Generalized box-plot for root growth ensembles

Viktor Vad*, Douglas Cedrim, Wolfgang Busch, Peter Filzmoser, Ivan Viola

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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 languageEnglish (US)
Article number65
JournalBMC BIOINFORMATICS
Volume18
DOIs
StatePublished - Feb 15 2017
Externally publishedYes

Bibliographical note

Funding 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.

Publisher Copyright:
© 2017 The Author(s).

Keywords

  • Bioinformatics visualization
  • Curve ensembles
  • Uncertainty visualization

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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