Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data

Methun Kamruzzaman; Ananth Kalyanaraman; Bala Krishnamoorthy; Stefan Hey; Pat Schnable

doi:10.1109/TCBB.2019.2947500

Back

Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data

Journal article

Open access

Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data

Methun Kamruzzaman, Ananth Kalyanaraman, Bala Krishnamoorthy, Stefan Hey and Pat Schnable

IEEE/ACM transactions on computational biology and bioinformatics, pp.1-1

10/22/2019

DOI: https://doi.org/10.1109/TCBB.2019.2947500

Handle:

https://hdl.handle.net/2376/113387

Files and links (1)

url

https://doi.org/10.1109/TCBB.2019.2947500View

Published (Version of record) Open

Abstract

visualization

hypothesis extraction

Data visualization

topological data analysis

Tools

Feature extraction

Agriculture

Biology

Topology

Computational phenomics

Data mining

graph algorithms

Phenomics is an emerging branch of modern biology that uses high throughput phenotyping tools to capture multiple environmental and phenotypic traits, often at massive spatial and temporal scales. However, computational tools that can parse through such complex data are currently lacking. In this paper, we present Hyppo-X, a new algorithmic approach to visually explore complex phenomics data and in the process characterize the role of environment on phenotypic traits. We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data. We present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We evaluate Hyppo-X on two real-world plant (maize) data sets. Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. To the best of our knowledge, this effort provides one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.

Metrics

13 Record Views

See more details

Details

Title: Hyppo-X: A Scalable Exploratory Framework for Analyzing Complex Phenomics Data
Creators: Methun Kamruzzaman - Electrical Engineering and Computer Science, Washington State University College of Arts and Sciences, 187873 Pullman, Washington United States 99164-2752 (e-mail: md.kamruzzaman@wsu.edu)
Ananth Kalyanaraman - School of EECS, Washington State University, Pullman, Washington United States 99164-2752 (e-mail: ananth@wsu.edu)
Bala Krishnamoorthy - Department of Mathematics and Statistics, Washington State University, 6760 Vancouver, Washington United States (e-mail: kbala@wsu.edu)
Stefan Hey - Department of Agronomy, Iowa State University, 1177 Ames, Iowa United States (e-mail: shey@iastate.edu)
Pat Schnable - Department of Agronomy, Iowa State University, 1177 Ames, Iowa United States (e-mail: schnable@iastate.edu)
Publication Details: IEEE/ACM transactions on computational biology and bioinformatics, pp.1-1
Academic Unit: Mathematics and Statistics, Department of; Electrical Engineering and Computer Science, School of
Publisher: IEEE
Grant note: 1819229 / Division of Mathematical Sciences (10.13039/100000121) 1661348 / Division of Biological Infrastructure (10.13039/100000153)
Identifiers: 99900547444501842
Language: English
Resource Type: Journal article