Enhancing Dimension-Reduced Scatter Plots with Class and Feature Centroids
This addresses interpretability issues in biomedical data visualization, but it is incremental as it builds on existing dimension reduction methods.
The study tackled the challenge of interpreting axes in dimension-reduced scatter plots by calculating and overlaying class and feature centroids, demonstrating increased interpretability with neurogenetic disease phenotype data.
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.