Figure 6

The WGCNA algorithm was used to identify key gene modules closely associated with the COVID-19 Severity respectively. (A) The sample dendrogram and feature heat map were drawn based on the Euclidean distance using the average clustering method for hierarchical clustering of samples, with each branch representing a sample, Height in the vertical coordinate being the clustering distance, and the horizontal coordinate being the COVID-19 severity information. (B) Soft threshold (power = 13) and scale-free topology fit index (R² = 0.9). (C,D) Gene hierarchy tree-clustering diagram. The graph indicates different genes horizontally and the uncorrelatedness between genes vertically, the lower the branch, the less uncorrelated the genes within the branch, i.e., the stronger the correlation. (E) Heat map showed the relations between the module and treat features 2. The values in the small cells of the graph represent the two-calculated correlation coefficients between the eigenvalues of each trait and each module as well as the corresponding statistically significant p-values. Color corresponds to the size of the correlation; the darker the red, the more positive the correlation; the darker the green, the more negative the correlation. (F) Scatter plot between GS and MM in brown. (G) Venn diagram demonstrating 47 genes associated with ICU COVID-19.