A visualization of the best-and worst-in-class reagents is useful to contextualize the scope of the dataset and therefore the extent of the reactome. The selection bias of reagents and clustering of high and low performing reagents can be easily interpreted. Whilst numerous techniques for dimensionality reduction and visualization of high-dimensional space are known, we chose to use PCA as its utility has been widely documented and numerous reliable, user-friendly implementations exist . Additionally, PCA is more interpretable than UMAP (Uniform Manifold Approximation and Projection) or tSNE (t-distributed Stochastic Neighbor Embedding) whose non-linearity necessitate warping the high-dimensional shape of the data during projection; the xy-axes of projection lose the easy interpretability of highest variance (x-axis) / second highest variance (y-axis) that is fundamental to PCA .