotype data were extracted from the pathology database at Indiana University. Temporal patient data, such as laboratory data, prescription fill dates, and medication dosage, are visualized through custom-designed multiple layer graphics. The visualization tools developed allow the user to interactively visualize and query the data. For exploratory analysis, the application offers an overview of the data through visual representations such as parallel coordinates and matrix methods. The user can interact with the data set in diverse ways, e.g, the order in which the variables are visualized can be changed; interactivity augments the insight that can be gained from visually exploring such data. The visualizations are dynamically linked, so that the user can obtain coordinate views of the data. Dynamic querying interactively filters data in all views. In addition, the user can highlight or select a subset of data elements in one view and thereby highlight data for the same subset in other views. For example, we show that patient data with a specific pattern in the parallel coordinate view can be selected, and then clinical, laboratory, and prescription data for the entire treatment period can be viewed through multiple layer graphics. In summary, the adaptation of temporal and multidimensional visualization tools to clinical data allows clinicians or clinical researchers to better explore these datasets. These tools improve understanding of the complex prognostic features of acute lymphoblastic leukemia, including type of leukemia, initial risk factors, therapy, adherence to therapy, and host factors that affect tolerance of therapy.

Analysis of pharmacokinetics, pharmacodynamics, and pharmacogenomics data sets using VizStruct, a novel multidimensional visualization technique.

Data visualization techniques for the pharmaceutical sciences have not been extensively investigated. The purpose of this study was to evaluate the usefulness of VizStruct, a multidimensional visualization tool, for applications in pharmacokinetics, pharmacodynamics, and pharmacogenomics. The VizStruct tool uses the first harmonic of the discrete Fourier transform to map multidimensional data to two dimensions for visualization. The mapping was used to visualize several published pharmacokinetic, pharmacodynamic, and pharmacogenomic data sets. The VizStruct approach was evaluated using simulated population pharmacokinetics data sets, the data from Dalen and colleagues (Clin. PharmacoL Ther. 63:444-452, 1998) on the kinetics of nortriptyline and its 10-hydroxynortriptyline metabolite in subjects with differing number of copies of the CYP2D6, and the gene expression profiling data of Bohen and colleagues (Proc. Natl. Acad. Sci. USA 100:1926-1930, 2003) on follicular lymphoma patients responsive and nonresponsive to rituximab. The VizStruct mapping preserves the key characteristics of multidimensional data in two dimensions in a manner that facilitates visualization. The mapping is computationally efficient and can be used for cluster detection and class prediction in pharmaceutical data sets. The VizStruct visualization succinctly summarized the salient similarities and differences in the nortriptyline and 10-hydroxynortriptyline pharmacokinetic profiles in subjects with increasing number of CYP2D6 gene copies. In the simulated population pharmacokinetic data sets, it was capable of discriminating the subtle differences between pharmacokinetic profiles derived from 1- and 2-compartment models with the same area under the curve. The two-dimensional VizStruct mapping computed from a subset of 102 informative genes from the Bohen and colleagues data set effectively separated the rituximab responder, rituximab nonresponder, and control subject groups. The VizStruct approach is a computationally efficient and effective approach for visualizing complex, multidimensional data sets. It could have many useful applications in the pharmaceutical sciences.

Application of VisAD in hydrological modeling and simulation

For visualization of distributed hydrological phenomena, a multidimensional visualization tool such as VisAD embeds sophisticated visualization techniques in hydrological models. Heterogeneous hydrological properties can be visualized draped on top of a digital elevation model (DEM), in order to model parametrization or results. This article discusses how the VisAD library was applied to hydrology related information in the headwater catchments of the Schmücker Graben and the Steinbach utilizing DEMViewer.