Fig. 2. Multivariate integration and factor analysis reveal comprehensive immune signatures that explain variance among patients in ACS.
a, Overview of IAI (Factor 2). For each view, the heatmap shows the percentage of the variance that is explained by the factor. The bar plots show the total amount of features (left) and the relative amount of features (right; in respect to the number of view-specific features) among the top 1% of the highest-ranking features that influence the factor. The color coding on the left indicates the data type of each view: green, plasma proteomics; blue, neutrophil prime-seq; orange, cytokine measurements; dark orange, clinical values; purple, scRNA-seq data. The greyscale grading in the heatmap depicts the percentage of variance. b, IAI (Factor 2). Comparison of the factor values for each timepoint for sterile ACS, non-CCS and CCS. Mean ± s.e.m. values are shown. c, Replication of IAI in the Groningen cohort. Comparison of the factor values for each timepoint for ACS with controls. Mean ± s.e.m. values are shown. d, IAI (Factor 2). Normalized expression values of the top 0.5% of features for cluster 0 CD4+ T cell for sterile ACS and CCS. A longitudinal comparison of the normalized expression values (heatmap) and the weight of the features (bar plot) are shown. Plus and minus signs indicate the direction of the feature factor weight. e, Longitudinal comparison of normalized gene expression values of selected features for sterile ACS and CCS. For the following comparisons, only the post hoc test was significant: HINT1 Treg cells (cluster 11). Plus and minus signs indicate the direction of the feature factor weight. f, Phenotyping by flow cytometry of the effects of plasma obtained from ACS and CCS on monocytes isolated from healthy donors. Individual timepoints for sterile ACS are compared with those for CCS. Mean ± s.e.m. values are shown for mean fluorescence intensities (MFIs). No post hoc analysis was performed when the Kruskal–Wallis test was not significant. For b, c, e and f, parametric-distributed data were analyzed using ordinary one-way ANOVA with correction for multiple comparisons by Dunnett’s test; nonparametric-distributed data were analyzed using the Kruskal–Wallis test with correction for multiple comparisons by Dunn’s test. In the case where only the ordinary one-way ANOVA or Kruskal–Wallis test, but not the multiple comparison, was significant, graphs are marked with a vertical bar on top. *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. Exact P and n values are summarized in Supplementary Tables 13 and 14, respectively.