Level set trees with enhanced marginal density visualization

We study level set tree methods to analyze and visualize multivariate data. The probability density function of the underlying distribution is estimated using a kernel density estimator, and the density estimate is visualized using level set trees. These trees can be used to analyze the mode structure of a function. We show how level set trees can be used to enhance more traditional density function visualization tools, like marginal densities and slices of the density. The method is applied to flow cytometry data.