24,873 research outputs found
Visualization of the Phosphoproteomic Data from AfCS with the Google Motion Chart Gadget
Results from multivariate molecular biological experiments become increasingly complex. Hence, the challenge of projecting high-dimensional data onto few dimensions for effective data visualization is becoming increasingly important in Systems Biology. Effective data visualization can summarize the activity of many variables over time as well as display relationships between variables. Dynamic interactive visualization tools can provide scientists with ways of visually identifying relationship and patterns, and improve communication of results on the web and in presentations. For this, interactive systems with animation have great potential since they add dimensions to static images limited to two dimensions. Interactivity and animation is particularly useful for showing time-series trends in multi-dimensional data. The Flash-based Motion Chart Google Gadget available through GoogleDocs is a recent advance in multi-dimensional data visualization. The Motion Chart Gadget is a component of the Trendalyzer software, which was developed for web-based animation of statistical results. Here we demonstrate the use of this Gadget to visualize molecular biological data, the phosphoproteomics results published on the Data Center of the Signaling Gateway web-site
Visual support for the understanding of simulation processes
Current visualization systems are typically based on the concept of interactive post-processing. This decoupling of data visualiza-tion from the process of data generation offers a flexible applica-tion of visualization tools. It can also lead, however, to information loss in the visualization. Therefore, a combination of the visual-ization of the data generating process with the visualization of the produced data offers significant support for the understanding of the abstract data sets as well as the underlying process. Due to the application-specific characteristics of data generating processes, the task requires tailored visualization concepts. In this work, we focus on the application field of simulating biochemical reaction networks as discrete-event systems. These stochastic processes generate multi-run and multivariate time-series, which are analyzed and compared on three different process levels: model, experiment, and the level of multi-run simulation data, each associated with a broad range of analysis goals. To meet these challenging characteristics, we present visualization concepts specifically tailored to all three process levels. The fundament of all three visualization concepts is a compact view that relates the multi-run simulation data to the characteristics of the model structure and the experiment. The view provides the visualization at the experi-ment level. The visualization at the model level coordinates mul-tiple instances of this view for the comparison of experiments. At the level of multi-run simulation data, the views gives an overview on the data, which can be analyzed in detail in time-series views suited for the analysis goals. Although we derive our visualization concepts for one concrete simulation process, our general concept of tailoring the visualization concepts to process levels is generally applicable for the visualization of simulation processes
Visual and interactive exploration of point data
Point data, such as Unit Postcodes (UPC), can provide very detailed information at fine
scales of resolution. For instance, socio-economic attributes are commonly assigned to
UPC. Hence, they can be represented as points and observable at the postcode level.
Using UPC as a common field allows the concatenation of variables from disparate data
sources that can potentially support sophisticated spatial analysis. However, visualising
UPC in urban areas has at least three limitations. First, at small scales UPC occurrences
can be very dense making their visualisation as points difficult. On the other hand,
patterns in the associated attribute values are often hardly recognisable at large scales.
Secondly, UPC can be used as a common field to allow the concatenation of highly
multivariate data sets with an associated postcode. Finally, socio-economic variables
assigned to UPC (such as the ones used here) can be non-Normal in their distributions
as a result of a large presence of zero values and high variances which constrain their
analysis using traditional statistics.
This paper discusses a Point Visualisation Tool (PVT), a proof-of-concept system
developed to visually explore point data. Various well-known visualisation techniques
were implemented to enable their interactive and dynamic interrogation. PVT provides
multiple representations of point data to facilitate the understanding of the relations
between attributes or variables as well as their spatial characteristics. Brushing between
alternative views is used to link several representations of a single attribute, as well as
to simultaneously explore more than one variable. PVT’s functionality shows how the
use of visual techniques embedded in an interactive environment enable the exploration
of large amounts of multivariate point data
Viewpoints: A high-performance high-dimensional exploratory data analysis tool
Scientific data sets continue to increase in both size and complexity. In the
past, dedicated graphics systems at supercomputing centers were required to
visualize large data sets, but as the price of commodity graphics hardware has
dropped and its capability has increased, it is now possible, in principle, to
view large complex data sets on a single workstation. To do this in practice,
an investigator will need software that is written to take advantage of the
relevant graphics hardware. The Viewpoints visualization package described
herein is an example of such software. Viewpoints is an interactive tool for
exploratory visual analysis of large, high-dimensional (multivariate) data. It
leverages the capabilities of modern graphics boards (GPUs) to run on a single
workstation or laptop. Viewpoints is minimalist: it attempts to do a small set
of useful things very well (or at least very quickly) in comparison with
similar packages today. Its basic feature set includes linked scatter plots
with brushing, dynamic histograms, normalization and outlier detection/removal.
Viewpoints was originally designed for astrophysicists, but it has since been
used in a variety of fields that range from astronomy, quantum chemistry, fluid
dynamics, machine learning, bioinformatics, and finance to information
technology server log mining. In this article, we describe the Viewpoints
package and show examples of its usage.Comment: 18 pages, 3 figures, PASP in press, this version corresponds more
closely to that to be publishe
What May Visualization Processes Optimize?
In this paper, we present an abstract model of visualization and inference
processes and describe an information-theoretic measure for optimizing such
processes. In order to obtain such an abstraction, we first examined six
classes of workflows in data analysis and visualization, and identified four
levels of typical visualization components, namely disseminative,
observational, analytical and model-developmental visualization. We noticed a
common phenomenon at different levels of visualization, that is, the
transformation of data spaces (referred to as alphabets) usually corresponds to
the reduction of maximal entropy along a workflow. Based on this observation,
we establish an information-theoretic measure of cost-benefit ratio that may be
used as a cost function for optimizing a data visualization process. To
demonstrate the validity of this measure, we examined a number of successful
visualization processes in the literature, and showed that the
information-theoretic measure can mathematically explain the advantages of such
processes over possible alternatives.Comment: 10 page
Using treemaps for variable selection in spatio-temporal visualisation
We demonstrate and reflect upon the use of enhanced treemaps that incorporate spatial and temporal ordering for exploring a large multivariate spatio-temporal data set. The resulting data-dense views summarise and simultaneously present hundreds of space-, time-, and variable-constrained subsets of a large multivariate data set in a structure that facilitates their meaningful comparison and supports visual analysis. Interactive techniques allow localised patterns to be explored and subsets of interest selected and compared with the spatial aggregate. Spatial variation is considered through interactive raster maps and high-resolution local road maps. The techniques are developed in the context of 42.2 million records of vehicular activity in a 98 km(2) area of central London and informally evaluated through a design used in the exploratory visualisation of this data set. The main advantages of our technique are the means to simultaneously display hundreds of summaries of the data and to interactively browse hundreds of variable combinations with ordering and symbolism that are consistent and appropriate for space- and time- based variables. These capabilities are difficult to achieve in the case of spatio-temporal data with categorical attributes using existing geovisualisation methods. We acknowledge limitations in the treemap representation but enhance the cognitive plausibility of this popular layout through our two-dimensional ordering algorithm and interactions. Patterns that are expected (e.g. more traffic in central London), interesting (e.g. the spatial and temporal distribution of particular vehicle types) and anomalous (e.g. low speeds on particular road sections) are detected at various scales and locations using the approach. In many cases, anomalies identify biases that may have implications for future use of the data set for analyses and applications. Ordered treemaps appear to have potential as interactive interfaces for variable selection in spatio-temporal visualisation. Information Visualization (2008) 7, 210-224. doi: 10.1057/palgrave.ivs.950018
Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts
Getting the overall picture of how a large number of ego-networks evolve is a
common yet challenging task. Existing techniques often require analysts to
inspect the evolution patterns of ego-networks one after another. In this
study, we explore an approach that allows analysts to interactively create
spatial layouts in which each dot is a dynamic ego-network. These spatial
layouts provide overviews of the evolution patterns of ego-networks, thereby
revealing different global patterns such as trends, clusters and outliers in
evolution patterns. To let analysts interactively construct interpretable
spatial layouts, we propose a data transformation pipeline, with which analysts
can adjust the spatial layouts and convert dynamic egonetworks into event
sequences to aid interpretations of the spatial positions. Based on this
transformation pipeline, we developed Segue, a visual analysis system that
supports thorough exploration of the evolution patterns of ego-networks.
Through two usage scenarios, we demonstrate how analysts can gain insights into
the overall evolution patterns of a large collection of ego-networks by
interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and
Technology (IEEE VAST 2018
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