Fig. 1
Results of the sparse principal components analysis using clinical data of Hefei cohort. Sparse principal analysis (SPCA) was performed based on the 44 clinical variables of Hefei cohort and the alpha parameter was adjusted from 0.0001 to 0.002 with stepsize 0.0001. For models of each alpha, the cumulative variance of the first 13 principal components (PCs) were summed and the number of variables selected in the first 13 PCs was counted. Variance of different alpha values in SPCA was plotted (a) and the number of selected clinical variables in the 13 PCs of each SPCA were added upon the point. b Distribution of the coronavirus disease 2019 (COVID-19) patients projected to principal components of SPCA with alpha of 0.0015. Depending on each patient's first (X-axis) and 12th (Y-axis) principal components value, COVID-19 patients were projected on the principal components plot of SPCA. c Scatter plot of the clinical markers selected in the first and 12th principal components of SPCA with alpha being 0.0015. Depending on each variable's first (X-axis) and 12th (Y-axis) principal components loadings, 44 clinical variables were projected on the principal components plot of SPCA. The first (X-axis) and 12th (Y-axis) principal components accounted for the 17.8% and 2.9% of the total variance of the 44 clinical markers, respectively