Fig. 5: Conceptual overview of t-SNE. | Nature Communications

Fig. 5: Conceptual overview of t-SNE.

From: Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data

Fig. 5

A–C t-Distributed Stochastic Neighbor Embedding algorithm. A The high-dimensional dataset needs to be projected into a low-dimensional representation. A, B Pairwise distances of high- and low-dimensional data points are converted into similarities using the joint probability distributions P (Gaussian) and Q (Student-t), respectively. C The low-dimensional representation that best resembles the high-dimensional dataset is found by minimizing the KL divergence between the two joint probability distributions. D, E 2D & 3D t-SNE scatter plots for a dataset composed of lipid concentrations analyzed via the LC-MS in plasma samples of patients with gout (red dots) and healthy subjects (blue dots). Created in BioRender. Demeulemeester, J. (2025) https://BioRender.com/sg7f248. The dataset has been published by Kvasnička et al. in their manuscript Alterations in lipidome profiles distinguish early-onset hyperuricemia, gout, and the effect of urate-lowering treatment127.

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