Background: In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often time dependent root phenotypes. Results: In this work, we present visual summaries of root ensembles by aggregating root images with identical genetic characteristics. We use the generalized box plot concept with a new formulation of data depth. In addition to spatial distributions, we created a visual representation to encode temporal distributions associated with the development of root individuals. Conclusions: The new formulation of data depth allows for much faster implementation close to interactive frame rates. This allows us to present the statistics from bootstrapping that characterize the root sample set quality. As a positive side effect of the new data-depth formulation we are able to define the geometric median for the curve ensemble, which was well received by the domain experts.
|Original language||English (US)|
|State||Published - Feb 15 2017|
Bibliographical noteFunding Information:
This project has been funded by the Vienna Science and Technology Fund (WWTF) through project VRG11-010 and also supported by EC Marie Curie Career Integration Grant through project PCIG13-GA-2013-618680. Work in the Busch lab is supported by funds from the Austrian Academy of Science through the Gregor Mendel Institute of Plant Molecular Biology (GMI). Publication costs were funded by the Vienna Science and Technology Fund (WWTF project VRG11-010) and the Gregor Mendel Institute of Plant Molecular Biology. These two funding sources have contributed equally.
© 2017 The Author(s).
- Bioinformatics visualization
- Curve ensembles
- Uncertainty visualization
ASJC Scopus subject areas
- Structural Biology
- Molecular Biology
- Computer Science Applications
- Applied Mathematics