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. We applied this novel algorithmic approach on a real-world maize data set . Our results demonstrate the ability of our approach to delineate emergent behavioramong subpopulations, as dictated by one or more environmental factors; notably, our approach shows how the environment plays a key role in determining the phenotypic behaviorof one of the two genotypes. Source code for our implementation and test dataare freely available with detailed instructions at URL
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 . The resulting high dimensional data represent a treasure trove of information for providing an in-depth understanding of how multiple factors interact and contribute to the overall growth and behavior of different genotypes. However, computational tools that can parse through such complex data and aid in extracting plausible hypotheses 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 test and 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. Notably, our approach shows how environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. 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.
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present an open source software which has interactive visualization capabilities to facilitate data navigation and hypothesis formulation. We test and evaluate Hyppo-X on two
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plant (maize) data sets
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emergent behavioramong subpopulations, as dictated by one or more environmental factors; notably,
divergent subpopulation-level behavior. Notably,
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the environment plays a key role in determining the phenotypic behaviorof
environmental factors could influence phenotypic behavior, and how that effect varies across different genotypes and different time scales. To the best of our knowledge, this effort provides
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two genotypes. Source code for our implementation and test dataare freely available with detailed instructions at URL
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.