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One of the grand challenges of modern biology is to understand how genotypes (G) and environments (E) interact to affect phenotypes (P), i.e., G × E - P . Phenomics is the emerging field that aims to study large and complex data sets encompassing combinations of genotypes, environments, phenotypes readings. A phenomenon of crucial interest in this context is that of divergent subpopulations, i.e., how certain subgroups of the population show differential behavior under different types of environmental conditions. We consider the fundamental task of identifying such "interesting" subpopulation-level behavior by analyzing high-dimensional phenomics data sets from a large and diverse population. However, delineation of such subpopulations is a challenging task due to the large size, high dimensionality, and complexity of phenomics data. We present a new framework to extract such subpopulation-level information from phenomics data. Our approach is based on principles from algebraic topology, a branch of mathematics that studies shapes and structure of data in a robust manner. In particular, our framework identifies and quantifies "flares", which are structural branching features in data that characterize divergent behavior of subpopulations, in an unsupervised manner. We present algorithms to detect and rank flares, and demonstrate the utility of the proposed framework on two real-world plant phenomics data sets.

Detecting Divergent Subpopulations in Phenomics Data using Interesting Flares