  High-throughput technologies to collect field data have made observations
possible at scale in several branches of life sciences. The data collected can
range from the molecular level (genotypes) to physiological (phenotypic traits)
and environmental observations (e.g., weather, soil conditions). These vast
swathes of data, collectively referred to as phenomics data, represent a
treasure trove of key scientific knowledge on the dynamics of the underlying
biological system. However, extracting information and insights from these
complex datasets remains a significant challenge owing to their
multidimensionality and lack of prior knowledge about their complex structure.
In this paper, we present Pheno-Mapper, an interactive toolbox for the
exploratory analysis and visualization of large-scale phenomics data. Our
approach uses the mapper framework to perform a topological analysis of the
data, and subsequently render visual representations with built-in data
analysis and machine learning capabilities. We demonstrate the utility of this
new tool on real-world plant (e.g., maize) phenomics datasets. In comparison to
existing approaches, the main advantage of Pheno-Mapper is that it provides
rich, interactive capabilities in the exploratory analysis of phenomics data,
and it integrates visual analytics with data analysis and machine learning in
an easily extensible way. In particular, Pheno-Mapper allows the interactive
selection of subpopulations guided by a topological summary of the data and
applies data mining and machine learning to these selected subpopulations for
in-depth exploration.
