How to visualize high‐dimensional data.

This article discusses the visualization of high-dimensional data in the field of physiology. The authors emphasize the importance of clarifying the axes and variables represented in diagrams to ensure accurate interpretation. They explain that traditional methods like principal component analysis (PCA) may not be sufficient for high-dimensional data and introduce nonlinear techniques like t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). These methods allow for the visualization of complex data and have been widely used in various fields, including neurophysiology, immunology, cancer research, and infectious diseases. The authors caution that the interpretation of these plots requires careful consideration due to the nonlinear transformations involved. They also mention ongoing efforts to improve these methods. Overall, the article highlights the need for clear explanations of high-dimensional plots in presentations and acknowledges the interdisciplinary nature of physiology and the rapid development of methods in the field. [Extracted from the article]