Fig. 1 | Nature Communications

Fig. 1

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

Fig. 1

Performance of Barnes-Hut t-SNE implementation for cytometry data visualization. Standard (1000 iterations) and extended (3000 iterations) embeddings of mass cytometry (a) or flow cytometry (b) data are presented as heatmap density plots (left) or color-coded population overlays based on ground-truth classification of single cell in the datasets (right). c KLD change over iteration time of gradient descent for standard 1000 iterations (red line) or extended 3000 iterations (black line) embeddings of mass41parmeter dataset. Representative examples of multiple runs with varying seed values are shown

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