PointCloudXplore: a visualization tool for 3D gene expressiondata

Rübel, Oliver; Weber, Günther; Keränen, Soile V. E.; Fowlkes, C. C.; Hendriks, Cris L. Luengo; Simirenko, Lisa; Shah, Nameeta; Biggn, Mark D.; Hagen, Hans; Sudar, Damir; Malik, Jitendra; Knowles, David; Hamann, Bernd

doi:10.2172/919265

Cited by 5 publications

(2 citation statements)

References 18 publications

(5 reference statements)

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“…Some of the early work to integrate both spatial and non-spatial visualizations includes [10, 5, 21], which, in addition to 3D views, include mathematical abstractions of the data. More recently, bioinformatics has provided a constant stream of datasets that contain both spatial and non-spatial data to be visualized [37, 49, 26]. …”

Section: Related Workmentioning

confidence: 99%

GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

Maries

Mays

Hunt

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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We present the design of a novel framework for the visual integration, comparison, and exploration of correlations in spatial and non-spatial geriatric research data. These data are in general high-dimensional and span both the spatial, volumetric domain – through magnetic resonance imaging volumes – and the non-spatial domain, through variables such as age, gender, or walking speed. The visual analysis framework blends medical imaging, mathematical analysis and interactive visualization techniques, and includes the adaptation of Sparse Partial Least Squares and iterated Tikhonov Regularization algorithms to quantify potential neurology-mobility connections. A linked-view design geared specifically at interactive visual comparison integrates spatial and abstract visual representations to enable the users to effectively generate and refine hypotheses in a large, multidimensional, and fragmented space. In addition to the domain analysis and design description, we demonstrate the usefulness of this approach on two case studies. Last, we report the lessons learned through the iterative design and evaluation of our approach, in particular those relevant to the design of comparative visualization of spatial and non-spatial data.

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Section: Related Workmentioning

confidence: 99%

GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

Maries

Mays

Hunt

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…Applications range from multidimensional geometry [10,11], GIS/remote sensing [12], data mining [13], currency and economic analysis [14]. These concepts have had success in the visual data exploration and analysis of particle-based data in life sciences data [15][16][17], and multiple teams concurrently and explored applying these concepts to fusion simulation data [18].…”

Section: Multidimensional Data Display: Histogram-based Parallel Coormentioning

confidence: 99%

Occam's razor and petascale visual data analysis

Bethel

Johnson

Ahern

et al. 2009

J. Phys.: Conf. Ser.

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One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today's largest platforms to address the challenges posed by large data with techniques with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena.

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Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

Rübel¹,

Ahern²,

Bethel³

et al. 2010

Procedia Computer Science

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Knowledge discovery from large and complex scientific data is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the growing number of data dimensions and data objects presents tremendous challenges for effective data analysis and data exploration methods and tools. The combination and close integration of methods from scientific visualization, information visualization, automated data analysis, and other enabling technologies —such as efficient data management— supports knowledge discovery from multi-dimensional scientific data. This paper surveys two distinct applications in developmental biology and accelerator physics, illustrating the effectiveness of the described approach.

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PointCloudXplore: a visualization tool for 3D gene expressiondata

Cited by 5 publications

References 18 publications

GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

GRACE: A Visual Comparison Framework for Integrated Spatial and Non-Spatial Geriatric Data

Occam's razor and petascale visual data analysis

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data

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