48,576 research outputs found
Visualizing the Structure of Large Trees
This study introduces a new method of visualizing complex tree structured
objects. The usefulness of this method is illustrated in the context of
detecting unexpected features in a data set of very large trees. The major
contribution is a novel two-dimensional graphical representation of each tree,
with a covariate coded by color. The motivating data set contains three
dimensional representations of brain artery systems of 105 subjects. Due to
inaccuracies inherent in the medical imaging techniques, issues with the
reconstruction algo- rithms and inconsistencies introduced by manual
adjustment, various discrepancies are present in the data. The proposed
representation enables quick visual detection of the most common discrepancies.
For our driving example, this tool led to the modification of 10% of the artery
trees and deletion of 6.7%. The benefits of our cleaning method are
demonstrated through a statistical hypothesis test on the effects of aging on
vessel structure. The data cleaning resulted in improved significance levels.Comment: 17 pages, 8 figure
Algorithms for Visualizing Phylogenetic Networks
We study the problem of visualizing phylogenetic networks, which are
extensions of the Tree of Life in biology. We use a space filling visualization
method, called DAGmaps, in order to obtain clear visualizations using limited
space. In this paper, we restrict our attention to galled trees and galled
networks and present linear time algorithms for visualizing them as DAGmaps.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Visualizing Evolving Trees
Evolving trees arise in many real-life scenarios from computer file systems
and dynamic call graphs, to fake news propagation and disease spread. Most
layout algorithms for static trees, however, do not work well in an evolving
setting (e.g., they are not designed to be stable between time steps). Dynamic
graph layout algorithms are better suited to this task, although they often
introduce unnecessary edge crossings. With this in mind we propose two methods
for visualizing evolving trees that guarantee no edge crossings, while
optimizing (1) desired edge length realization, (2) layout compactness, and (3)
stability. We evaluate the two new methods, along with four prior approaches
(two static and two dynamic), on real-world datasets using quantitative
metrics: stress, desired edge length realization, layout compactness,
stability, and running time. The new methods are fully functional and available
on github
Mapping Topographic Structure in White Matter Pathways with Level Set Trees
Fiber tractography on diffusion imaging data offers rich potential for
describing white matter pathways in the human brain, but characterizing the
spatial organization in these large and complex data sets remains a challenge.
We show that level set trees---which provide a concise representation of the
hierarchical mode structure of probability density functions---offer a
statistically-principled framework for visualizing and analyzing topography in
fiber streamlines. Using diffusion spectrum imaging data collected on
neurologically healthy controls (N=30), we mapped white matter pathways from
the cortex into the striatum using a deterministic tractography algorithm that
estimates fiber bundles as dimensionless streamlines. Level set trees were used
for interactive exploration of patterns in the endpoint distributions of the
mapped fiber tracks and an efficient segmentation of the tracks that has
empirical accuracy comparable to standard nonparametric clustering methods. We
show that level set trees can also be generalized to model pseudo-density
functions in order to analyze a broader array of data types, including entire
fiber streamlines. Finally, resampling methods show the reliability of the
level set tree as a descriptive measure of topographic structure, illustrating
its potential as a statistical descriptor in brain imaging analysis. These
results highlight the broad applicability of level set trees for visualizing
and analyzing high-dimensional data like fiber tractography output
TreeViewJ: An Application for Viewing and Analyzing Phylogenetic Trees
BACKGROUND. Phylogenetic trees are widely used to visualize evolutionary relationships between different organisms or samples of the same organism. There exists a variety of both free and commercial tree visualization software available, but limitations in these programs often require researchers to use multiple programs for analysis, annotation, and the production of publication-ready images. RESULTS. We present TreeViewJ, a Java tool for visualizing, editing and analyzing phylogenetic trees. The software allows researchers to color and change the width of branches that they wish to highlight, and add names to nodes. If collection dates are available for taxa, the software can map them onto a timeline, and sort the tree in ascending or descending date order. CONCLUSION. TreeViewJ is a tool for researchers to visualize, edit, "decorate," and produce publication-ready images of phylogenetic trees. It is open-source, and released under an GPL license, and available at http://treeviewj.sourceforge.net
Visualizing Decision Trees and Forests using Radial Trees
Data visualization has become a big representation of many company’s data and schedules. Now people are not using just simple bar graphs and pie charts in business meetings but utilizing other fields of study and even more complex graphs. By using multiple visualizations to display their results and projects, it is letting more outside people understand what they are working on and can lead to more viewpoints on the topic being displayed. Also, schedules for projects are now being displayed visually so the workers can see how much time each part of their project is going to take. With this increase in visualization, decision trees are now starting to become visualized. Decision trees zero in on object classification and find a way to label or group those objects. In this paper, complex decision trees that can be hard to understand for everyone will be visualized using radial trees. The program will take the advantages that radial trees offer for data and create an interactive display for users of decision trees and forests
RELT - Visualizing trees on mobile devices
The small screens on increasingly used mobile devices challenge the traditional visualization methods designed for desktops. This paper presents a method called "Radial Edgeless Tree" (RELT) for visualizing trees in a 2-dimensional space. It combines the existing connection tree drawing with the space-filling approach to achieve the efficient display of trees in a small geometrical area, such as the screen that are commonly used in mobile devices. We recursively calculate a set of non-overlapped polygonal nodes that are adjacent in the hierarchical manner. Thus, the display space is fully used for displaying nodes, while the hierarchical relationships among the nodes are presented by the adjacency (or boundary-sharing) of the nodes. It is different from the other traditional connection approaches that use a node-link diagram to present the parent-child relationships which waste the display space. The hierarchy spreads from north-west to south-east in a top-down manner which naturally follows the traditional way of human perception of hierarchies. We discuss the characteristics, advantages and limitations of this new technique and suggestions for future research. © Springer-Verlag Berlin Heidelberg 2007
Uncertainty in phylogenetic tree estimates
Estimating phylogenetic trees is an important problem in evolutionary
biology, environmental policy and medicine. Although trees are estimated, their
uncertainties are discarded by mathematicians working in tree space. Here we
explicitly model the multivariate uncertainty of tree estimates. We consider
both the cases where uncertainty information arises extrinsically (through
covariate information) and intrinsically (through the tree estimates
themselves). The importance of accounting for tree uncertainty in tree space is
demonstrated in two case studies. In the first instance, differences between
gene trees are small relative to their uncertainties, while in the second, the
differences are relatively large. Our main goal is visualization of tree
uncertainty, and we demonstrate advantages of our method with respect to
reproducibility, speed and preservation of topological differences compared to
visualization based on multidimensional scaling. The proposal highlights that
phylogenetic trees are estimated in an extremely high-dimensional space,
resulting in uncertainty information that cannot be discarded. Most
importantly, it is a method that allows biologists to diagnose whether
differences between gene trees are biologically meaningful, or due to
uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical
Statistic
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