  This paper presents two approaches to quantifying and visualizing variation
in datasets of trees. The first approach localizes subtrees in which
significant population differences are found through hypothesis testing and
sparse classifiers on subtree features. The second approach visualizes the
global metric structure of datasets through low-distortion embedding into
hyperbolic planes in the style of multidimensional scaling. A case study is
made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary
Disease.
