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BMC Bioinformatics. 2017 Feb 15;18(Suppl 2):65. doi: 10.1186/s12859-016-1445-3.

Generalized box-plot for root growth ensembles.

Author information

1
TU WIEN, Karlsplatz 13, Vienna, 1040, Austria. vad@cg.tuwien.ac.at.
2
ICMC - University of São Paulo, São Carlos, 15260, Brazil.
3
Gregor Mendel Institute of Molecular Plant Biology GmbH, Dr. Bohr-Gasse 3, Vienna, 1030, Austria.
4
TU WIEN, Karlsplatz 13, Vienna, 1040, Austria.

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.

KEYWORDS:

Bioinformatics visualization; Curve ensembles; Uncertainty visualization

PMID:
28251866
PMCID:
PMC5333180
DOI:
10.1186/s12859-016-1445-3
[Indexed for MEDLINE]
Free PMC Article

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