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Phenomics is an emerging branch of modern biology that uses high throughputphenotyping tools to capture multiple environmental and phenotypic traits,often at massive spatial and temporal scales. The resulting high dimensionaldata represent a treasure trove of information for providing an in-depthunderstanding of how multiple factors interact and contribute to the overallgrowth and behavior of different genotypes. However, computational tools thatcan parse through such complex data and aid in extracting plausible hypothesesare currently lacking. In this paper, we present Hyppo-X, a new algorithmicapproach to visually explore complex phenomics data and in the processcharacterize the role of environment on phenotypic traits. We model the problemas one of unsupervised structure discovery, and use emerging principles fromalgebraic topology and graph theory for discovering higher-order structures ofcomplex phenomics data. We present an open source software which hasinteractive visualization capabilities to facilitate data navigation andhypothesis formulation. We test and evaluate Hyppo-X on two real-world plant(maize) data sets. Our results demonstrate the ability of our approach todelineate divergent subpopulation-level behavior. Notably, our approach showshow environmental factors could influence phenotypic behavior, and how thateffect varies across different genotypes and different time scales. To the bestof our knowledge, this effort provides one of the first approaches tosystematically formalize the problem of hypothesis extraction for phenomicsdata. Considering the infancy of the phenomics field, tools that help usersexplore complex data and extract plausible hypotheses in a data-guided mannerwill be critical to future advancements in the use of such data.

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