Fig. 5 | Nature Communications

Fig. 5

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

Fig. 5

Evaluation of opt-SNE embeddings. a Endpoint KLD values for standard t-SNE (initial learning rate step = 200, EE stop = 250 iterations) and opt-SNE (initial learning rate = n/α, EE stop at maxKLDRC iteration). N = 5 seeds used for random initialization; error bars denote SEM. b Post-EE graph of KLD minimization over physical time for standard t-SNE, adjusted parameter (as indicated) t-SNE and opt-SNE (representative examples of mass cytometry data embeddings are shown). c 1NN accuracy scores for standard t-SNE and opt-SNE embeddings of of mass cytometry (left) and flow cytometry (right) data per assigned class values (cell subsets, open circles; overal scores, filled circles). Representative examples of multiple runs initiated with varying seed values are shown

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