Statistics
Due to the heterogeneity of clinical and flow cytometric data, non-parametric tests of association were preferentially used throughout this study unless otherwise specified. Correlation coefficients between ordered features (including discrete ordinal, continuous scale, or a mixture of the two) were quantified by the Spearman rank correlation coefficient and significance was assessed by the corresponding non-parametric methods (null hypothesis: ρ = 0). Tests of association between mixed continuous versus non-ordered categorical variables were performed by unpaired Wilcoxon test (for n = 2 categories) or by Kruskal-Wallis test (for n > 2 categories). Association between categorical variables was assessed by Fisher-exact test. For association testing illustrated in heatmaps, categorical variables with more than 2 categories (e.g., ABO blood type) were transformed into binary dummy variables for each category versus the rest. All tests were performed two-sided, using a nominal significance threshold of P < 0.05 unless otherwise specified. When appropriate to adjust for multiple hypothesis testing, false discovery rate (FDR) correction was performed using the Benjamini-Hochberg procedure at the FDR < 0.05 significance threshold. Joint statistical modeling to adjust for confounding of demographic factors (age, sex, and race) when testing for association of UMAP Components 1 and 2 with the NIH Ordinal Severity Scale was performed using ordinal logistic regression provided by the polr function of the R package MASS. Statistical analysis of flow cytometry data was performed using R package rstatix. Other details, if any, for each experiment are provided within the relevant figure legends.