Fig. 5: Autoantibody correlation signatures associate with disease burden.
From: Autoantibodies targeting GPCRs and RAS-related molecules associate with COVID-19 severity
a Correlation matrices of autoantibodies targeting GPCRs and the RAS (denoted by numbers as per legend) for the control (n = 77) and COVID-19 groups (mild [n = 74], moderate [n = 63], and severe [n = 32]). The color scale bar represents the range of Spearman’s rank correlation coefficient. b Box plots ilustrating the correlation distribution of autoantibodies with significant changes (as defined in Supplementary Fig. 4) in pairwise correlations: those belonging to the RAS are placed in the upper row, and autoantibodies targeting other GPCRs are exhibited in the lower row. Antibodies with the highest or lowest correlations and thus contributing more to changes in the correlation pattern of the severe COVID-19 group are indicated. Each box plot shows the median with first and third interquartile range (IQR), whiskers representing minimum and maximum values within IQR, and individual data points. c Canonical-correlation analysis (CCA) of autoantibodies. Correlation between autoantibodies against molecules belonging to or influencing the RAS (dataset X, in green) versus the other autoantibodies (those targeting other GPCRs, NRP1, and STAB1; dataset Y, in blue). Only autoantibodies with Spearman’s rank correlation coefficient ≥ 0.6 are shown while those with a correlation coefficient < 0.6 (gray points) have their names omitted. Autoantibody correlations are plotted based on their relation to the first 2 canonical variates (x-CV1 and x-CV2; y-CV1 and y-CV2: ranging from −1 to 1). Autoantibodies located close in the same CCA quadrant region are those with the highest Spearman’s rank correlation coefficient. Source data are provided as a Source Data file.
