RT Journal Article
SR Electronic
T1 Toward A Scalable Exploratory Framework for Complex High-Dimensional Phenomics Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 159954
DO 10.1101/159954
A1 Methun Kamruzzaman
A1 Ananth Kalyanaraman
A1 Bala Krishnamoorthy
A1 Patrick Schnable
YR 2017
UL http://biorxiv.org/content/early/2017/07/05/159954.abstract
AB Motivation Phenomics is an emerging branch of modern biology, which uses high throughput phenotyping tools to capture multiple environment and phenotypic trait measurements, at a massive scale. The resulting high dimensional data sets represent a treasure trove of information for providing an indepth understanding of how multiple factors interact and contribute to control the growth and behavior of different plant crop genotypes. However, computational tools that can parse through such high dimensional data sets and aid in extracting plausible hypothesis are currently lacking. In this paper, we present a new algorithmic approach to effectively decode and characterize the role of environment on phenotypic traits, from complex phenomic data. To the best of our knowledge, this effort represents the first application of topological data analysis on phenomics data.Results We applied this novel algorithmic approach on a real-world maize data set. Our results demonstrate the ability of our approach to delineate emergent behavior among subpopulations, as dictated by one or more environmental factors; notably, our approach shows how the environment plays a key role in determining the phenotypic behavior of one of the two genotypes.Availability Downloadable Source code and test data are freely available with instruction set at https://xperthut.github.io/HYPPO-X.contact ananth@eecs.wsu.eduSupplementary Information Supplementary data are available at Bioinformatics online.