Fig. 2 | Nature Communications

Fig. 2

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

Fig. 2

Effect of EE plateau phase on t-SNE visualization. EE was stopped after varying number of iterations and embedding visualization was examined at several intermediate timepoints and in the end of embedding for flow cytometry (total of 2000 iterations), (a) and mass cytometry (total of 3000 iterations). b Graphs showing KLD change over iteration time are color-labeled to distinguish curves corresponding to experiment perturbations, with black line indicating the run with the shortest EE but uninterrupted plateau. t-SNE maps are annotated with color-coded population overlays based on ground-truth classification of single cell in the datasets. c KLD and KLD relative change plotted against iteration time for the mass41parameter embedding. All embeddings were generated with standard BH-tSNE implementation and representative examples of multiple runs with varying seed values are shown

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