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caOmicsV: an R package for visualizing multidimensional cancer genomic data

BMC BIOINFORMATICS 2016年第1期17卷 1-5页

作者： Zhang, Hongen Meltzer, Paul S. Davis, Sean R. NCI Genet Branch Ctr Canc Res NIH Bldg 37Room 613837 Convent Dr Bethesda MD 20892 USA

Background: Translational genomics research in cancers, e.g., International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), has generated large multidimensional datasets from high-throughput technologies. data analysis at multidimensional level will greatly benefit clinical applications of genomic information in diagnosis, prognosis and therapeutics of cancers. To help, tools to effectively visualize integrated multidimensional data are important for understanding and describing the relationship between genomic variations and cancers. Results: We implemented the R package, caOmicsV, to provide methods under R environment to visualize multidimensional cancer genomic data in two layouts: matrix layout and combined biological network and circular layout. Both layouts support to display sample information, gene expression (e.g., RNA and miRNA), DNA methylation, DNA copy number variations, and summarized data. A set of supplemental functions are included in the caOmicsV package to help users in generation of plot data sets from multiple genomic datasets with given gene names and sample names. Default plot methods for both layouts for easy use are also implemented. Conclusion: caOmicsV package provides an easy and flexible way to visualize integrated multidimensional cancer genomic data under R environment.

关键词： Software R package Genomic data visualization multidimensional data visualization

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Z-Glyph: Visualizing outliers in multivariate data

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INFORMATION visualization 2018年第1期17卷 22-40页

作者： Cao, Nan Lin, Yu-Ru Gotz, David Du, Fan Tongji Univ Shanghai Peoples R China Univ Pittsburgh Pittsburgh PA USA Univ N Carolina Chapel Hill NC USA Univ Maryland College Pk MD 20742 USA

Outlier analysis techniques are extensively used in many domains such as intrusion detection. Today, even with the most advanced statistical learning techniques, human judgment still plays an important role in outlier analysis tasks due to the difficulty of defining and collecting outlier examples. This work seeks to tackle this problem by introducing a new visualization design, Z-Glyph, a family of glyphs designed to facilitate human judgment in outlier analysis of multivariate data. By employing a location-scale transformation, a Z-Glyph represents the normal data using regular shapes (e.g. straight line and circle), such that the abnormal data can be revealed when deviating from the regular shapes. Extensive controlled experiment and case studies based on real-world datasets indicate the superior performance of the Z-Glyph family, compared with the baselines, suggesting that the proposed design is able to leverage human perceptional features with statistical characterization. This study contributes to a more fundamental understanding about designing visual representations for revealing outliers in multivariate data, which can be applied as a building block in many domain-specific anomaly detection applications.

关键词： Outlier detection anomaly detection information visualization multidimensional data visualization

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Does design matter when visualizing Big data? An empirical study to investigate the effect of visualization type and interaction use

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JOURNAL OF MANAGEMENT CONTROL 2020年第1-2期31卷 55-95页

作者： Perkhofer, Lisa Walchshofer, Conny Hofer, Peter Univ Appl Sci Upper Austria Controlling Accounting & Finance Steyr Austria

The need for good visualization is increasing, as data volume and complexity expand. In order to work with high volumes of structured and unstructured data, visualizations, supporting the ability of humans to make perceptual inferences, are of the utmost importance. In this regard, a lot of interactive visualization techniques have been developed in recent years. However, little emphasis has been placed on the evaluation of their usability and, in particular, on design characteristics. This paper contributes to closing this research gap by measuring the effects of appropriate visualization use based on data and task characteristics. Further, we specifically test the feature of interaction as it has been said to be an essential component of Big data visualizations but scarcely isolated as an independent variable in experimental research. data collection for the large-scale quantitative experiment was done using crowdsourcing (Amazon Mechanical Turk). The results indicate that both, choosing an appropriate visualization based on task characteristics and using the feature of interaction, increase usability considerably.

关键词： Interactive visualization Usability multidimensional data visualization Visual analytics Big data

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TripVista: Triple Perspective Visual Trajectory Analytics and Its Application on Microscopic Traffic data at a Road Intersection

TripVista: Triple Perspective Visual Trajectory Analytics an...

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4th IEEE Pacific visualization Symposium

作者： Guo, Hanqi Wang, Zuchao Yu, Bowen Zhao, Huijing Yuan, Xiaoru Peking Univ Minist Educ Key Lab Machine Percept Beijing 100871 Peoples R China

ISBN: (纸本)9781612849324

In this paper, we present an interactive visual analytics system, Triple Perspective Visual Trajectory Analytics (TripVista), for exploring and analyzing complex traffic trajectory data. The users are equipped with a carefully designed interface to inspect data interactively from three perspectives (spatial, temporal and multidimensional views). While most previous works, in both visualization and transportation research, focused on the macro aspects of traffic flows, we develop visualization methods to investigate and analyze microscopic traffic patterns and abnormal behaviors. In the spatial view of our system, traffic trajectories with various presentation styles are directly interactive with user brushing, together with convenient pattern exploration and selection through ring-style sliders. Improved ThemeRiver, embedded with glyphs indicating directional information, and multiple scatterplots with time as horizontal axes illustrate temporal information of the traffic flows. Our system also harnesses the power of parallel coordinates to visualize the multi-dimensional aspects of the traffic trajectory data. The above three view components are linked closely and interactively to provide access to multiple perspectives for users. Experiments show that our system is capable of effectively finding both regular and abnormal traffic flow patterns.

关键词： Visual analytics Linked view Spatiotemporal multidimensional data visualization Traffic visualization

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ImAxes: Immersive Axes as Embodied Affordances for Interactive Multivariate data Visualisation 17

ImAxes: Immersive Axes as Embodied Affordances for Interacti...

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30th Annual ACM Symposium on User Interface Software and Technology (UIST)

作者： Cordeil, Maxime Cunningham, Andrew Dwyer, Tim Thomas, Bruce H. Marriott, Kim Monash Univ Melbourne Vic Australia Univ South Australia Adelaide SA Australia

ISBN: (纸本)9781450349819

We introduce ImAxes, an immersive system for exploring multivariate data using fluid, modeless interaction. The basic interface element is an embodied data axis. The user can manipulate these axes like physical objects in the immersive environment and combine them into sophisticated visualisations. The type of visualisation that appears depends on the proximity and relative orientation of the axes with respect to one another, which we describe with a formal grammar. This straight-forward composability leads to a number of emergent visualisations and interactions, which we review, and then demonstrate with a detailed multivariate data analysis use case.

关键词： multidimensional data visualization immersive visualization virtual reality information visualization immersion immersive analytics

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Coordinated Image- and Feature-space visualization for Interactive Magnetic Resonance Spectroscopy Imaging data Analysis 10

Coordinated Image- and Feature-space Visualization for Inter...

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14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 10th International Conference on Information visualization Theory and Applications (IVAPP)

作者： Jawad, Muhammad Molchanov, Vladimir Linsen, Lars Westfalische Wilhelms Univ Munster Munster Germany

ISBN: (纸本)9789897583544

Magnetic Resonance Spectroscopy Imaging (MRSI) is a medical imaging method that measures per voxel a spectrum of signal intensities. It allows for the analysis of chemical compositions within the scanned tissue, which is particularly useful for tumor classification and measuring its infiltration of healthy tissue. Common analysis approaches consider one metabolite concentration at a time to produce intensity maps in the image space, which does not consider all relevant information at hand. We propose a system that uses coordinated views between image-space visualizations and visual representations of the spectral (or feature) space. Coordinated interaction allows for analyzing both aspects and relating the analysis results back to the other for further investigations. We demonstrate how our system can be used to analyze brain tumors.

关键词： multidimensional data visualization Medical visualization Coordinated Views Spectral Imaging Analysis

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Synthetic data Generation and Automated multidimensional data Labeling for AI/ML in General and Circular Coordinates 28

Synthetic Data Generation and Automated Multidimensional Dat...

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28th International Conference on Information Visualisation (IV)

作者： Williams, Alice Kovalerchuk, Boris Cent Washington Univ Dept Comp Sci Ellensburg WA 98926 USA

ISBN: (纸本)9798350380170;9798350380163

Insufficient amounts of available training data is a critical challenge for both development and deployment of artificial intelligence and machine learning (AI/ML) models. This paper proposes a unified approach to both synthetic data generation (SDG) and automated data labeling (ADL) with a unified SDG-ADL algorithm. SDG-ADL uses multidimensional (n-D) representations of data visualized losslessly with General Line Coordinates (GLCs), relying on reversible GLC properties to visualize n-D data in multiple GLCs. This paper demonstrates use of the new Circular Coordinates in Static and Dynamic forms, used with Parallel Coordinates and Shifted Paired Coordinates, since each GLC exemplifies unique data properties, such as interattribute n-D distributions and outlier detection. The approach is interactively implemented in computer software with the Dynamic Coordinates visualization system (DCVis). Results with real data are demonstrated in case studies, evaluating impact on classifiers.

关键词： Synthetic data Generation Automated data Labeling General Line Coordinates Circular Coordinates Parallel Coordinates Shifted Paired Coordinates Tabular AI/ML data multidimensional data visualization Visual Knowledge Discovery

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ImNDT: ImmersiveWorkspace for the Analysis of multidimensional Material data From Non-Destructive Testing 21

ImNDT: ImmersiveWorkspace for the Analysis of Multidimension...

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27th ACM Symposium on Virtual Reality Software and Technology (VRST)

作者： Gall, Alexander Groeller, Eduard Heinzl, Christoph Univ Appl Sci Upper Austria Wels Austria TU Wien Vienna Austria VRVis Vienna Austria

ISBN: (纸本)9781450390927

An analysis of large multidimensional volumetric data as generated by non-destructive testing (NDT) techniques, e.g., X-ray computed tomography (XCT), can hardly be evaluated using standard 2D visualization techniques on desktop monitors. The analysis of fiber-reinforced polymers (FRPs) is currently a time-consuming and cognitively demanding task, as FRPs have a complex spatial structure, consisting of several hundred thousand fibers, each having more than twenty different extracted features. This paper presents ImNDT, a novel visualization system, which offers material experts an immersive exploration of multidimensional secondary data of FRPs. Our system is based on a virtual reality (VR) head-mounted device (HMD) to enable fluid and natural explorations through embodied navigation, the avoidance of menus, and manual mode switching. We developed immersive visualization and interaction methods tailored to the characterization of FRPs, such as a Model in Miniature, a similarity network, and a histo-book. An evaluation of our techniques with domain experts showed advantages in discovering structural patterns and similarities. Especially novices can strongly benefit from our intuitive representation and spatial rendering.

关键词： multidimensional data visualization immersive analytics virtual reality interaction techniques fibre-reinforced polymers

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A Reproducibility Study for Visual MRSI data Analytics 14th

A Reproducibility Study for Visual MRSI Data Analytics

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14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)

作者： Jawad, Muhammad Molchanov, Vladimir Linsen, Lars Westfalische Wilhelms Univ Munster Munster Germany

ISBN: (纸本)9783030415907;9783030415891

Magnetic Resonance Spectroscopy Imaging (MRSI) is a spectral imaging method that measures per voxel spectral information of chemical resonance, from which metabolite concentrations can be computed. In recent work, we proposed a system that uses coordinated views between image-space visualizations and visual representations of the spectral (or feature) space. Coordinated interaction allowed us to analyze all metabolite concentrations together instead of focusing only at single metabolites at a time [8]. In this paper, we want to relate our findings to different results reported in the literature. MRSI is particularly useful for tumor classification and measuring its infiltration of healthy tissue. We compare the metabolite compositions obtained in the various tissues of our data against the compositions reported by other brain tumor studies using a visual analytics approach. It visualizes the similarities in a plot obtained using dimensionality reduction methods. We test our data against various sources to test the reproducibility of the findings.

关键词： Reproducibility multidimensional data visualization Medical visualization Coordinated views Spectral imaging analysis

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Many objective visual analytics: rethinking the design of complex engineered systems

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2013年第1期48卷 201-219页

作者： Woodruff, Matthew J. Reed, Patrick M. Simpson, Timothy W. Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA Penn State Univ Dept Civil & Environm Engn University Pk PA 16802 USA

Many cognitive and computational challenges accompany the design of complex engineered systems. This study proposes the many-objective visual analytics (MOVA) framework as a new approach to the design of complex engineered systems. MOVA emphasizes learning through problem reformulation, enabled by visual analytics and many-objective search. This study demonstrates insights gained by evolving the formulation of a General Aviation Aircraft (GAA) product family design problem. This problem's considerable complexity and difficulty, along with a history encompassing several formulations, make it well-suited to demonstrate the MOVA framework. The MOVA framework results compare a single objective, a two objective, and a ten objective formulation for optimizing the GAA product family. Highly interactive visual analytics are exploited to demonstrate how decision biases can arise for lower dimensional, highly aggregated problem formulations.

关键词： Multi-objective optimization multidimensional data visualization Product family design

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