These approaches provided insight into potential immune phenotypes associated with patients with severe disease, but suffered from the use of a small number of manually selected T or B cell subsets or phenotypes. We therefore next employed Uniform Manifold Approximation and Projection (UMAP) to distill the ~200 flow cytometry features (see tables S5 and S6) representing the immune landscape of COVID-19 disease in two dimensional space, creating compact meta features (or Components) that could then be correlated with clinical outcomes. This analysis revealed a clear trajectory from HD to COVID-19 patients (Fig. 6D), which we centered and aligned with the horizontal axis (“Component 1”) to facilitate downstream analysis (Fig. 6E). An orthogonal vertical axis coordinate (“Component 2”) also existed that captured non-overlapping aspects of the immune landscape. We next calculated the mean of Component 1 for each patient group, with COVID-19 patients separated by severity (Fig. 6E). The contribution of Component 1 clearly increased in a stepwise manner with increasing disease severity (Fig. 6F). Interestingly, RD were subtly positioned between HD and COVID-19 patients. Component 1 remained an independent predictor of disease severity (P = 5.5 × 10−5) even after adjusting for the confounding demographic factors of age, sex, and race.