  Dimension reduction is increasingly applied to high-dimensional biomedical
data to improve its interpretability. When datasets are reduced to two
dimensions, each observation is assigned an x and y coordinates and is
represented as a point on a scatter plot. A significant challenge lies in
interpreting the meaning of the x and y axes due to the complexities inherent
in dimension reduction. This study addresses this challenge by using the x and
y coordinates derived from dimension reduction to calculate class and feature
centroids, which can be overlaid onto the scatter plots. This method connects
the low-dimension space to the original high-dimensional space. We illustrate
the utility of this approach with data derived from the phenotypes of three
neurogenetic diseases and demonstrate how the addition of class and feature
centroids increases the interpretability of scatter plots.
