  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.
