Fig. 3

Comparison between PCA and t-SNE dimensional reduction methods using the 3D ‘S’ shape in left panel. Note that PCA is a projection that aims at maintaining the maximum variance, whereas t-SNE keeps local similarity in the low dimensional space. For instance, PCA projection clearly maintains the original ‘S’ shape, where the third dimension is lost. In contrast, the plot produced by t-SNE is better at representing local relative distances, where the first dimension reproduces the contour of the shape and the second dimension, the relative position within that part of the contour. As a result of different targets, very different pictures emerge. Implications for phenomics are to be explored. Plot using scikit [53], slightly modified from J. Vanderplas code (https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html)