Fig. 6

opt-SNE allows high-quality visualization of large cytometry and transcriptomics datasets. a–d 20 million datapoints from fluorescent cytometry dataset concatenated from 27 subjects vizualized in 2D space. a, c Cell type classes and density overlaid on 2D opt-SNE embedding. b Subject identifier overlaid on 2D opt-SNE embedding. Dashed arrows indicate clusters represented by datapoints from a single subject. d Standard t-SNE visualization (4000 iterations). e, f 10x Genomics mouse brain scRNA-seq dataset (1.3 million datapoints) visualized in 2D space with opt-SNE (e) or standard t-SNE (f). From left to right: density features, single gene classes, and Louvain clusters (0–38) overlays. g 5.22 million datapoints from mass cytometry dataset used in van Unen et al (2017) visualized in 2D space with opt-SNE. From left to right: CD4 expression overlaid on opt-SNE embedding; CCR7 and CD28 expression overlaid on CD4⁺ opt-SNE cluster; CD45RA and CD56 expression intensity overlaid on CD4⁺CD28⁻CCR7⁻ cluster. h CD4⁺CD28⁻CCR7⁻ cells from control, celiac disease (CeD), refractory celiac disease (RCeD) and Crohn’s disease (CrohnD) subjects presented on density plots. Dashed encirclements indicate CD45RA⁺ and CD56⁺ areas of the cluster as defined in g. i Hierarchical t-SNE (HSNE) embedding of the CD4 (left) and CD4⁺CD28⁻ cluster (right) reproduced from van Unen et al.⁷ (licensed under a Creative Commons Attribution 4.0; http://creativecommons.org/licenses/by/4.0/). Color indicates marker expression intensity