Fig. 4 | Nature Communications

Fig. 4

From: Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets

Fig. 4

Learning step size optimization for t-SNE visualization of large datasets. a–c KLD change over iterations for embeddings with varying values of initial learning rate step size, color coded as indicated. a EE = 1000 iterations, learning rate step = 25–4000; b EE = 1000 iterations, learning rate step = 8000–2,048,000; c EE = 100–1000 iterations, learning rate step = 2000–512,000. d Representative t-SNE plots of embeddings graphed on a. e t-SNE plot of an optimized embedding. All color overlays on t-SNE plots correspond to cell type classes labeled as in Figs. 1, 2. Representative examples of multiple runs with varying seed values are shown

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