We next took advantage of the FlowSOM clustering in Figs. 2 to 4 that identified individual immune cell types most perturbed in COVID-19 patients and linked these FlowSOM clusters to UMAP Components (Fig. 6H). For non-naïve CD8 T cells, FlowSOM Cluster 11 that contained T-bet+CX3CR1+ but non-proliferating effector-like cells was positively correlated with UMAP Component 2 and negatively correlated with Component 1 (Fig. 6H). In contrast, FlowSOM Cluster 14 contained activated, proliferating PD-1+CD39+ cells that might reflect either recently generated effector or possibly exhausted CD8 T cells (50) and was strongly associated with UMAP Component 1 (Fig. 6H). For CD4 T cells, FlowSOM Cluster 14, containing activated, proliferating CD4 T cells, was captured by both UMAP Components, whereas a second activated CD4 T cell population that also expressed CD95 (FlowSOM Cluster 13) was only captured by UMAP Component 1 (Fig. 6H). In addition, Component 1 was negatively correlated with CD4 T cell FlowSOM Clusters 2 and 3 that contained cTfh (Fig. 6H). Finally, for B cells, the FlowSOM Cluster of T-bet+CD138+ PB (Cluster 5) was positively correlated with Component 1 whereas the Tbet-CD138+ Cluster 3 was negatively correlated with Component 1 (Fig. 6H). Locations in the UMAP immune landscape were dynamic, changing from D0 to D7 for both Component 1 and Component 2 consistent with the data in Fig. 5 and fig. S9, A to F. The most dynamic changes in Component 1 were associated with the largest increases in IgM antibody levels (fig. S9G).